diff --git a/docs/source/api.rst b/docs/source/api.rst index feeb28501..d9d89256e 100644 --- a/docs/source/api.rst +++ b/docs/source/api.rst @@ -46,6 +46,16 @@ Continuous nodes continuous_node_posterior_update continuous_node_posterior_update_ehgf +Exponential family +------------------ + +.. currentmodule:: pyhgf.updates.posterior.exponential + +.. autosummary:: + :toctree: generated/pyhgf.updates.posterior.exponential + + posterior_update_exponential_family_dynamic + Prediction steps ================ @@ -144,7 +154,8 @@ Exponential family .. autosummary:: :toctree: generated/pyhgf.updates.prediction_error.exponential - prediction_error_update_exponential_family + prediction_error_update_exponential_family_fixed + prediction_error_update_exponential_family_dynamic Distribution ************ diff --git a/docs/source/images/multivariate_hgf.gif b/docs/source/images/multivariate_hgf.gif deleted file mode 100644 index 4ddc905e6..000000000 Binary files a/docs/source/images/multivariate_hgf.gif and /dev/null differ diff --git a/docs/source/images/multivariate_normal.gif b/docs/source/images/multivariate_normal.gif new file mode 100644 index 000000000..a2197775c Binary files /dev/null and b/docs/source/images/multivariate_normal.gif differ diff --git a/docs/source/notebooks/0.3-Generalised_filtering.ipynb b/docs/source/notebooks/0.3-Generalised_filtering.ipynb index 5a186d8f2..5d19a1bf0 100644 --- a/docs/source/notebooks/0.3-Generalised_filtering.ipynb +++ b/docs/source/notebooks/0.3-Generalised_filtering.ipynb @@ -12,8 +12,14 @@ }, "source": [ "(generalised_filtering)=\n", - "# From Reinforcement Learning to Generalised Bayesian Filtering\n", - "\n", + "# Generalised Bayesian Filtering of exponential family distributions" + ] + }, + { + "cell_type": "markdown", + "id": "fe6b34cf-2de5-4763-af6a-785b2930e679", + "metadata": {}, + "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ComputationalPsychiatry/pyhgf/blob/master/docs/source/notebooks/0.3-Generalised_filtering.ipynb)" ] }, @@ -23,12 +29,6 @@ "id": "31b80846", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:53:59.914472Z", - "iopub.status.busy": "2025-01-10T13:53:59.913668Z", - "iopub.status.idle": "2025-01-10T13:53:59.921214Z", - "shell.execute_reply": "2025-01-10T13:53:59.919815Z" - }, "slideshow": { "slide_type": "" }, @@ -54,12 +54,6 @@ "id": "6e337fd3-5a3e-4e0f-ab4f-e055cebfb7ff", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:53:59.930368Z", - "iopub.status.busy": "2025-01-10T13:53:59.924990Z", - "iopub.status.idle": "2025-01-10T13:54:02.194691Z", - "shell.execute_reply": "2025-01-10T13:54:02.193859Z" - }, "slideshow": { "slide_type": "" }, @@ -84,7 +78,6 @@ "\n", "from pyhgf.math import MultivariateNormal, Normal, gaussian_predictive_distribution\n", "from pyhgf.model import Network\n", - "from pyhgf.utils import beliefs_propagation\n", "\n", "np.random.seed(123)\n", "plt.rcParams[\"figure.constrained_layout.use\"] = True" @@ -101,14 +94,17 @@ "tags": [] }, "source": [ - "Hierarchical Gaussian filters can receive one-dimensional continuous and binary inputs by default, but they in practice be extended to a much broader class of distributions. Here, we use the approach described in {cite:p}`mathys:2020` to demonstrate that the Hierarchical Gaussian Filter can be generalized to any probability distribution that belongs to the [exponential family](https://en.wikipedia.org/wiki/Exponential_family). This is the sample principle that underpins the {ref}`categorical_hgf`, in which case the implied distribution is a Dirichlet distribution. However, by abstracting the specificity of each distribution away, we can implement probabilistic nodes that can flexibly filter any distribution from the exponential family, as an input node or as a state node, which greatly enlarges the range of possible models.\n", + "In this tutorial, we are interested in online Bayesian filtering when applied to [exponential family distributions](https://en.wikipedia.org/wiki/Exponential_family). Bayesian inference in high-dimensional space can rapidly become intractable, requiring approximation or sampling methods that always remain computationally costly. However exponential family distributions have interesting properties that allow expressing Bayesian updates through a simple closed-form solution over hyperparameters that is common to all family members. This property is well-described for stationary distributions and can extend to non-stationary distributions through the application of a fixed learning rate. But this solution can be further improved by dynamically learning the learning rate itself, which is something that Hierarchical Gaussian Filters are especially good at. \n", "\n", - "Generalised Bayesian filtering in this context requires first expressing the Bayesian update of an exponential family distribution as a simple update over hyperparameters. Exponential families of probability distributions are those which can be written in the form:\n", + "Here, we leverage the approach described in {cite:p}`mathys:2020` to demonstrate that the Hierarchical Gaussian Filter can be generalized to any probability distribution that belongs to the [exponential family](https://en.wikipedia.org/wiki/Exponential_family). In this tutorial we will describe how to filter stationary and non-stationary distributions using a fixed learning rate or by using a predictive coding network on top of sufficient statistics elicited by new observations. This approach has the advantage of being extremely modular and flexible while extending dynamic Bayesian filtering to a whole range of models. This already underpins the {ref}`categorical_hgf`, in which case the implied distribution is a Dirichlet distribution.\n", "\n", + "## Theory\n", + "\n", + ":::{hint} Exponential family distributions\n", + "Exponential family distributions are probability distributions which can be written in the form:\n", "$$\n", "p(x|\\vartheta) = f_x(\\vartheta) := h(x) exp(\\eta(\\vartheta) ยท t(x) โˆ’ b(\\vartheta))\n", "$$\n", - "\n", "where:\n", "- $x$ is a vector-valued observation\n", "- $\\vartheta$ is a parameter vector\n", @@ -116,6 +112,8 @@ "- $\\eta(\\vartheta)$ is the natural parameter vector\n", "- $t(x)$ is the sufficient statistic vector\n", "- $b(\\vartheta)$ is a scalar function\n", + ":::\n", + "\n", "\n", "It has been shown in {cite:p}`mathys:2020` that, when chosing as prior:\n", "\n", @@ -161,12 +159,6 @@ "id": "f0be17ad-5611-4c89-80a2-9e45b1ddffc4", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:02.197152Z", - "iopub.status.busy": "2025-01-10T13:54:02.196883Z", - "iopub.status.idle": "2025-01-10T13:54:02.200982Z", - "shell.execute_reply": "2025-01-10T13:54:02.200133Z" - }, "slideshow": { "slide_type": "" }, @@ -185,16 +177,12 @@ "id": "ba318975-ce19-4b47-9934-bd02c0130e10", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:02.203128Z", - "iopub.status.busy": "2025-01-10T13:54:02.202914Z", - "iopub.status.idle": "2025-01-10T13:54:02.823804Z", - "shell.execute_reply": "2025-01-10T13:54:02.822473Z" - }, "slideshow": { "slide_type": "" }, - "tags": [] + "tags": [ + "hide-input" + ] }, "outputs": [ { @@ -218,7 +206,9 @@ " linestyle=\"--\",\n", ")\n", "for i, x_i in enumerate(xs):\n", - " xi = xi + (1 / (1 + nu)) * (Normal().sufficient_statistics(x=x_i) - xi)\n", + " xi = xi + (1 / (1 + nu)) * (\n", + " Normal().sufficient_statistics_from_observations(x=x_i) - xi\n", + " )\n", " nu += 1\n", "\n", " if i in [2, 4, 8, 16, 32, 64, 128, 256, 512, 999]:\n", @@ -263,12 +253,6 @@ "id": "add927a3-4233-484d-ad01-74ad03b87b5b", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:02.826901Z", - "iopub.status.busy": "2025-01-10T13:54:02.826428Z", - "iopub.status.idle": "2025-01-10T13:54:03.406849Z", - "shell.execute_reply": "2025-01-10T13:54:03.406071Z" - }, "scrolled": true, "slideshow": { "slide_type": "" @@ -319,11 +303,19 @@ }, "source": [ "## Filtering the Sufficient Statistics of a Non-Stationary Distribution\n", - "Real-world applications of Bayesian filtering imply non-stationary distributions, in which cases the agent cannot rely anymore on distant observation and has to weigh down their evidence proportional to their distance from the current time point. In the current framework, this suggests that $\\nu$, the pseudo-count vector, cannot linearly increase with the number of new observations but has to be limited. The most straightforward way is then to fix it to some values.\n", + "Real-world applications of Bayesian filtering imply non-stationary distributions, in which cases the agent can no longer rely on distant observation and has to weigh down their evidence proportional to their distance from the current time point. In the current framework, this suggests that $\\nu$, the pseudo-count vector, cannot linearly increase with the number of new observations but has to be limited. The most straightforward way is then to fix it to some values. Here, we start by applying this naive approach to a set of popular distributions, and then for each of them we illustrate how a collection of Hierarchical Gaussian Filters over sufficient statistics can help dynamic inference over the variable $\\nu$ itself.\n", + "\n", + "### Gaussian distribution\n", "\n", - "### Using a fixed $\\nu$\n", + "#### Generalised Bayesian Filtering: using a fixed $\\nu$\n", "\n", - "This operation can be achieved using a continuous state node that implements the exponential family updates on the values that are passed by the value child nodes. Such nodes are referred to as `exponential-state` nodes, with the type of distribution (here a simple one-dimensional Gaussian distribution). The input node is set to generic, which means that this input simply passes the observed value to the value parents without any additional computation. We can define such a model as follows:" + "This operation can be achieved using an exponential family state node, using the following parameters:\n", + "- Setting `kind=\"ef-state\"`. We also set `learning=\"generalised-filtering\"` to explicitly inform the node it should use the general filtering approach (using a fixed $\\nu$)\n", + "- using a 1-dimensional Gaussian distribution by setting `distribution=\"normal\"`\n", + "- using `nus=3.0`, this parameter will behave as the inverse of a learning rate, informing how much in the past we are looking to update the current sufficient statistics.\n", + "- setting `xis=np.array([0, 1 / 8])`, this is our starting point and first guess for the expected sufficient statistics.\n", + "\n", + "Some of these steps were unnecessary, as this is the toolbox's default behaviour, but we added them here for clarity." ] }, { @@ -331,30 +323,75 @@ "execution_count": 6, "id": "528427f1-5b70-4fa4-8bc1-ef8ebcf62bbc", "metadata": { - "execution": { - "iopub.execute_input": "2025-01-10T13:54:03.410164Z", - "iopub.status.busy": "2025-01-10T13:54:03.409350Z", - "iopub.status.idle": "2025-01-10T13:54:03.414735Z", - "shell.execute_reply": "2025-01-10T13:54:03.413777Z" - } + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] }, "outputs": [], "source": [ - "generalised_filter = Network().add_nodes(kind=\"ef-state\", xis=np.array([0, 1 / 8]))" + "generalised_filter = Network().add_nodes(\n", + " kind=\"ef-state\", distribution=\"normal\", nus=3.0, xis=np.array([0, 1 / 8])\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "87760ecd-821e-458d-903a-594018610d1d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "````{note} From sufficient statistics to distribution parameters and backwards\n", + ":class: dropdown\n", + "\n", + "When using a 1-dimensional Gaussian distribution, Setting $\\xi = [0, \\frac{1}{8}]$ is equivalent to a mean $\\mu = 0.0$ and a variance $\\sigma^2 = \\frac{1}{8}$. You can convert between distribution parameters and expected sufficient statistics using the distribution classes from PyHGF (when implemented):\n", + "\n", + "```{code-cell} python\n", + "from pyhgf.math import Normal\n", + "\n", + "# from an observation to sufficient statistics\n", + "Normal.sufficient_statistics_from_observations(x=1.5)\n", + "```\n", + "> Array([1.5 , 2.25], dtype=float32)\n", + "```{code-cell} python\n", + "# from distribution parameters to sufficient statistics\n", + "Normal.sufficient_statistics_from_parameters(mean=0.0, variance=4.0)\n", + "```\n", + "> Array([0., 4.], dtype=float32)\n", + "```{code-cell} python\n", + "# from sufficient statistics to distribution parameters\n", + "Normal.parameters_from_sufficient_statistics(xis=[0.0, 4.0])\n", + "```\n", + "> (0.0, 4.0)\n", + "\n", + "````" + ] + }, + { + "cell_type": "markdown", + "id": "594c726c-bfee-41df-a72b-7cbbac89aaa6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "The resulting network consists in a single node that encapsulate all computation and do not depends on other nodes. Nodes supporting exponential family distribution can therefore support inputs of various shapes whithout requiring multiple input nodes." ] }, { "cell_type": "code", "execution_count": 7, "id": "1798765e-3d65-4bfd-964b-7f9b6b0902be", - "metadata": { - "execution": { - "iopub.execute_input": "2025-01-10T13:54:03.418432Z", - "iopub.status.busy": "2025-01-10T13:54:03.417963Z", - "iopub.status.idle": "2025-01-10T13:54:03.454413Z", - "shell.execute_reply": "2025-01-10T13:54:03.453224Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -380,7 +417,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -406,12 +443,6 @@ "id": "2d921e51-a940-42b2-88f2-e25bd7ab5a01", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:03.458489Z", - "iopub.status.busy": "2025-01-10T13:54:03.457995Z", - "iopub.status.idle": "2025-01-10T13:54:03.462879Z", - "shell.execute_reply": "2025-01-10T13:54:03.462074Z" - }, "slideshow": { "slide_type": "" }, @@ -422,46 +453,51 @@ "x = np.arange(0, 1000) # time points\n", "\n", "# create noisy input time series with switching means\n", - "xs = np.random.normal(0, 1 / 8, 1000)\n", - "xs[200:400] += 0.5\n", - "xs[600:800] -= 0.5" + "y = np.random.normal(0, 1 / 8, 1000)\n", + "y[200:400] += 0.5\n", + "y[600:800] -= 0.5" ] }, { "cell_type": "code", "execution_count": 9, + "id": "adf8d014-77a2-4789-a200-18533adb0915", + "metadata": {}, + "outputs": [], + "source": [ + "generalised_filter.input_data(input_data=y);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "9176ddfd-df3f-4733-bb97-81deb71aa1b8", - "metadata": { - "execution": { - "iopub.execute_input": "2025-01-10T13:54:03.466181Z", - "iopub.status.busy": "2025-01-10T13:54:03.465620Z", - "iopub.status.idle": "2025-01-10T13:54:03.667947Z", - "shell.execute_reply": "2025-01-10T13:54:03.667203Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "means = []\n", + "means, variances = [], []\n", "nus = [3, 9, 35]\n", "for nu in nus:\n", - " # set the learning rate\n", + "\n", + " # set a new learning rate\n", " generalised_filter.attributes[0][\"nus\"] = nu\n", "\n", - " means.append(generalised_filter.input_data(input_data=xs).to_pandas().x_0_xis_0)" + " # fit to new data and convert the sufficient statistics into distribution parameters\n", + " mean, variance = Normal.parameters_from_sufficient_statistics(\n", + " xis=generalised_filter.input_data(input_data=y).node_trajectories[0][\"xis\"].T\n", + " )\n", + "\n", + " # save distribution parameters \n", + " means.append(mean)\n", + " variances.append(variance)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "0754380d-87d0-430e-a533-540c5f252091", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:03.672185Z", - "iopub.status.busy": "2025-01-10T13:54:03.671465Z", - "iopub.status.idle": "2025-01-10T13:54:03.946934Z", - "shell.execute_reply": "2025-01-10T13:54:03.946361Z" - }, "slideshow": { "slide_type": "" }, @@ -472,9 +508,9 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -482,14 +518,22 @@ } ], "source": [ - "_, ax = plt.subplots(figsize=(12, 3))\n", - "ax.scatter(x, xs, color=\"grey\", alpha=0.6, s=10)\n", + "_, axs = plt.subplots(figsize=(12, 5), nrows=2, sharex=True)\n", + "axs[0].scatter(x, y, color=\"grey\", alpha=0.6, s=10)\n", "for mean, nu in zip(means, nus):\n", - " ax.plot(x, mean, label=rf\"$\\nu = {nu}$\")\n", - "ax.grid(linestyle=\"--\")\n", - "ax.set_title(r\"Filtering sufficient statistics using a fixed $\\nu$\")\n", - "ax.set_xlabel(\"Observations\")\n", - "ax.legend()\n", + " axs[0].plot(x, mean, label=rf\"$\\nu = {nu}$\")\n", + "axs[0].grid(linestyle=\"--\")\n", + "axs[0].set_title(r\"Filtering sufficient statistics using a fixed $\\nu$\")\n", + "axs[0].set_ylabel(\"Mean\")\n", + "axs[0].legend()\n", + "\n", + "for variance, nu in zip(variances, nus):\n", + " axs[1].plot(x, jnp.sqrt(variance), label=rf\"$\\nu = {nu}$\")\n", + "axs[1].grid(linestyle=\"--\")\n", + "axs[1].set_xlabel(\"Observations\")\n", + "axs[1].set_ylabel(\"Standard deviation\")\n", + "axs[1].legend()\n", + "\n", "sns.despine()" ] }, @@ -514,31 +558,23 @@ ] }, "source": [ - "### Using a dynamically adapted $\\nu$ through a collection of Hierarchical Gaussian Filters\n", + "#### Using a dynamically adapted $\\nu$ through a collection of Hierarchical Gaussian Filters\n", "\n", "Limiting the number of past observations weighting in the predictive distribution comes with the difficult question of how to choose the correct value for such a parameter. Here, one solution to handle this is to let this parameter vary across time as a function of the volatility of the observations. Large unexpected variations should increase the learning rate, while limited, expected variations should increase the posterior precision. Interestingly, this is the kind of dynamic adaptation that reinforcement learning models are implementing, including the Hierarchical Gaussian Filter in this category. Here, we can derive the implied $\\nu$ from a ratio of prediction and observation differentials such as:\n", "\n", "$$\n", - "\\nu = \\frac{\\delta}{\\Delta}\n", + "\\nu \\leftarrow \\frac{\\delta}{\\Delta}\n", "$$\n", "\n", - "with $\\delta$ the prediction error at time $k$ and $\\Delta$ the differential of expectations (before and after observing the new value).\n", - "\n", - "#### Univariate normal distribution" + "with $\\delta$ the prediction error at time $k$ and $\\Delta$ the differential of expectations (before and after observing the new value)." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "baf6e2fc-dc8d-46bb-896b-ff5c45a944ee", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:03.949485Z", - "iopub.status.busy": "2025-01-10T13:54:03.948882Z", - "iopub.status.idle": "2025-01-10T13:54:04.636554Z", - "shell.execute_reply": "2025-01-10T13:54:04.635183Z" - }, "slideshow": { "slide_type": "" }, @@ -546,50 +582,132 @@ }, "outputs": [], "source": [ - "univariate_hgf = (\n", - " Network()\n", - " .add_nodes(node_parameters={\"precision\": 100.0})\n", - " .add_nodes(value_children=0, node_parameters={\"tonic_volatility\": -6.0})\n", - " .add_nodes(\n", - " volatility_children=1, node_parameters={\"mean\": 0, \"tonaic_volatility\": -2}\n", - " )\n", - " .input_data(input_data=xs)\n", - ")" + "univariate_hgf = Network().add_nodes(kind=\"ef-state\", learning=\"hgf-1\")\n", + "univariate_hgf.attributes[1][\"precision\"] = 100.0\n", + "univariate_hgf.attributes[4][\"precision\"] = 100.0" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, + "id": "2f56c8e2-125e-4099-b511-ef8160d1aa56", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "hgf-nodes\n", + "\n", + "\n", + "\n", + "x_0\n", + "\n", + "EF-0\n", + "\n", + "\n", + "\n", + "x_1\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "x_1->x_0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x_2\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "x_2->x_1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x_3\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "x_3->x_0\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x_4\n", + "\n", + "4\n", + "\n", + "\n", + "\n", + "x_4->x_3\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univariate_hgf.plot_network()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "id": "2cc62622-0709-41f3-8ccd-d7024099de7a", "metadata": { - "execution": { - "iopub.execute_input": "2025-01-10T13:54:04.639360Z", - "iopub.status.busy": "2025-01-10T13:54:04.639144Z", - "iopub.status.idle": "2025-01-10T13:54:04.674265Z", - "shell.execute_reply": "2025-01-10T13:54:04.673450Z" - }, "scrolled": true }, "outputs": [], "source": [ - "expected_mean = univariate_hgf.node_trajectories[1][\"expected_mean\"]\n", - "mean = univariate_hgf.node_trajectories[1][\"mean\"]\n", - "\n", - "nus = (xs - expected_mean) / (mean - expected_mean)" + "univariate_hgf.input_data(input_data=y);" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, + "id": "4a37b874-2085-4028-9358-6897f33dbda6", + "metadata": {}, + "outputs": [], + "source": [ + "# get the sufficient statistics from the first observation to parametrize the model\n", + "mean, variance = jnp.apply_along_axis(\n", + " Normal().parameters_from_sufficient_statistics, 1, univariate_hgf.node_trajectories[0][\"xis\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "id": "45cdf250-5a7c-4dc3-b5e7-46055cb35adf", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:04.676756Z", - "iopub.status.busy": "2025-01-10T13:54:04.676453Z", - "iopub.status.idle": "2025-01-10T13:54:04.897442Z", - "shell.execute_reply": "2025-01-10T13:54:04.896519Z" - }, "slideshow": { "slide_type": "" }, @@ -598,9 +716,9 @@ "outputs": [ { "data": { - "image/png": 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nbv11Vnq9HlFRUVi4cCG++uorRzeHuIDFixfj7NmzHdYKJIQQQvozqtlFCCGEEOIC1q9fj+rqaixdutTRTSFOSKVSmf2dk5ODTZs2YcaMGY5pECGEENIDNI2REEIIIcSJHTlyBFlZWXj11VcxatQoTJ8+3dFNIk5owIABuOuuuzBgwAAUFhbik08+gUwmw9NPP+3ophFCCCFWo2AXIYQQQogT++STT7B69WqMHDkSq1atcnRziJOaO3cufvjhB1RUVMDDwwPp6el4/fXXkZyc7OimEUIIIVajml2EEEIIIYQQQgghxGVQzS5CCCGEEEIIIYQQ4jIo2EUIIYQQQgghhBBCXIbbBbs4jkNjYyNo9iYhhBBCCCGEEEKI63G7YFdTUxP8/f3R1NTk6KbYBcuyqKurA8uyjm4KIU6Hxg8htqGxQ4jtaPwQYhsaO4TYzh3Hj9sVqG9sbIS/vz8UCgX8/Pwc3RxCCCGEEEIIIYQQYkdul9nlagwGAy5cuACDweDophDidGj8EGIbGjuE2I7GDyG2obFDiO3ccfxQsMvJsSyL7Oxst0pHJMReaPwQYhsaO4TYjsYPIbahsUOI7dxx/FCwixBCCCGEEEIIIYS4DAp2EUIIIYQQQgghhBCXIXF0A0jPiEQixMXFQSSiuCUh1qLxQ4htaOwQYjsaP4TYhsYOsRTHcdDr9W5Vn6o7BoMBsbGx0Gq1/f55EYvFkEgkYBimR8eh1RgJIYQQQgghhBDi9LRaLcrLy9HS0uLoppAe8PLyQmRkJGQymc3HoMwuJ2cwGJCVlYXhw4dDLBY7ujl2p9YZoNYZEOBl+4uckM64+vghpLfQ2CHEdjR+CLENjR3SHZZlkZ+fD7FYjKioKMhksh5nB7kKjuOgVqvh6enZr58TjuOg1WpRXV2N/Px8JCcn25zNScEuJ8eyLIqKijB06FCXe9PX6A244dODyK9WYvsT0xEVIHd0k4iLceXxQ0hvorFDiO1o/BBiGxo7pDtarRYsyyI2NhZeXl6Obk6/wrIsNBoNPDw8+v1UYLlcDqlUisLCQmi1Wnh6etp0nP7dS+LWPtubhzOljVBqDdh6tsLRzSGEEEIIIYQQ0s/192AO6Z49ziG9Cki/dKm6GR/uyhX+3nm+yoGtIYQQQgghhBBCiLNwaLDrk08+wfDhw+Hn5wc/Pz+kp6dj8+bNXd7n559/RmpqKjw9PTFs2DBs2rSpj1rbP4lEIqSkpLhU9JplOTy77jS0BhZDo/lFBI7k16JJrXNwy4irccXxQ0hfoLFDiO1o/BBiGxo7hNiOYRh4eHj063pd9ubQd4qYmBi88cYbOH78OI4dO4ZZs2Zh0aJFOHv2bIf7Hzx4ELfccguWLVuGEydOYPHixVi8eDHOnDnTxy3vP8RiMVJTU11q3vrPx4uRkV8HuVSMT28fg+gAOXQGDufKGh3dNOJiXHH8ENIXaOwQYjsaP4TYhsYOIbZjGAZyudytgl0Mx3GcoxvRVlBQEN566y0sW7as3W033XQTlEolNm7cKGybOHEiRo4ciU8//bTD42k0Gmg0GuHvxsZGxMbGoqamBn5+fNaQSCSCWCyGwWAAy7LCvqbter0ebZ8msVgMkUjU6XadzjwDSSLh1wHQ6/UWbZdKpWBZFgaDQdjGMAwkEkm77QaDAZmZmRgzZozZVQ5n7VNNswZz3vsLjWo9/jU/FXelx+H6Tw8jq7QRX9wxGrPTIp2uT223d9Z26pNj+qTVapGRkYExY8ZAIpG4RJ9c8TxRn/pfnzQaDY4fP44xY8ZAKpW6RJ9c8TxRn/pnn/R6PY4fP46JEydCJBK5RJ9MXOk8UZ/6X5/0ej0yMzMxfvx4XM5Z+9TVduqT9X3S6XTIy8tDQkKCUNScYRgwDAOO48z6ZNre9him7QDM9u1qu0gk6vTYjtreUZ84joNSqYS3t7dT9EmtVqOgoAAJCQnw8vJq99qTSqXoTr9ZjdFgMODnn3+GUqlEenp6h/scOnQITzzxhNm2OXPmYP369Z0ed+XKlVixYkW77du2bRNWaIiLi8OoUaOQlZWFoqIiYZ+UlBSkpqYiIyMD1dXVwvaRI0ciPj4e+/btQ1NTk7A9PT0dYWFh2LZtm9nAnjlzJuRyebspl/Pnz4dKpcLu3buFbRKJBAsWLEBNTQ0OHTokbPf19cWsWbNQXFyMkydPCttDQkJQU1OD3Nxc5Oa21rhy1j59e1GERrUIw6L9MSlEi02bNkHdLAIgwtmLeZidFul0fQKA0NBQTJo0CTk5OcjOznb68+QqfSopKUFtbS22bdvmMn1yxfNEfeq/fdq2bZvL9QlwvfNEfep/fQL4Hxeu1CdXPE/Up/7XJwBoamrC/v37XaZPrnieHNWnhIQEaLVaNDc3Q6vVAuBX9vPw8EBTU5NZEMjb2xtSqRSNjeazh3x9fcEwTLvtfn5+4DjO7HkBgICAAOj1eiiVSmGbSCSCn58ftFotVCqV2XPg4+MDtVptlpAjk8ng5eUFlUoltBsAPDw8IJfLoVQqzZ53W/oEAPPmzcOwYcOwcuVKAMDixYsxYsSIdvESa/t09dVXY+jQoXjttdfs1ifT49TX18PLy6vda2/RokXojsMzu06fPo309HSo1Wr4+Phg7dq1mD9/fof7ymQyfPvtt7jllluEbR9//DFWrFiBysrKDu/j6plder0e27Ztw5w5c8xSep2xT+fLm3DNx4cgYoDfl0/B4AgfsCyLh9aexPbzVXjlmiFYOinRqfp0+XZ3v9rS3/qk0WiwZcsWzJ49G1Kp1CX65IrnifrU//qkVquxfft2zJ49GzKZzCX65IrnifrUP/uk0+mwfft2zJ8/X2iPs/fJxJXOE/Wp//XJNHbmzZvXbiqWs/apq+3UJ/fK7Lr77rvR0NCAdevW9eg4XWV2TZs2DWPGjMG7774LAKivr4dUKoWPj0+P+jRz5kyMGDFCOG5P2u5SmV0pKSk4efIkFAoFfvnlF9x5553Yu3cvhgwZYpfje3h4wMPDo912qVTa7gkSi8VmASMT02C1dHtnT7w120UiEUSi9iXVOtsuFos7PI4z9en7I8UAgHnDIjE02l9ov7+XDACg1LLCNmfp0+U6azv1yXF9Mt2n7f2cvU+ueJ6oT47rk1Kjh4hhIJeJhbab9jdNYTRtd5Y+Xc4VztPlqE/UJ4D61Fkbrd1OfepffWIYxuX61Nl26pN1fdLpdGAYpsNjmYIpHT1uRzrat7PtnR3b2u2dtcfa43R0jLaBJdPtwcHBHbbBlsc0Pe/2OI7pWG2PaUlw63IOX8pCJpMhKSkJY8aMwcqVKzFixAi89957He4bERHRLoOrsrISERERfdHUfkksFmPkyJEdvgE4k4YWLTacLAMA3DM50ew2Hw/+zbZZQ6sxEvtylfFDSG/R6A1Y8P5+XPmfvdDqW68S0tghxHY0fgixDY0dYi2O49Ci1Tvkv55MoJsxYwYeeeQRPPbYYwgMDER4eDi++OILKJVK3H333fD19UVSUhI2b97c7n7Lly/H8uXL4e/vj5CQELzwwgvgOK7DYNSMGTPw2GOPCX+zLIuVK1ciMTERcrkcI0aMwC+//GJ2H6VSiaVLl8LHxweRkZF45513uu3P/Pnzceeddwp/7969GyEhIWaZfL3B4ZldlzMVvu1Ieno6du7caXZCtm/f3mmNL3cgEokQHx/v6Gb02JYzFdAaWKRG+GJMfKDZbb6e/Mu0Sa3v6K6E2MxVxg8hvWVPdjUKalsAAGUNKiSE8EVNaewQYjsaP4TYhsYOsZZKZ8CQF7c65LHPvTIHXjLbwy3ffvstnn76aWRkZODHH3/Egw8+iN9++w3XXnstnnvuObz77ru44447UFRUJNQiN91v2bJlyMjIwLFjx3D//fcjLi4O9913n5At1ZmVK1di9erV+PTTT5GcnIx9+/bh9ttvR2hoKKZPnw4AeOqpp7B3715s2LABYWFheO6555CZmYmRI0d2etzo6Gjk5+cLf0+fPh0qlQqHDx/G5MmTbX6OuuPQzK5nn30W+/btQ0FBAU6fPo1nn30We/bswW233QYAWLp0KZ599llh/0cffRRbtmzBO++8gwsXLuDll1/GsWPHsHz5ckd1weH0ej127drVbj60s/kji8/qWjgiqt1tQmYXBbuInbnK+CGkt/xuzLgFgIpGtfBvGjuE2I7GDyG2obFD3MmIESPw/PPPIzk5Gc8++yw8PT0REhKC++67D8nJyXjxxRdRW1uLrKwss/vFxsbi3XffRUpKCm677TY88sgjePfdd8FxHAwGQ6cZZxqNBq+//jq+/vprzJkzBwMGDMBdd92F22+/HZ999hkAoLm5GV999RXefvttXHHFFRg2bBi+/fbbbsdkdHQ0SkpKhL9FIhHkcjmqqqp6+Cx1zaGZXVVVVVi6dCnKy8vh7++P4cOHY+vWrZg9ezYAoKioyCzVbtKkSVi7di2ef/55PPfcc0hOTsb69esxdOhQR3XB4UwrQjh4nYEeqVCocehSLQBg4fD2wS5fT35+bpOGPtiIfbnC+CGkJ3KrmnDLF0ewZEwMnp6banZbnVKLHedbSwdUtgl20dghxHY0fgixDY0dYi25VIxzr8xx2GP3xPDhw4V/i8ViBAcHY9iwYcK28PBwAGgXMJo4caJZ9lZ6ejreeeeddosOXC43NxctLS1CLMZEq9Vi1KhRAIBLly5Bq9ViwoQJwu1BQUFISUnpsi/R0dEoLS0V/j558iQaGhp6fYaeQ4NdX331VZe379mzp922JUuWYMmSJb3UImIvHMcht6oZA0J9IBZ1nioJAN8eKgDLAeMTghAX7NXudh9hGiPV7CKEEHt67c/zqG7S4OM9l3D/tAEI8JLhYG4Nwvw88cepMmja1OlqG+wihBBCCOnvGIbp0VRCR7q8IPvlCzOYAlqXr7xoq+bmZgDAn3/+iejoaLPbOlrwzxrR0dFobm5GY2MjfHx88Pjjj+O2227r9drrznnmSb/38Z5LeGtrNl5eOAR3XVZwvi2lRo+1R4oAAMumdryfr1CgnjK7CCHEXsoVKvyVWyP8/duJUrAc8OrGc2b7xQTKUVKvQoWi43qahBBCCCGkfzhy5IjZ34cPH0ZycnK3CzsMGTIEHh4eKCoqEupzXW7gwIGQSqU4cuQI4uLiAAD19fW4ePFip/cBIATPSkpK8M0336CiogIbNmywpls2oWCXkxOLxUhPT+9Xq5IYWA5vbc0GAPx7a3anwS6tnsV7O3OgUOmQEOyFKweHd7ifqUA91ewi9tYfxw8hfaFJrcOyVcegM7Sms6/441y7/YZF+2PRyCi89ud5s8wuGjuE2I7GDyG2obFDSPeKiorwxBNP4G9/+xsyMzPxwQcf4J133gHDMF2OHV9fXzz55JN4/PHHwbIspkyZAoVCgb/++gt+fn6488474ePjg2XLluGpp55CcHAwwsLC8K9//avdKo+XMwW7/vGPf+DixYvYt28f/Pz87NrvjlCwy8mJRCKEhYU5uhlmDrTJFIjw9+xwn4IaJa758AAajQGsp+emdjrd0TSNkTK7iL31x/FDSF94ccNZnCtvRIiPDN/dMwGvbjyHQ3m1YBjgzvQElNSrEOglxYsLh2DfRf49vcJsGiOD0NDQLlf0IYR0jD57CLENjR1Curd06VKoVCqMHz8eYrEYjz76KO6//34wDCP815lXX30VoaGhWLlyJfLy8hAQEIDRo0fjueeeE/Z566230NzcjIULF8LX1xf/+Mc/oFAoumxTSEgIPDw8UFhYiL1797abJtlbGM7NKvw1NjbC398fCoWiT6KJvU2n02Hbtm246qqr2s3rdZS7v8nA7uxqAHxhvrMr5kB0WSDr/u+OYds5vvDx9EGhWHX3uE4HXkl9C6a8uRsyiQgXX5vXu40nbqU/jh9CetuuC5W4Z9UxiBjg5wfSMSY+CAaWw5+nyzEgxBtDo/3N9j9eWIfrPzmEmEA5DvxzFgDgqZ9PYtOpUuz8x3REBPo4ohuEOC367CHENjR2SHfUajXy8/ORmJgIT8+Oky5c2YwZMzBy5Ej897//bXcby7JobGyEn59ft5lY/YE9ziVldrmA/rT87plShRDoAgCVzoDKJjUi/eXCttMlCmw7VwkRA2x4eAqGRvt1GWE2rcao1bPQ6A3wkFDqMrGf/jR+COkL3x0qBADcPTkRY+KDAABiEYNrRrRfDRcAwv34LxhVjRpwHIcyhRo/H+dX1DmSX49FFOwixGr02UOIbWjsEEIs1f9DesSpfLQ7FwCwaGQUEowrK+bXKM32+e0E/yNpwfAoDIvx73YajI9Ha0xWqTHYs7mEEOJWyhUq7LvIX5C4Y2K8RfcJ8+WDXVoDi8pGDdYdL+m19hFCCCGEEGIPFOwidpNT2YTNZyoAAA/NSEJCiDcA82AXy3L483QZAHSaRXA5sYiBl4zP5qIi9YQQYrvVhwvBcsD4xCDhPbo7MokIQ6P5af9/ni7HDxlFwm1US5EQQgghpH/Ys2dPh1MY3RUFu5ycRCLBzJkzIZE4dkaq3sDi5T/OAgDmpIUjJcIXicYfUjmVzcJ+h/NrUdmoga+nBNMGhVh8fNOKjI1qnR1bTdxdfxk/hPSF6iYNvvmrAABwz+QEq+579XD+4sSrG8+hTNFaqF6ld6uyn4TYBX32EGIbGjuE2I5hGPj6+rrV4kIU7HIBcrm8+5162fu7cvFXbi3kUjGevCoFADA+ga8Fs+VMBQws/4Po6wMFAICFI6Ksqr1lmspoS7CrWaPH8rWZ+OavfKvvS1xffxg/hPSFLw/koUVrwIgYf8xJi7DqvguGRZr9HeQtAwAoKbOLEJvQZw8htqGxQ4jt3CnQBVCwy+np9Xps2rTJocUaVVoDVhkDSSuvG4bkcF8AwKzBYQjwkqKiUY3rPzmIn44WY8d5fgXGZVMSrXqM2CC+/lduVXM3e7b39YF8bMwqx4o/zuHnY8VW35+4rv4wfgjpCy1aPX44wk8/XD4r2eovO7FBXrhnciJGxgbgzeuH4dqRfPCrUaW1e1sJcXX02UOIbWjsEGI7juPQ2NgIjnOfrHwKdpEe23CyFI1qPeKCvMzqcHlIxFg8MhoAcLK4AU//mgUAmJsWgYGh1q3eNTI2gD9OUYNV92tS6/DVgdaMrtc3nReyzAghxF2sy+TfpxOCvXBFaphNx3hx4RCsf3gybhoXJ2TbNtOiIYQQQgjpZ9wpoOOq7HEOKdhFeqSyUY13tl8EANw+MQ4ikXm2wCOzknBnejxGGINVw6L98eYNw61+HNP9TxY3WHwfjuPwr9/OQKHSIT7YC74eEtS36HCyuB56A2t1GwghxFn97yif1XVHekK792lbeAvBLrq6TgghhJD+QSqVAgBaWloc3BLSU6ZzaDqntqDqfsRmpQ0q3PPNUVQ3aZAS7ovbO1jGPtjHAysWDQXHcThf3oSBYd5W1eoyGRkTAADIq1FC0aKDv1f3L/qtZyvx+6kyiEUM3l4yAt8fKsTvp8pw/SeHAAAzUkKx6u7xVreFEEKcydkyBc6UNkImFuG6UdF2OaaPB/8+TjW7CCGEENJfiMViBAQEoKqqCgDg5eXldnWqOsOyLLRaLdRqNUSi/pvzxHEcWlpaUFVVhYCAAIjF1scOTCjY5eQkEgnmz5/f56uScByHh9ZkIruyCWG+HvjsjjHwknXeBoZhMCTKz+bHC/SWIT7YC4W1LXjp9zN4e8kISMSdD1IDy+E/27MBAA9MH4BxCUEoa1Dh91Nlwj57squh0hogl9k+gIhzc9T4IaQvfbmfn8o9e0g4Ao2F5XvKz8sDANCioyxZQqxFnz2E2IbGDrFERAS/CI8p4EVacRyH2tpaRzfDIgEBAcK5tBW9U7gAlUoFX1/fPn3MjPw6nCpugIdEhHUPTUJMoFevP+bymUn4569ZWH+yDDNTw7BoZOcZCjvPV+JiZTP8PCW4f9pAAMCs1DCkRvgiKkCOXRf4N7/qJg3ignu/7aT/csT4IaQv6Aws/ne0GL+dKAXDAPdOtW5hkK74GC9uNKsps4sQW9BnDyG2obFDusMwDCIjIxEWFgadTufo5vQbHMdBqVTC29u732e7SaXSHmV0mVCwy8np9Xrs3r0b8+fP79F8Vmvszq7C8jWZAIDrRsf0SaALAJaMjcX58iZ8/Vc+jhXUdxns2nqWX/XxhjGx8Jfzz4uvpxRbHpsGAJj2790oqmtBdbOagl1uzBHjhxBraPQGvLD+DIZF++OO9ASL79es0ePeb4/icF4dAOD2CfEYFRdot3Z5SkyPQ18iCbEWffYQYhsaO8QaYrHYLgETV6HT6fDXX3+51fjpv5M1Sb/UpNbh4TWZUGoN8PWU4P5pA/r08cfE8z/WuipUb2A57M7mM7euHNLxqmOhvvwUnKpGjX0bSAghdvTdwUL8dKwEL2w4a/F9tHoWD3x/HIfz6uDjIcFTc1LwwtVD7Nou02qMSlqNkRBCCCGE9EOU2UUsotYZoGc5/HaiFC1aA4K9Zdj2+DQE+3j0aTtGxgUAAM6XN0KtM8BT2j5af7K4AXVKLXw9JRiXENThccKMwa7qZgp2EUL6r01nyoV/6wwspF3UKgSA1zaew5cH+BpdXjIx1tw7QVjN1p5oNUZCCCGEENKfUbDLBfR2kUa9gcX89/cjr1opbPv7Fcl9HugCgCh/T4T6eqC6SYMzpQqM7SCYtfM8P4VxRkpYpz8MKbOLmFCRU9Jf1Sm1yCpRCH/XNmsR4e/Z6f71Si2+O1Qo/P3WDSN6JdAFtGZ2afSsRUE4Qog5+uwhxDY0dgixnbuNH/p26uSkUikWLFjQq/Nu9+fUmAW6ogPkuG60fZavtxbDMBgREwAAOFOq6HAfU/H5K1I7nsIIAKHGQF1Vk9q+DSROpS/GDyG2+t/RIhhYTvi7pptM1N9OlEJrYBEbJMex56/EguGRvda2AJ/WoJuSsrsIsQp99hBiGxo7hNjOHccPBbucHMuyqKqqAsv23vLvv2SWCP8eERuA/90/Eb6ejhskyeE+AIDc6uZ2t5XUt+BCRRPEIgYzUkI7PUaYn3EaY1PnPx7/l1GEJZ8eRL1S28MWk/6qL8YPIbbQGVh8d7DQbFtX0645jsOPR4sBAPdPG4iQXs68FTOATMyv5ENTGQmxDn32EGIbGjuE2M4dxw8Fu5ycwWDAoUOHYDDYViRYodLhoTXHseVMRce3t+iw/Rw/LXDjI1Ow4eHJiA1y7OqFSaHGYFdV+2CXaRXGMfGBCPCSdXoMYRpjJ8EuluXwzLrTOFpQj9WHCzvchzi/no4fQnrLptPlqGhUI8THAxMH8NO1a7oIzp8sbkB2ZRM8JCJcMyKq19tnMBggY/gvS1SknhDr0GcPIbahsUOI7dxx/FCwy82t3HQem05X4IHVxzu8fePpMmj1LFLCfZEW5dfHretYUpgp2KU0285nNhQBABZ2M30nzJefgtNZZtfpNlMkRSLG5rYSQoi1OI7DV8Yi83dMjEdUgBwAUNPceZapKatrwbBI+Mv7JvPWw7g+SLNG1yePRwghhBBCiKUo2OXmjuTXdXn7r8f5KYzXjY4Gw/SPoM9AY7CrplkDRUvrj6zMogZcrGyGp1SERaO6rilmyuyqadagRdt+Cs5OY90vAGhooWmMhJC+c6ywHlklCsgkItw+MU6oMdhZza5mjR6/nyoDANw0LrbP2ukpBLvc5wohIYQQQghxDhTscnIMw8DX19emQBTLciitVwl/tw0cAUC5QoXMogYwDLC4m+BRX/LxkCDSuCJZbnWTsP33k6UAgPnDIuHXTU2xMF8PxAd7geWA/2UUo6pJDYWqtf+mqZtA13W9iHPryfghpLd8tZ/P6rpuVDSCfTyE+ludBbv+zCpDi9aAxBBvjE9sv0Jtb2AYBnIZH+1SdXDBgBDSOfrsIcQ2NHYIsZ07jh8Kdjk5iUSCWbNm2bSM6PmKRmgNrQXqiutbzG7fn1MDABgeE4Bwv86Xu3eE1qmMfN0ujuOEANX8od2vQMYwDO6fNgAA8MrGcxj/fzsx7d+7oWjRIb9GifPljcK+XU0dIs6tJ+OHkN5QXNeCbef4Gor3TEkEAIT48vUHOwp2cRyH1Yf56ds3jYvtsy8wEokEESF8YE2tc59Cp4TYA332EGIbGjuE2M4dxw8Fu5wcy7IoLCy0aVWFA8ZglklxXUuHt09LDrG9gb1k4GVF6s+WNaJMoYZcKsYUC9t7/egYDAjxFv5WqHQ4WlCHTafLzfajzC7X1ZPxQ0hv+HhPLlgOmJocgkHhvgDQmtnVZB54z61qwsNrM3G6VAG5VIwbxsT0WTtZlgWr498bVTqaxkiINeizhxDb0NghxHbuOH4o2OXkDAYDTp48adOqCnsvVpv93Tazi2U5/JXLB7umJPW/YNflmV3bjFld0waFwFMqtugYnlIxtj4+DYefvQJXGwvanyiux28n+OmQd01KAND51CHi/Hoyfgixt+K6Fvx8jK+T+PcrkoXtpmBXVZNa2KbWGbDk00PYdJrPAntwxkBhv75gMBigalYIbSGEWI4+ewixDY0dQmznjuOHgl1uqlmjx9ECvjj9gmF8oKeoTWbX2bJG1Cq18JaJMSou0CFt7IoQ7Ko2BrvO8j/4Zg+JsOo4UrEIEf6eSB8YDAD4aPcl5FY1w9dDgqXp8QCAuhYt9Ab3iYATQvqegeXw1C+noGc5TEkKwbiE1tpbcUFeAID6Fh3qlHx217Zzlag31lmckxaO+6YO6PM2S43fICizixBCCCGE9DcU7HJTB3JqoDNwiAvywrRBfOZWcV1rsfpdxtUIpySHQCbpfy8T0zTGknoVciqbcKGiCSIGuCI1zKbjjYwNMPv71olxiA/2hogBOA7CD0yTgholHl6TibNlCpsejxBCTHQGFk//koXDeXXwkonx2uKhZrd7e0gQGyQHAGRX8ItymFbK/fusJHx2x1ihWHxfkhk/GqhmFyGEEEII6W/6XxSDWIVhGISGhnZblLhZo8fPx4qh0vJX4H88yhc0vmpIOGID+ayBtjW7dmXzwa5ZNgaPeluIjwz+cik4DvhkzyUAwLiEIAR6y2w6Xkq4L7yMPxZ9PSRYNiURYhGDIG9+WlD1ZVMZX9l4Dn+eLsfHuy/1oBfE0SwdP4T0pm/+ysevmSUQixisvG4YEtrUEjRJCfcDAFysbEJxXQv25/DT0K8b3Xd1utpiGAb+PnwAjqYxEmId+uwhxDY0dgixnTuOH/cpxe+iJBIJJk2a1O1+7+24iC/252P7uUo8v2AI9hjrdd02MR4SEf+CL6lXgWU51LVokVXSAACYkdI/g10MwyApzAfHC+uxzlhjy1R3yxYSsQgvX5OG0yUKPHplslD7JsRHhppmjVmR+uyKJiHz7WRxg+2dIA5n6fghpLfoDSxW/VUAAHj5mjQsGhnd4X4pET7Ycb4SFyqaUNagEorYdxQY6wsSiQQD42OBS7kU7CLESvTZQ4htaOwQYjt3HD+U2eXkDAYDLly40G2huS/25wPg67z8a/1pcBwwbVAoEkO8EenvCbGIgdbAoqpJgz3Z1eA4IC3KD+F+nn3RDZu0nXro6yHB9T1ciezGsbF4dfFQsyLPEf58/wtrW7PePt+XJ/y7tEFlVjSaOBdLxw8hvWXdiVKUKdQI9pZhSRfvYSkRfGbXDxlFWHOEz8w1LaLhCAaDAY0NfN1HU8YwIcQy9NlDiG1o7BBiO3ccPxTscnIsyyI7O7vLJURVWoOQvQUA+3NqwDDAP2YPAsBnNUUH8NNRiupasPtC/57CaPLolckYE88Xz79/2gB4yeyfqGgqEr0/h1+Zslyhwu+n+EwyP0/+8U4WNdj9cUnfsGT8ENJbTpco8ML6MwCAuycndLmSbEq4r/DvZo0e4xOCHJp5y7IsFHV8hrBaT+OHEGvQZw8htqGxQ4jt3HH8ULDLBRXUKPFDRpGwgmBWSQP0LAcACPCSAgBunxCPEW0yo0zFjwtqlNhnrAUzs58Hu/w8pVhz7wT8cN9EPDQzqVceY/qgUADAoUs1yCyqx02fHYbOwGF8YhDmG1exzKRgFyHEBu/vyoFGz2JWahgenNH1e9igcB/cOiEOQyL9cNuEOHx7z3iIRY6tuWAqUE+ZXYQQQgghpL+hml0uaO57+6DWsRAzDG4cF4vjRfUAgPnDIvDfm0Yhr6YZyWG+ZveJC/LCX6jFt4cK0KTWI9hbhhExAQ5ovXU8pWKkDwzuteMPifRDsLcMtUotrvv4IAC+jtcLC4Ygt7oJ/ztajC1nyvHPuSluVeyPENIzVU1qofbfs/NSuw1cMQyD168d1hdNs5hUWI2Rgl3ujOM4vLv9IsAweMKYMU4IIYQQ4miU2eXkRCIR4uLiIBLxp1JnYIVl4DMK+HoqmYV8sGt0XCBkEhFSI/za/bCKMa7IeLasEQBw47hYh2cN9AciEYOr0sKFv71lYmx/fDqGxfhjTloEvGViFNS24GhBvQNbSWx1+fghpK+sP1EKA8thVFwAksN9u79DPyMSiRAVFgKAgl3u7lSJAu/vysX7O3OohqWF6LOHENvQ2CHEdu44ftynpy5KLBZj1KhREIv5Wi8XypuE2zwkInAch+PGYJepvlVH4oK8Wo8pYrA0Pb6XWux8nps/GMNj/AEAj1yRjEBvGQDASybBAuMKkBtOljqsfcR2l48fQvoCx3H48WgxAH5hDGckFosxOIWfeqmiYJdVnvvtNO5ZdbTLIKFaZ8DBSzVgjSUI+jPTaxkAiutautiTmNBnDyG2obFDiO3ccfw4NNi1cuVKjBs3Dr6+vggLC8PixYuRnZ3d5X1WrVoFhmHM/vP07L8rBvY2g8GAEydOCKsqZBa1ZhiVNaiQX6NEfYsOMokIaVH+nR5nTHwgPKUiyKViPDsvFZH+8l5vu7Pw9ZTif/dPxJp7J+D+qQPMbhufyE+hLKlXOaJppIcuHz+E9IXMogZcqlZCLhXjamPA3NkYDAaUFPAr0/ZGZtfB3Br8mVVu9+M62qniBqw9UoRdF6qw5UxFp/u9uvEcbv3iCFYfKezD1nWPZTl8tvcS9l7ka3uWNajwe5uLPUUU7LIIffYQYhsaO4TYzh3Hj0ODXXv37sXDDz+Mw4cPY/v27dDpdLjqqqugVCq7vJ+fnx/Ky8uF/woL+9eXwb7EsiyKioqEVRVOtAl2lTaohKyuETH+kEk6P91RAXIcf342Ml+YjXsvC+gQPotrclIIRJdN7QyQ8wX/G1Q6RzSL9NDl44eQvvBDRhEAYP6wSPh6Sh3cGtuwLIv66koAEKbO20tBjRK3fnkED6/NRGmDa11I+O5Q6/cV0+vgco1qHX7NLAHAT3ftT3acr8TKzRdw59cZyK5owvK1mVC2WaCgqNa1zldvoc8eQmxDY4cQ27nj+HFogfotW7aY/b1q1SqEhYXh+PHjmDZtWqf3YxgGERERvd08p3TGWHMLAErrVTicx9ftGh3X+RRGE28PWq/AWv7G1S0VLVq7HZNlOVQ3axDu574Zi4S4qqomNX4/WQYAuG1inINb0zNSYxa8vTO73th8Qfj3xcomRAe4RqZxk1qHP7LKhL+P5NehqLYFccFeZvttOFkmBBAzixpQ1ahGWD/5PNhwqrX9c/67DwDg6ynB/KGR+PFYMWV2EUIIIaTf6FfRDYVCAQAICgrqcr/m5mbEx8eDZVmMHj0ar7/+OtLS0jrcV6PRQKPRCH83NvLBIJ1OB52Oz8YRiUQQi8UwGAxmkU7Tdr1eD45rrZshFoshEok63W46rolEwj/Ner3eou1SqRQsy5qlGDIMA4lE0m676b4GgwFaPYuCmtasOKXWIFwdnpoUBJ1O5xR9Mm3v7Hz0p/Pk58H/2mto4V9P9ujT/226gFWHirDm3gmYkBBA56mX+wRAeAxX6ZMrnidX6dN3BwugNbAYFRuAYZE+Tvva0+l0kBmThU01u+xxns5XNGPL2dbpfbmVjZieHOISr72tp8ug1bMYEOKFUF9PHMmvw87zFbh9QqzZ/j8c4TO+RAzAcsDPx4rw0Mxkh/dJqdFj5/lKXO65eSnwlIjw47FiFNe1OP156ovx1PZ+rtInE1c6T9Sn/tcn0z4cx7Xb31n71NV26hP1yZ59Mu3X9jZn7pNU2v3siH4T7GJZFo899hgmT56MoUOHdrpfSkoKvv76awwfPhwKhQJvv/02Jk2ahLNnzyImJqbd/itXrsSKFSvabd+2bRu8vPirqXFxcRg1ahSysrJQVNQ6rSAlJQWpqanIyMhAdXW1sH3kyJGIj4/Hvn370NTUWhA+PT0dYWFh2LZtm9kLZubMmZDL5di0aZNZG+bPnw+VSoXdu3cL2yQSCRYsWICamhocOnRI2O7r64tZs2ahuLgYJ0+eFLaHhoYiJSUFeXl52HsqB3pWAk8xB5lEjEYN/6IK9uBQfe4wNp13nj5NmjQJOTk5ZjXc+uN5MqibAQAKlRYb/9wEf7+e9UmtB1Yd5YflDxlFYKou0nnqxT6VlfFZCtu3b3eZPrnieXKVPmkNwKoT/Pi+JtXX7DjO2CepKdilNYDjOLucpw+Pmpcx2Hv8PK6Kl7rEa++b/bUAREj2bEZ8mC+O5APrD2cjqPa0sL9/4nCcK2+EhAHmxhqwsUiMd7dfxIQ4X4yID3Fonw5XMVDrxIjyleJ/D0zBQ9/sh79YC3n5KRQpAUCCoroWi8/TkLGT8L/MSsSpLiHAo/+cp74aTyKRCCKRyKX65IrnifrU//qUlJSElpYW7N2712X65IrnifrU//okk/GLrJl+9zh7nxYtWoTuMFzbsJkDPfjgg9i8eTMOHDjQYdCqMzqdDoMHD8Ytt9yCV199td3tHWV2xcbGoqamBn5+fgBcJzr8x6kyPPZTFkbG+qNFa8DFSj4Q8/dZA/HIzIFO2af+HsVXaXUY/OI2AMCx52YiwEvWoz7972gJXvj9HADglvGxePWaIXSeqE/UJxfp00/HSvCvDecQHSDH7n9MA4PWx3TGPjWqdBjzOv/lKPu1uZCKmB6dp8omLaa+tQccB9w7JQFfHijApAFBWH3vBKd/7VUpWjD533ugM3DYtHwSOIbBgg/+glwqxuFnpsNLxrft2fVn8cvxUiwcHoF3bhiG+1afwN6LNbhuVDTeuXGEQ/t0/aeHkVXaiKfnDMJDM5PNzkd9ixbjV+4BAGS9eAXk0taVnjo7T1e+ewCFdS2YlxaO928e0S/Ok6PfI6hP1CfqE/WJ+kR9oj65WGbX8uXLsXHjRuzbt8+qQBfAd3LUqFHIzc3t8HYPDw94eHi02y6VSts9QWKxGGJx+6U4TS8CS7d39sRbs910xa+77Xq9HgcPHsT48eORV8PXykgJ94NMIsLFymbMTYvAgzOSIZWa96s/98mks/PRn86TXCaFl0yMFq0BSh2HEONj2dKnRrUBH+3JE7ZVNmroPHXSRmu3d9Z2lmVx9OhRjB8/3uzxnblPrnieXKFPHMfh28P8Va27JiVAJu34MZ2lTyzL4mzWCWGbWsfCQy7t0XnacrYYHAeMTwjCvGGR+PJAAQpqW4RjOvNrb8OpCugMHIZG+2FITCA4jkOUvyfKFGpMe3s/piSF4KZxsfg1ky9If9fkAZDJZHhwRhL2XqzB7uwqGFjOYX06U9aErNJGyMQi3DSOrzXX9nyE+kkQHSBHaYMKX/1VhPSBwZg4gF+tuFGtQ1FtE1IjfJFVqoCYYeAnl6LQWN9rz8WaDtvvyu8Rer0eGRkZwmePK/SpLVc5T21Rn/pHn/R6PQ4dOoTx48e7TJ+62059oj4B9umTXq/v8HdPZ/s7Q5+649BgF8dxeOSRR/Dbb79hz549SExMtPoYBoMBp0+fxvz583uhhf0fx3Gorq4Gx3FCJldyuA/umpSA+6YOaFf4lthfgFyKFq0Bih6uyPjan+dR0agW/q5s82/SO9qOH0J60/6cGlysbIa3TIybxsd2f4d+juM41NXUQMRIwHJ8kXp/ec9WlvzDWPz86hGRSAzxAQCUKdRQaQ2Qy8RYuek8fs0swTd3jcewGP8e96GvcBwnrLx46/h4APxV0PdvGYW/fX8ctUot/jxdjj9PlwMAFgyPxJh4flGZsfGBCPCSor5Fh+OF9RifGIT9OTUYEOqNmMC++3z/bN8lAMDVwyMR7NP+AiLDMLh+TAze35mD93bm4INdOdj5jxlIDPHG8rUnsO9idbv7mOgMLNQ6Azyl7b8Iuyr67CHENjR2CLGdO46f9iG5PvTwww9j9erVWLt2LXx9fVFRUYGKigqoVK1LVy9duhTPPvus8Pcrr7yCbdu2IS8vD5mZmbj99ttRWFiIe++91xFd6FdMV0kHhvpAIhZRoKuP+Hvx858bWqwPdv1yvATPrz+NCoVa+KH32mK+Zh0FuwhxHasOFgAAloyNhZ9nz4JC/QXDQJiuZsuKjLuzqzD3v/vw1M+nkFXSgFMlCogYYN7QSAR6SRFoXO32fEUjqps0+GxfHmqatbh7VQZUWvuuANmbThQ3IK9GCS+ZGNeMjBK2j00Iwq5/zMDr1w6Dl4x/HuODvfD8gsHCPhKxCLNSwgAAf2SVYdXBAiz9OgNT/70bn+y51Kvt1upZ/HGqDH/l1mDzGX7RgAdmDOx0/yVjWjPzWQ44eKkGxXUtnQa6/DyNUydYDmfbrCRNCCGEEGIPDs3s+uSTTwAAM2bMMNv+zTff4K677gIAFBUVmaXJ1dfX47777kNFRQUCAwMxZswYHDx4EEOGDOmrZvdbTWo+2OLv5Ro/pJxFgDGbocHKzK6skgY8+fMpAMChS7XQGlikRflh3tAIPL/+DGqatdDqWcgkDo1JE0J6qLJRjT3ZVQCAO9LjHdwa+/KQiqDUGoQVGS2h1hmwfO0J7DCu7Hehogk/Hy8BACweGY1QXz5zaHxiELaercShS7XYo28NmNQ0a7H1bAUWj4oWtiladFh1sAC3jI9FmJ+nPbpmN7+f5C9kzEmLgI+H+dcufy8pbp0Qh+Ex/vgrtwa3ToiD72XB0BvGxmDdiVKsPtxa3JXjgLe3ZWNmaihSI/x6pd3/+u20cF749odjULhvp/vHBnnh6bkp+PcWvjjt0fw61DZrhduvGx2NR69IRk2zBhtOluHOSQlYuek8dpyvwpJPD2LLY9O6PD4hhBBCiDUcPo2xO3v27DH7+91338W7777bSy1yPmKxGCNHjoRYzNeNAtDuyzTpXaapO4oWbTd7mntxw1nh35eq+RXIbhoXiyBvGaRiBjoDh+pmDaID5PZrLDHTdvwQ0lt+O1EKlgPGxAdiYKiPo5tjF6axIz9/CYDOqkyrP06VYcf5SkhEDPRsmwKkIgZ/vyJZ+HtKUgi2nq3EW1tbV/bx9ZSgSa3HhYoms2P+4+dT2HG+EscK6/D9sgkWteObv/IhEYtwx8TeC0DqDSw2ZvHBrrZZXZcbGu2PodEdT81MHxCMcQmBOFpQDwBICPZCUpgPdpyvwhubL2DV3ePt3u4tZyrMAl0BXlK8sqjzlbJNHpqRhOHRAbj9qyM4kFuDI/l1AIC3l4zADcbMr/hgb4yJDwIApA8MwY7zVWA54JM9l/DuTSPt3pf+iD57CLENjR1CbOeO44eiIk5OJBIhPp7/ot6s4VdRME2HIH0jwJhJZ800xpL6FpwsbjDblhTmgyVjYsEwDMJ8PVHaoEKFQo3oADk4jgPDMPZsNoH5+CGkN7Ro9Vj1VwEA82lezs40djyl+QD4AvWWMk2Je2RWMh6cMRAv/X4W9Uotbhofi4QQb2G/KcmhZve7Z3Ii4oLkePmPc8ipbA12KVQ6IUtsf04NWrR6YXXDzmSVNGDFH/zKtzMGhSI2yD7T/uuVWjz58ykkhfngiasGYfu5StQ0axHiI8OUpBCbjskwDF64egie/iULIT4eeGZeKrw9JNhxvgr7c2pQ26zpsI5WT3xrnHZ7w5gYhPp6YG5aBMItzJgbHR8AiYhBjTGrK9LfE1cPj+xw36Xp8ciuaMRPx0qQV91sl7bbysBy+GRPLoZE+WFWanivPhZ99hBiGxo7hNjOHccPzY9ycnq9Hrt27YJKw095Ayizq6+Zpo1aM41x1wV+StP4hCC8ef0wzBsagbX3TYDcGKiM8Od/VFQ2qvHzsWKMenU7tpwpt3PLiWn8XL7cLiH2YGA5/HtLNioa1YgJlJtNu3N2prHjaWXNrka1DgdyagAA84dFQCYRYeV1w/DpHWMw01ibyiQh2AsDQvng10sLh+DFhUOQYpyyl1PVGhhZfbjQ7H6m99eufH+o9T7bzlVa1HZLfLr3EnZeqMJn+/Kw8IMDQgbvbRPiIRXb/pVreEwAtjw2DavvnYCh0f5IDPHGsGh/GFhOCB7aS0GNEofyasEwwBOzB+Gfc1MxIjbA4vt7ySS4bnTra/3RK5I7LT4vFYtw79QBAIC8GqVDi+ZuOl2Ot7ddxD2rjvV64I0+ewixDY0dQmznjuOHoiJOjuM4NDU1QalpfdF2d0Wb2FeA3PoC9TvO8z/GZg0Ow03j4oSl3E3C/fir9M/9dlo47voTZZg7tOOr48Q2pvHjTquSkN7324kSbDtbiQsVTciv4acoPzd/sEutNmcaO3Ipn63U0sE0RrXOgN9PlmHO0AhhuvemrHJoDSwGhnojuZv6TAzD4Lt7xqNCocbYBH7aW3I4Pw20uL4FKq0Bl6qb8d8dFwEAYb4eqGrSYOOpclw9vPMpgw0tWvxuXBAEALaercCyKearQesMLJrUegR5y7p7KgR1Si2+bxN4M62QLBOLcHsvTJW8engkTpcqsOl0uV2P/+OxYgDA9EGhiLJxGv0b1w3HrNRwVDdrsGRs16uPxgV5gWGAJrUetUotQuycpWaptUdaa6Kt+OMcvr3H/tNDTeizhxDb0NghxHbuOH4os8tFmIJdMrGICpr3sSBv/kfcmVIFdIbup/I0qXU4fKkWAHDl4LAO91kyJhZiEWMWQKPzSkj/V1zXgqd/ycLmMxXIr1HCx0OCN68fhvnDXDNQHWhcjbZWqWl32zvbsvH0r1l4+hd+IQ6O44RVKW++LMDfmZhALyHQBQDB3jIEeknBccCHu3Pw0u9noTNwuGpIOL6+axwAfpXHZk3nVy1/PlYCjZ5FTCAfyDlWUAfFZZm5D63JxLj/24HcKsszfH47UYoWrQFDIv2w+dGpSAzxxuBIP3x46yih6L49XTGYn2p3vLAeGr19VqfUGVj8YqzVdfO4roNUXRGJGMwdGoE7JsZDLOp6Cr6nVIwof/5cFBiDw73t8tdHRn4dDuXVCn8fL6zvk3YQQgghpPfQr2cXoTReVff2cJ3MAWcxMzUMfp4SZFc24fN9ed3uvye7GloDiwGh3kgK6zizYWZqGL5fNh4LR0RhdFwAgI4zJwgh/cuHu3KhM3CQS8V47+aROPTsrHaZm67ElIVaoVCbbec4Dl/s5+t5bT1bCaVGj4z8OlyoaIJcKsaN3WT7dIZhGCEj7KPdl3C8sB4MA7x0TRrSovwwIMQbGj2Laz44gC/352HLmQphpWKAn1q6+gifffXwzCREB8jBckB2m4L3RwvqsP1cJQwsh30Xq2Gp30+WAuAXGhkc6YfdT87A5ken4qq0CJv62p2Bod4I8pZBo2dxprTRLsfceb4K1U0ahPjIer1uVVuJxlpteX0Q7Np5vhJDX9qKu77JwE/HivHEjydx59cZAIAZKXyduGaN3uKpuYQQQgjpnyjY5eTEYjHS09Oh1vPpiDSFse+F+XripYVpAIDP9+V1uyrZ1rN8fZWrhnT9A2jSwBB8cMso3DkpAQBf6JrYl2n8uNOqJKT3KFQ6/HaCD3isvnc8Fo2Mhq+n1MGt6h2msRNhzMipbDTP7Dpx2QIcm89U4NtDBQCAxaOihVqHtnhw+kCMbFND6obRMYgOkINhGFw9gp++mFejxGt/nscDq4/jkR9OgOM4/JBRhCv/sxeFtS3w85Rg0cgopEbwgbMLFa3Bog935Qr/rm5un7HWkfwaJU6VKCAWMVjQSTF2e2MYBmPjAwEA139yECs3nQfA157an2N5kM5ErTPgjc38MW4YE9un2cSmYFdfZHaZsgv3ZFfj6V+ysO5EKVQ6AyYnBePj20ZDYsxEq1Nat8KyNeizhxDb0NghxHbuOH4oMuLkRCIRwsLCkJPLF/yl4vSOsXhUNN7dcREl9Sr8fqq000wOjd6APdn8j5A5aZZdNTedUyVldtmdafwQYg9bz1RAa2CREu6LMfFB3d/BiZnGTqQ/HxCobDTP7Pr9ZJnZ30/+fEr4952TelZfamZqGGamhuFsmQLrMkvx4IyBwm33TU2EgWWh1BhQ3aTBn6fLsfdiNVYfLsQLxmLxAPDSwjR4ySRIifDFzgtVuGDM7MqrbsbeNtlcRbUtFrXJ1N8pSSF9WnNqfGKQUGD/s315iPT3xMt/nINMLMLxF660ONiaWVSPh1ZnoqJRjXA/Dzw0c2D3d7KjQcZabD8dK8GtE+IQE2if1TEvV9Woxl/G70uJId4I9fHA8Bh/zB4SjrEJQRCLGAR5y1DVpEGdUmtzzbLu0GcPIbahsUOI7dxx/FBml5PT6XT4888/oTDWS/GiaYwOIRYxWJrO/4D76VhJp/sdzK1Fs0aPcD8PjIgJsOjYpmw9ZRc1aIhtTONHp7N8cQFCOmMqen7NyM6Lo7sK09gJ8ebfnyraBLsMLIc/T/Orx35wyyghewrgg0GpxhUVeyotyh8vXD3ELLjk6ynFU3NS8fI1afjottEYnxgEjoNZoGv5zCRhtcDUSL4tF8r5zK7vL1vZsbCu+0wjjuOw4RSf0beoj8/9tEGhaFsS6+U/zgEAtAYWf+XW4Ju/8vHejpxui9G+8sc5VDSqIROL8O6NI+HXxxmJ146OQWqEL2qaNXipzbmytx+PFoPlgNFxAdj95Az89EA6nr96CCYMCBZqiwUbX0+1vZjZRZ89hNiGxg4htnPH8UPBLheg1+uFKW6U2eU4pgLUJ4sbOi2O3HYKo6ibor0mpjpsLRTs6hXutPwu6T3FdS04eInPGLm6j6axOZper0eYsfB628yuI3m1qG7SwF8uxZy0CHxz9zjcMCYGj16RjI9uG92nbbx2VLTwb4mIwaFnZ+HJOSlgGP79d7AxEJdd0YQzpQqsOcyvyPev+YMBAIW1Ld0Gis6WNSKvWgkPiajX6nN1ZlC4L7Y8Ng1PzB7U7rY3t2RjxR/n8O6Oi8iubOrg3rzsiiacLG4AwwB7npqBSUkhvdnkDvl4SPDhrfxrY1d2FUrqLcuo68jGrDLc990xNLSYB6sqFGp8svcSAAjlAToSbFyBs9bCKay2os8eQmxDY4cQ27nb+KFgl4toNk5x85JRZpejxAR6IT7YCwaWw9H8una36w0sdpznp5vMseIHkZDZRdMYiZvgOA6lDSqnWhr524MFYDk+0yY+2NvRzekz4X6eAIAmdetFlz+y+Ay3+cMiIJOIEOkvx9tLRuDx2YPgL+/jjKFR0bhlfBwGR/rh8dmDEOlvPi0tMcQbcqkYSq0BS7/OgNbA4srB4bhtYpzQr8tXarzcemOdtiuHhDvkgtOgcF/cNiEOIT4yDIv2xwe3jALA1xEzOVbQ+eqCPx4tBgBcNSS816btWSIpzAeTBgaD41rbZC2V1oDla09g+7lKfLHffMGYT/bkokVrwOi4AFwzovMMvGAfPtjVmzW7CCGEENL7KNjlIkxT3Lwps8uh0gcEA4CQ4aHSGvDaxnP49mAB9ufUoKZZiyBvGSYMsLyej1CzS6N3qh//hNjq5d/PYvIbu4T6dv2dWmfAj8f4H+d3T05wbGP6mK+nBN7GiywVCjW0ehabz/AZrAuHO346p6dUjJXXDcPmR6fi4ZlJ7W6XiEVYaqwhVqfUItzPA/++YTi8ZBIha62wtgXFdS14b0dOu+nkBpYTgnuLR0bDUYJ9PHDo2Suw/uHJmD0kHNGXBa2OF3Yc7NLoDfjtBD/1/qZxtq2QaU+mNuw4X2W2vapRjUZ199MuTAtEAMDRNgG+Zo0ev2bytz0+e5CQ2deRIGNmV00zBbsIIYQQZ0bBLicnkUgwc+ZMYTVGmsboWOkD+WDX4bw6aPUsbv7iML48kI+Xfj8rTJ9YNDIKUrHlQ89Uh03PctAaWPs32mjHuUpMeXMXMjrISnNVpvEjkdC46S9qmzX49hBfN2n9ydJu9u4ftp+rRJNaj5hAOaYnhzq6OX2i7dgJ9+ezuyobNTiQW42GFh1CfT0wwRj87+8empGEEB8ZRAzw7k0jhWBHgnGFwKySBix4fz/e3XERn+69hOK6Frz8+1kU17Xg0KVaVDbyUzanD3LsuZeKRRCLGHhKxdjxxHRse3yakOW1O7uqw4slO85Vob5Fhwg/T0zrB6/dCYn8aya7olHIFCyua8HMt/fg5s8Od3nBp0mtw0e7W1fSzCysR5MxQPb9oUI0a/QYEOqNKd1M0zTVgKtT9t40RvrsIcQ2NHYIsZ07jh/36akLk8vlwtVm05Q34hhjE/iMrfPljdh8phynihuE20xBpBvGxFh1TC9p69TUFo0BHhL7T1XlOA6vbz6PknoVtp+rwPhE115Jri253HHTdkgrtc6AqkYNfj7eOn0p0EvmwBZZzpRNcu2oaItr8bkC09iJ8PNEXrUSOVVN+OU4nyW0YFikUPC7v/OXS7Fh+RQ0qXVmxfOvSA1DRn6dWXH7dZml+N/RYlQ3aVDWoEKNsa7TNSOiIJP0n+uHcpkYg8J9EeHvCYYBGlp0mPLmbmx9fJrZRbG1GXxg+YYxMZBYcRGmt0T4eyLK3xNlCjWyShSYOCAYPx4thlJrwLnyRmRXNrVb4OC/Oy7ir9wa1Cm1KG1QITZIDq2eRWWjBh/uysX0QaF4e1s2AOC+qQO6zOoCWjO7ans5s4s+ewixDY0dQmznbuPH8d9sSI/o9Xps2rQJzcarlz60GqNDRfl7ItTXA3qWw5ubL7S7fdLAYKRF+Vt1TIlYBA/jj6jOCt/31F+5tcir5uu7dFefxpWYxo+7FWvsj5746SSmvbUbH+xqzcy4vMB0f1RS34K9F/nploscOI2tr7UdO6bg+IsbziKrRAF/uRT3TRvg4BZaJzpA3i6IcnUHdZ1KG1SobuIDXNvOVSKzqAFyqRjLZ7WfItkf+HlKcZexGHtpg8qsnmR2RRP+yq2FiAFumRDnoBa2NyouEACQWVQPvYE1C4DvvGx6Y3WTBv/dkYOjBfW4VK2EWMTgrRtG4PEr+YL9n+3Lw61fHoGB5XDdqGjcbMFUTSHY1Ys1u+izhxDb0NghxHbuOH4o2OUilBpTgXrK7HIkhmEwIiYAAFCm4Fcn+/DWUcLtLy1Ms+m4pivxLb1UpH7VwQLh3+4U7CL9Q4VCjU2nK9ptr2/pm9eiRm/AwdwaGFjra+J9uvcSDCyHSQODkRTm0wut6/+uvqw211s3DG9XM8oZRQfIMcEYyLsiNQyxQR336cEZA4VC/f3RSwvTsNAYuDtTqoDewKKyUY2vD+QD4BdM6U/na1RcAAAgs7ABe7KrUdnYOp1w+7lKs323nCkX/n3VkHCsf2gyJg4Ixs3j4/DsvFThNrGIwUvXpHWb1QUAIT6mYFfvrsZICCGEkN5FwS4X0WysbUE1uxzP9EUd4Ff6WjAsEqvuHoe1901AinGZe2uZ6nYptfaPxBfXtWDnhdYfEBTsIn1tnbFANgDcMzkRb90wHEDfZXY9svYEbv3yCH7NLOnwdo3egI925yKrpMFs+5lSBX46yt/nkVnJvd3MfispzEdYZXFIpB+usmK12f7u7SUj8Ob1w/DRbaPxwPSBkIlFeH7BYNwzOREAEOwtwwPTBzq4ld0bEcNnFJ8sbsAtXxzGhNd3Cosq3Ds10ZFNa2ecsRzA4bxafH+Yn2ZpWj3xVEmDkFkHAH9k8cGuf80fjM+XjsWwmNbM6funDRDqqD01J8XilUDDfPnAZYVC3W5BAkIIIYQ4D4qMuAhTxg+txuh4o41TMAC+2DHDMJiREtajY3obM/ZaNPbP7Fp9pBAcxwdKmzV6NKroyz3pOwqVDt/8VQAA+PcNw3Hj2FgcK+CnWjX0QeC1tlmDbcZskTVHinDj2PbTnP6z7SI+25eHb/4qwLHnrwQAVDaq8bfvj0NrYHHl4DBMtGKFVVf0yW2jsSajCM/NH+zopthVbJAXbgrip/jdNiEeN46NhVQsQkOLFj4eYiwZG9uvanV1xjR9fucF82mA0waFYkx8/3rtDov2R4iPDDXNWmGK8N+vSEZBrRJZJQrsOF8Jbw8JTpc0ICO/DiIGWDA8st1xGIbBZ3eMQWZhPSZasVhCTKAc8cFeKKxtwf6cGuECVn/O3iOEEEJIe/3/GxrpkkQiwfz584VglxfV7HK4iQOC8PLCIfjx/okYGRtgl2N6yfjzau+aXWqdAT8e5a/u3zkpHoB7ZXaZxo87rUrS37y9NRvVTRoMCPXGopF89kaAsTB9fS/WzDFZfbhI+Ldc2v4jUaU14PP9eQCAmmYNqps0UOsMWPpVBkobVEgM8cY7N460aHqUK7l87ExKCsFHt47uV9PheoNpJd0ALxmeuCoFsUFeDm6RZYZEmdcj85dLEewtM5vq11+IRAymD2q9QGSaInxFajgA4IX1Z/D3H07gi/38NMzFI6MR1cnrzlMqxqSkEKsWjmAYRnisb/7Kx4TXd2LB+/uh1dtvNWT67CHENjR2XFdOZRMWfnAAG5xkJW5n5I7jh4JdLkClUgmp9t5Us8vhGIbBXZMTMcGKK8nd8RZqdtk32LXmSBEaWnSIDpBjsbG4dqMbBbsAfvwQx1Bq9ELx6dcWDRVWGg304qcbNar10Bvs9wPzcocu1eL9XTnC3/k1ynb7rDpYAK5NKa+Dl2qwMasc2ZVNCPGR4bt7xls8PcrV0NhxHv5yKVLC+Wn0D0wfiFMvXYXjL8zG4Ei/bu7pGLOHhAv/fvN6flrzFYP5AJie5eDrKYFUzEAmEfXK4gBXGh/riLGgf02zFg0q+wbfafwQYhsaO66H4zjMfncfTpcq8Ny6045ujktzt/FDwS4np9frsXv3buiMPwg9nGA6BbGeKYiptGOB+g925uDVjecAAEvT4xFoXIGqSaO3qVC3MzKNH3dalaQ/2X6uEmodi8QQb6QPbA0Otw0eNap779y8ueUCDCyHWan8D9vKRo1Z9mSdUouPd/OrQ4b4eAAA9l6sxg8ZfDbY3ZMTnSazx95o7Difz5eOwbf3jMc/56Y4uindumpIOF5eOAS/L58sjLG0KD88PTcFy2cmYd9TM7HrHzOw6e9TMSDU/gtDjE8MwpWDw822NdvxvYjGDyG2obHjmg7k1gj/FrlZpnxfcsfxQ5ERF2FKrzdNsSCuRShQb6dpjE1qHT7dewkAcNekBNwzJdE8wOBm2V3EMX4/VQYAWDgiymwaoEQsgq8nH+Ct76Ui9UW1LThZ3AARw2eOBBuDvQVtsrvWHC5Ek0aPIZF+eP/mkQCAdZmlOF5YD4mIwZKxMb3SNkJ6Q3ywN6YPCnWKKbciEZ8hPdy4ujHAZ00/NCMJT85JQaC3DLFBXr22AqpELMIXS8dg4yNThG1NvRh4J4QQd/Ztm1XhPWVUkofYD0VGXITOwGfiyCT9/0sssV5rgXrrv2w3qnWoaTZfQn1dZimUWgMGhnrjpYVDIBWLIBWLhNpgjWoKdhHbbTlTgdyqpi73aWjRYp+x+PQ1I9oXlw401u3qrRUZ/8jiA22TBoYg1NcDiSHeAIA8Y7CLZTn8fJxfafHeqYlIHxiMWyfECfd/eGaSsGobIcT1MAyDodH+wvRPe9fMJIQQwq98u6vN4in1Si1YN5lhQnofFXhyARKJRJjGSJldrslUs8vaaYxqnQGLPvwLNc0a7HxiOsL8PMFxnDAN646J8WZX+f3lUrRoDW5XpJ7Yz5Yz5XhgdSaCvGXIfGF2p/ttPlMBPcthcKQfksJ8290e4CVFUR3Q0GL/16KB5YSFGa4ZwRfFTwrzwbHCejz50ylsP1eJYdF+KKprgY+HBPOGRoJhGLxyTRqGRPphQKg3Jg0MsXu7nA2NHeIOfIxZpvbO7KLxQ4htaOy4Dpbl8MrGs2A5YGRsAE4WN0DPcmhU64TFioh9udv4ociIk5NKpViwYIGQ2UXBLtfkbVqN0cov21//lY/8GiWa1Hphyti58kZcqGiCTCzCtaPMp2GZpjK6S7DLNH6kUvcsMN4bfsjgg0h1Sm2Xqyn+fpJ/PZqCTZcTVmS0c7CruK4Ff/v+GIrqWhDgJcXVxqyyuyYnwMdDAq2BxR+nyvD6pgsAgGtHRUNuHH8SsQi3T4ynQBdo7BD34WO82GTPzC4aP4TYhsaOa1mbUYRNpysgFTN4fsFgoYRFbR+sxu2O3HH8UGTEybEsi8rKSmgps8ul+clNq9NZ/sNfrTPg0z2XhL83GIMLv2XyS/peOSQM/l7mb3Z+nu4V7GJZFlVVVWDZ3lvxz50oNXpkGFcvA4CTxQ0d7ldc14JDebUAgKuHt5/CCLSuyNhVwMxaLMvhzm8ysOM8ny5/24Q4eBmnCKdG+OHbe8ZhanJrICs+2AtPOUExb0egsUPcRWtml/0+F2n8EGIbGjuuw8By+GJ/HgDgn3NTMTYhSKifWttMwa7e4I7jhyIjTs5gMOCvg4eFv2W0GqNLCvCyPgh1IKcGjWo9ZGIRGAY4XarAR7tzhSmM141qX1xbCKqp3KM2icFgwKFDh2Aw2G+VS3ej0Ruw5Uw5FCod1p8shUrX+lyeKKrv8D6mWliTk4I7Xc0wIZivoXWiuP0xmjV6/Gf7xU6DaZ3Zc7EKedV8Ta4ZKaG4b+oAs9vHxAfh+2UTcOz5K/HUnBSsXjZBCAATczR2iLvwMwa77LkaI40fQmxDY8d17DhficLaFvh5SoSaqMHGla/rlJqu7kps5I7jx70mbboofZsafjLK7HJJpiCUNfWLNp0pBwDcOiEOEhGDLw/k462t2QCAEbEBmJUa1u4+7jaNkfRMk1qHZd8eQ0Z+HcYlBKLGeCUuKcwHuVXNyCxqaHcfrZ7Fz8f4qY43jYtrd7vJFYPD8N7OHOzNroZGb4CHhJ9KyHEcHlx9HPtzavBnVhl2PDHd4tXlvvmrAABw39RE/GvBkE73C/HxwMMzkyw6JiHEtfXGNEZCCHFn9UotXv79LADg1gnxQpZ9kDGzq4Yyu4idUGTEBRjaBLukYlqN0RVZG4TS6A3Yfq4SALBgeCSemz8Yf5s+AJ5SETwkIrxyTRpEovavFdPjNKjoQ4Z078PducK0xaMF9civUcJfLsX/LR4KADhf3tjuPusyS1CuUCPU1wNXDQnv9NhDo/wR7ucBpdaAw3mtUyN/Pl6C/Tk1AIBL1Upc+/FBzP3vPry68Rx0BhbrMkvwzV/5KGtQmR3vYmUT9ufUQMQAS9MTetp1Qoib8PHgPxebKNhFCCF28e+t2ShXqDEgxBuPzGq9uBjiwwe76qhml01YlsMX+/IwaeVOrD1S5Ojm9AuU2eXkGIaBp5cPADUYBhB3EMAgzi/AymDXwdxaNKn1CPP1wJi4QIhEDJ6dNxiPXTEIap0Bgd4dr3ASFeAJACisabFPw/s5hmHg6+trcWYQadWo1mHtYf6DNDpAjlJjcOlf8wcjJYJfXbFWqYVaZ4CnlM/KMrAcPjbWkfvbtAHC9o6IRAxmpYbhh4xiHMytwfRBoVC06PDG5gtm+5mmMl6oaMJXB/KF7R/tzsWR564U3hNNWV1XDYnodOoksRyNHeIuemM1Rho/hNiGxo5zKm1Q4UBONa4ZEY26Fi1+Oc5n+L9x/XBhxXmgNbOrtpmmMdpiTUYR/m/TeQDAf7ZfxPjEIEQFeAqZc+44fiizy8lJJBKMT58EgC9O704vXndiyrhq1uihM3RfVHDTaX4K49yhEWYZXHKZuNNAFwAhSHGxqslsu96Cx3RGEokEs2bNcrtleHtKqdHj7z+cQJNGj0HhPvjz71Nwx8R4fHbHGNw4Lhb+cinkxkBWhUIt3G/vxSphFcTbJsR3+zhpUf4AgJyqZgDAVwfyUKfUIinMB/+9aSQAvk7h0vR44fFMapq1KFeohPZuOMkvzHDX5IQe9Z3waOwQd+FrmsZoxwL1NH4IsQ2NHeej1hlw6xeH8c9fT2PBB/txx1dHoDNwSB8QjPGJQWb7BnvzNbtoNUbrsSyHr4wF/wGgplmDK/+zF//89bSwzR3HDwW7nBzLsigo4qPjVK/LdZmCXQDQ2E12l0ZvwDbjFMb5wzpe6a4zg8L5YFdhbQvUxkLjWSUNGPryVjzx00mwLNfV3Z0Oy7IoLCx0q1VJ7OH9nTnYk10NT6kIL16dhgAvGV5dPBRz0iIA8FeOTFmCbacTrjFmgt0wOgZyWedZXSbJYT4A+CmIKq0B3x0uBAA8MXsQFo2MwpvXD8Ofj0zBK4uGYv3DkzEhMQg3j4tFQjCfuVVUy2cobj1bgRatAQnBXphw2RcrYhsaO8Rd+Hrav2YXjR9CbENjx/l8uCsXhcbvY3nVSuRV8yUv/rVgcLt9owLkAIDdF6pwvLCu3e2kc3suVqGgtgW+nhKzlcX/OFUGjuN/v7nj+KHoiJMzGAw4c5ZPV6R6Xa5LIhYJRXK7m8r4Zxa/Ml6EnyfGJVj3wz7M1wN+nhIYWE5Yte7tbReh1rFYl1mKT/ddsq0D/ZTBYMDJkyfdalWSntLoDcJqiv+5cSSmtPlAbcv0hcU0vTG7ogm7sqsAQFh1pzvJxuBrSb0K3x0qQEOLDrFBcsxJiwDDMLhpXJywT0qEL378WzreuH44EkP4lRwLjF+ufs3k23vtqBjKfrUTGjvEXfTGNEYaP4TYhsaO86hqVOOzvZfw4e5cAHyZiydmD8LfZyVh62PTMDTav919ZqWGYUpSCJRag7CoFumeWmfAqxv5eMDN42Jxz+REs9tNwUZ3HD8U7HIBptUYpZTZ5dIsKVLPcRy+PVgAALgjPd7qGm4MwwjZXZlF9Vj1Vz72XawWbv/FGOQg7mvHuSrUKbUI66bAfJQ/H+wqN05jfGtrNjgOmD8sAgNCfSx6rCBvmVCsdKWxVte9UwZ0+7qOD+aDXYV1SpwpVeCv3FqIGOC60dEWPS4hhJiYLjTZM9hFCCGujOM43PnNUeG72xWpYbh3aiL+fkUynrgqBRH+nh3eTyYR4bErkwEAZQ3qDvch5jiOw4o/ziG/RokwXw88ckUyZqaG4c+/T8GQSD8AwP+OFltc99nVUHTEBZjKKckkdDpdWetKiZ2/WW09W4lTJQrIJCLcPC7WpscxZco8v/4MXv7jHAD+SgtgPr2RuKf/HeWnIi4ZGwNJFwH2yDbTGHeer8SO85UQMcATs1OseryBbQJj/nIployN6fY+cUGt0xj/u+MiAOCaEVFUmJ4QYrWeTmPMq27Gkk8PYoexvAAhhLi6UyUKYUXu5DAfvLJ4qMWZ9cE+fN0uWpHRMj8dK8YPGUVgGGDldcPg58n/XkyL8hdqon269xLm/XcfKhvdL4BI0REnxzAMfPwDAVDNLlcX4MW/eXVWs4tPYeWDU/dNTRQ+LKx1VVq4MCU2McQbK65Jw2d3jGk3vdEVMAyD0NBQmtpmoeK6FhzIrQEA3DS266mIpmmMWSUKPPcbXxxz2ZREJIVZltVlYso0BID7pw0QVpTpSryxZtfmMxXYcb4KIgZ45Ipkqx6XdI3GDnEXPh6tC8SY6p5Y4/EfT+JoQT3u/e6YsI3GDyG2obHjHH41zgRZNDIK25+Yjmjjd0JLmFZkbNbo6QK7Bb49aKxne+UgXDHYfMbFyNgA4d9lCjX+tuYkfAND3Gr8uE8pfhclkUiQOngI8NcRmsbo4oTMrpaOg107z1ehtEGFcD8PLJ9p+w/7mSlhOP3yHGh0LPy9WgvjDwr3xbHCeuRUNWFIlJ/Nx+9PJBIJJk2a5OhmOAWO4/DfHTngOGByUjDigrvOkjJ9sTlnvLI3MNQb/7jKuqwugK/vVVLfgoUjonDtKMumIcZf1rbbJsSbZYiRnqOxQ9yFqWaXgeWg0hksCribKFp0OFWiEP6e995+PDxzIK4eHkXjhxAb0GdP/6fWGfD7qTIAwPWju8/Gv5yfpwRSMQOdgUOdUitcPCXtXahoxLnyRkjFDG6f2H6V8zlpEbhudDSi/OX4IaMI4X6emDBhFK3GSJyHwWBAXgE/rUgqcZ8orTsyZXZ1Nuf6txP8VZTrLVzpriueUrFZoAtond54sbKpR8fuTwwGAy5cuOBWhRpt9dWBfPyaWQKGAR6YPrDb/ZPDfYQMQX+5FF8sHQtPqfWvy8GRfvjm7vG4brTlxeVjg7yEK4PRAXI8PnuQ1Y9LukZjh7gLb5kYpjKBjSrLpzLqDSxW/HHWbNv58kYsX3sClYoWGj+E2IA+e/q/7ecqoVDpEOnviclJHS9i1BWGYYTvcDSVsWumDLpZqWEIND5nbcllYvznxpF4ck4Kfn1wEj65dSSK83PdavxYHeziOA6HDh3Chg0bUFVV1RttIlZgWRYFxfwLnTK7XJtfF5ldB3NrsCebLyTfW0W4B4XzmTEXK5t75fiOwLIssrOz3WoJXlto9AZ8ujcPAPDCgiGYmhza7X3CfD2x9bFp+HLpWOx5cobFRentwUMixu/LJ+PH+ydi+xPThC9NxH5o7BB3wTAMArz495AGleU/vL7Yn491J0ohFjGYnBRsdtt/tufQ+CHEBvTZ0/+ZVuy+YUyM1QtlmQR586VYapo1dmuXq1FpDfjpGP9cLxnTfZ3mhBBviBi43fixOoctLCwMKpUKcrkcCoUCt99+O95//334+NAUEUeh1RjdQ4Cc/7J9eXHBrJIGLP06A3qWw4yUUCSF+XZ09x5LNh73UrXrBLtIxz7anYtNp8vxj6sGIS3KH4//eBI1zRpE+nvijvT2adKdGRDq06dBrrZiAr0QE0gF6QkhPRfoJUWdUmtxloHewOK7QwUAgBXXpGHJ2Bh8e7AAAXIZnv41Cz8fL0HCsF5sMCGEOEBZgwr7c/iL7zeMsX4Ko0kwZXZ1649TZVCodIgJlGOmcSEx0p7V0ZGff/4ZTU1NqK6uRkZGBgoKCjBhwgRUVFRY/eArV67EuHHj4Ovri7CwMCxevBjZ2dkWtSE1NRWenp4YNmwYNm3aZPVjuxJhNUYKdrm0cQn8QgTbz1WiXKECwH+h/uevp6FnOVyRGoZPbx/Ta49vWuGutF5lU5FeAMjIr8OTP59CrRVXanQGFq9tPIdp/96NZauOolyhQkOL5T86iHXqlFq8tTUbZ8sacc+qY5jw+k4cvFQLALhrUgIF1QkhbifQlNnVSc3MtqqbNLjn22MoV6gR5C3DkrEx8JCIcf+0gbhxXCwWDIsEywGbS+i9lBDiWn49XgKOAyYkBiE+2Nvm4wT78O+5tc30Xb8zPx4rBsDXpbU1g84dWP1JO2PGDKFuysiRI7Fz507Mnz8fU6dORXl5uVXH2rt3Lx5++GEcPnwY27dvh06nw1VXXQWlsvPV3g4ePIhbbrkFy5Ytw4kTJ7B48WIsXrwYZ86csbYrLkEkEsE/kE+PN9XHIa5pbEIQJiQGQWtg8eX+fADAL8dLcL68EQFeUrx5w3CbaiJZKsLfEyIG0OhZVNuQVnyxsgn3rDqKX46X4PvDhRbf76sD+fjyQD6K6lqw80IV0lfuwshXtuOqd/dCpe3ZnHORSIS4uDiIRPSjw2RdZkm7bTKJCP937VDcMyXRAS0i/RGNHeJOAi3MMmBZDg+sPo59F/nMhnsmJ8BDYv65fPN4frpJvV5G44cQK9FnT/+l1bNCAObGsd1Pq+uKqfxELV3Y7lBhrRLHC+shYqwrX+OO46fHPVUqlVi2bBmSk5Nx1VVXWXXfLVu24K677kJaWhpGjBiBVatWoaioCMePH+/0Pu+99x7mzp2Lp556CoMHD8arr76K0aNH48MPP+xpV5ySWCxGZAyfJkoZF67v7skJAID9OdXQ6ll8uDsXALB8ZhJCfDx69bFlEhEi/DwBACX1KqvuW6/U4t5vj6FZwxf3PZJXZ9H9KhvV+O+OiwCA60ZFIymsdUpcTbMWWSUNVrXjcmKxGKNGjYJY3HtBQmfCcRx+PMp/UXllURpmpYZBxAAf3zoat02Ip/cYIqCxQ9xJkDGzq76bH15rMopwvLAeMokI7908Eg/PTGq3j2k1R0Yio/FDiJXos6f/0RlYPLTmOAY9vxkl9SqE+Mgwb1hEj47ZOo2RanZ1ZF1mKQBgclIIwo2/zSzhjuPH6ppdd999N0pLS1FSUoLS0lI0N/P1eziOg6en5U92RxQKfnnmoKCgTvc5dOgQnnjiCbNtc+bMwfr16zvcX6PRQKNpHSiNjY0AAJ1OB52OT0cXiUQQi8UwGAxmBdtM2/V6vdm0LbFYDJFI1Ol203FNTMt76vV6i7ZLpVKwLGu2UgLDMJBIJO22syyLfNNqjCLGJfpk2t5Z2925T6PjAgAAOVXNeGnDaZTUqxDqI8OtxivFvd2nqABPlCnUKKxuwvAoX4v79NKGMyiqa0Gwtwy1Si0yi+rR3KKGh1Tc5Xn6+Vgx1DoWI2L88ca1Q1DVpMG935/AhQp+Rcij+bUYHesHADiUX4/MwgY8MC3BLJ23qz7pdDpkZWUhLS0NYrHY7V97J4oakFPVDE+pCAuHhePG0VGoU2oR6usBlmWdsk9tt7vKeeoPfdJqtTh79izS0tIgkUhcok+ueJ6oT/bpk7+c/2FQ26yGTqcTthdUNyKrRIG5aeFQ61i8Z7w48885gzA/LUzoQ9s+icE/blOLGgaDAQzD2LVP5Qo1Xv7jPG4ZH4vZaZFudZ6oT67fJ4PBgHPnzmH48OHtSmo4a5+62u4MffrqYBE2nW4tZ/TElcmQMpxwX1v6FCDnn4/qJv43PJ2n1j5p9CzWHuFnyNwwJsaqPhkMBpw6dUr43dNf+nR52y3tk1QqRXesDnZVVVUhISEBkydPRnR0tNl/wcHB3R+gEyzL4rHHHsPkyZMxdOjQTverqKhAeHi42bbw8PBOa4atXLkSK1asaLd927Zt8PLiaxDFxcVh1KhRyMrKQlFRkbBPSkoKUlNTkZGRgerqamH7yJEjER8fj3379qGpqUnYnp6ejrCwMGzbts3sBTNz5kzI5fJ2tcXmz58PlUqF3bt3C9skEgkWLFiAmpoaHDp0SNju6+uLWbNmobi4GCdPnhS2h4SEoLquHoAYVRVl2LSpxOn7FBoaikmTJiEnJ8eshhv1ie9TXJAXiupa8MNR/lxfHaVCs6IeXn3QJ0ZZB0CEXUdOIlxTYlGfNAZg82kxAAafLx2De74+DIWGxa0fbsdtSSwmjun4PE2YOFFYZWSoRx02b94MAPjx7hlYm1mFN7dexNvbc7DpaDZuHsjixeP825mi+AJGBnPd9ikoeTTW7D+PeK4KJSUldj9P/fm1x3HAPkUA4qOjEM7VoaS8Esn+HNbmigCIsGBYFM5nnXCqPrnieXKGPpWUlLhcnwDXO0/Up571qb6C/4w4k1OATZvykJ6eDo3EBwvf348mHXBDogF6FqhpFiMmwBMBNWewaVNreY22fapUAYAEah3/5b++vr7HfdIFxOPxtUdxTYwGx2sYZFSLsCu7GrufnIHyi6fc5jxRn9yjTwCQkJCA/fv3u0yfnPU8NemA/57is7C8xBwmhnGQV5zCpk2netSn/DoGgBjFVfxMEDpPrX3KqGZQ3SxGiLcE84ZGYt++PRb3SSKRoKSkRPjd01/6ZOt5WrRoEbrDcLZWmrazBx98EJs3b8aBAwcQE9P56g0ymQzffvstbrnlFmHbxx9/jBUrVqCysrLd/h1ldsXGxqKmpgZ+fnxGiDNHh/V6PZ76Zjs2FomxZEw0Xl+c5vR9oqstXffpkf+dxJ9ZfH28W8fHYMXCIX3Wp3e2ZeOjPXm4ZVwMXl2UZlGfNmaV4/GfTyMh2Au7n5yBv/+QiT+y+OD0P+cMwt+mD+zwPGUUNOC2rzLg7SHGwaenC1M/JBIJjhfW44ZPW99821o2OR7PzE3ptE9KjR4rNl7AbyfLAADDAln8+MgVkEqlbvPaO1HcgBs/z2htl4jBn8sn4dpPDkGlY/HLA+kYGePnVH0CXO889ec+qdVqbN++HbNnz4ZMJnOJPrnieaI+2adPPxwpwLO/ncX05BB8uXQ0xGIxlnx2GMcL6/m2iRnoDPz93rhuGK4fFdlpn0obVJjxzn5IGA7nX5kjtKcnfRr5yg40GcsEeEhE0Oj5268eHon/3jjcbc4T9cn1+6TT6bB9+3bMmzdPqCHt7H3qant/79O7O3Lx8d48DI/xxy/3jzc7Jz3p05H8Otz+9TEMDPXGzn/MoPNk7FNRnRI3fHYEdUodnpozCA/PTLaqTzqdDps3b8bs2bOFrChH96nfZXb1huXLl2Pjxo3Yt29fl4EuAIiIiGgX1KqsrERERMdzgz08PODh0b6WkVQqbfcEicViIaWvLdOLwNLtnT3x1mwXiUQQidrXx+lou/E7DTykYpfpE9B52929T6NiA4Rg1z/nDTFrV2/3KT6Yr5lVptAIbeuuT1vOVQEAFgyPBMMweGb+ECHYVd6oER7r8r7+fJy/6rBoZDT8veVmtw2N9oeXTIyWDgrUnyppbNcvU584jsO/fj4tPH8AoGHbvx+4+mtv05kqs9sMLIfn1p+FSsciKcwHY+ID232JNOmvfbJku7OdJ0u2O6pPpu1SqVR4LGfvkyueJ+qTffoU4st/BjWodJBKpThTqhCKA/vLpag3rtIYF+SF68fEdFrfUCqVwkfOf2nTcwxYloNU2rM+FdQohUAXACHQBQA5lc38DxwDi//78zwGhfviFmPZA1c8T9Qn9+kTwzAu16fOtvfXPik1eqzJ4Ou8PjRjIGQyWZf7W9J203Y/L/63u+l7Pp0nfvuTv5xBnVKHodF+uGfyAADW9cn03b6jOIgzvfas4dBqwxzHYfny5fjtt9+wa9cuJCZ2v9JXeno6du7cabZt+/btSE9P761m9msiUdvVGKl4tDu4dUIc7kyPx88PpMNfbv2g74mYQP4Lf0l9i0X7N2v02J3Np6cuGBYFAIgOkOPlhUMAADWdrOpYp9Ri8xk+IHZTByu6eErFWHX3eHyxdCw+uW00JicFY2Aov8RxVqkCl6qb8eDq45j+1m7kVrWmwa7LLMWfWeWQiBjhC7+n3KvDN2xXpTew2JhV1m57ZlEDAODmcbGdBroIMRGJREhJSXGrsUPcV5A3/1lb18IXqP/+EF8vZcHwKPy+fAoGhfMXgv5x1aBuv4u1XTVZ34O5FRzHobpJg2/+ym932z/npgLgP6s5jsPWsxVYdbAAz/12Gu/uyLH9QQlxMPrs6T9+yCiCQqVDYog3Zg/pWUH6y3nJ+PfJji5qu6vfT5Uhs6gBXjIxPr9jLOQy64vMu+P4cWhm18MPP4y1a9diw4YN8PX1Fepu+fv7Qy7nf1QvXboU0dHRWLlyJQDg0UcfxfTp0/HOO+9gwYIF+N///odjx47h888/d1g/HEksFsMvIBBAA2QU7HILXjIJVizqvK5db4oJ5OvcldSrwHFct0GRnecrodWzSAzxxuBIX2F7iC9/xaamuf3KVkqNHnd9kwGNnsXgSD8Mj/Hv8NjjE1sXspg3LBIcx2HsaztQq9Tiinf2Crf9fKwEz84fDLXOgLe28vPIH589CIkh3vghoxgenvIOrza4qoOXalHTrEWQtwyf3DYaxwrrhefFx0OC60Z3nV1LCMB/9qSmpjq6GYT0iQDjaowNSh3+yq3BT8f5bIal6fGIDfLCxkemoqxBhYQQ726P5SFp/a6mY7vYsRvv7czBf9sErh69IhnrTpTgwelJuG50NN7ccgFKrQH1LTr8car1Aseqv/LxxOxBnR63WaNHo0qHqAB5p/sQ4ij02dM/aPUsvjrAB9rvnzbAbGEoe5AbS5eoHBjs0upZLPv2KLxlEnxy+2i7XAjOqWzCf7ZfxMIRUZg/LLL7OxhxHIf3dvLv9w9OH2jz+7M7jh+HRkc++eQTKBQKzJgxA5GRkcJ/P/74o7BPUVERystbpxxNmjQJa9euxeeff44RI0bgl19+wfr167ssau/K9Ho9ikr4LzGU2UV6W4S/J0QMP02io0DV5TYapwsuGBZp9iER7M0Hu2o7yOz6/nAhskoUCPSS4t2bRlj84cIwDK4f0z5Qk1HAF7f87lABKhrViPL3xLIpiZAYP5jrFY3t5qm7sg3GWmULhkViwoBg3Dd1AAK8+KyFt5eMQJB3x2nohLSl1+tx8OBBtxo7xH0FGYNdTRo9Hv/xJDiOz4Idl8BfdJFJRBYFugD+u5rph2GzuvvP0Y7oDSy+M2aXAcDCEVF4fPYg7H96Fm6dEAdPqRhhxotKF8obhQxrAGhU67v8AXnft8cw/a3dFmdwE9KX6LPHsViWw/6cany4OxflCjVCfT1w7ahouz+OtzFrSWtgoTP04KqAFRpatFi56Tw+3XsJVU1q/HaiBPtzarDlbAUqGzueiWKpmmYNzpU1Yva7+7D5TAU+2JVr1f0PXapFXrUS3jIx7p7S/Uy4zrjj+HFoZpcltfH37NnTbtuSJUuwZMmSXmiR8+E4Dk0tKgAiCnaRXieTiBDh54kyhRol9S0I9W1fD8+krEGFXRf42lALR0SZ3Rbiw/9wqFWaf9HXG1h8d7AAAPDs/MFIjfCzqn3/nJsKpUaPvRer8ey8wXh4bSZOlyhQ2ajGR7svAeCzujylYmG8qLV6i96LXIFaZ8DWs3wG7aKR/DmRSUT43/0ToWjRYcIA21fUJe6F4zhUV1e7zdgh7s1PLgXD8CvZVjVpEO7ngZcWptl8PE+JCEqtAWobsxYOXqpFnfHz8+WFQ7Ckg+n+MYFyVDVp8Pn+PGj1LAaGeqOsQQ2VzoCqJjXig9sH53KrmnAorxYAcKlaKWRzE9Jf0GeP4yhUOtyz6qiwMAcA3DM50Wxqtr20naLXojXAX967v3HLGlS4/pODKFeoAQBvb82Gnm19jeVUNSHC39OmY28/V4n7vjtmtu18eaPF92dZDp/uywMAXDs6Gj4etodv3HH8UHTEBZgC3jIJnU7S+9pOZezK2iNFMLAcJiQGISXC1+y2YB8+SNbQojO7YvPz8RKUKdQI9pbhmssCZJYQixj837XDsP/pmZg/LAJR/p7Qsxzu//44FCodBoX7CNP0JGL+yjrrPu/32HWhCs0aPaID5BgdFyhsT43wo0AXIYR0QixicEVqmPD38plJNtVLMfGQGi+26GwLdpkydG+fGIe7JifCu4MfP7FB/Gf1HmNW1w1jYhHux3/2dpal8PvJ1umOKq37XPknxN18sS8PV7yzBwU1Sovv89bWC2aBLl8PCW6dENcbzYNMLBJmYLT08nsRx3F4Zt1plCvUiA/2wui4ALNAF8Av9mEtrXGxkA92dVwn0dJ+vbUtG/suVkMqZnBneoLV7XB3/WI1RtIzpgKnUjEVlSa9LyZQjoyCroNdBpbDj8f4miZ3TUpod3uAXAqxiIGB5VCn1CLczxMFNUq8tvEcAODBGQN7dKXINPUxfWAIfs0swaniBgDAU3NShekjpv8b3CjYteFkKQDgmpFRENm5vgIhhLiyD28djY9356K+RYebxvXsB56nRAxAB7Xe+uk55hm6nU8fMi0oA/Cfd9ePicbu7CoU1LagolHdbn+O47ChTW0vKgxNiGtq1ujxf5vOAwDe2HwBn94xptv7nC9vxJojRQCANfdOQJ1Si8QQ715bKIthGMhlYjSp9b3+XrTnYjX2XayGh0SEr+8ah4GhPsirbkZhbQv+OFWGdSdKkdNmsStLfLwnF29vzcbtE+ORVaIQtt8yPg4bT5WhSaNHWYMaSWE+ZvfjOA6vbzoPlc6AVxcNxU/HivHJHn5mysrrhiM53Dx5gHSPgl1OTiwWw9c/AKhppMwu0ifarsjY0KKFTCKCl8z8reRIfi2qmzTwl0txxeDwdscQiRgEectQ3aRBTbMGDIDbvzoCpdaAcQmBuHuy7fPR23rsymT8mlkCABgS6YcrB7demTdNY5TIPNyiQL2iRYfdF/gr/LZkzRHSllgsxsiRI91i7BAC8KsoPnFVil2O5WG8mGNDrAu7jRm6Uf6eGNMmQ/dybacgLhoRhTBfT4T78dNwqjoIdp0qUaCwtrVOl5KCXaQfos+enjNd+ASA/TnVUGr0ZtmhOZVN+O1EKW4ZHydkiH51IB8cx9d7nZwU0ift9JZJ0NRNjUF7+C2Tfz5uGR+HgaF88GlAqA8GhPqgUa3jg11WZHatyyzBv7fwCz+ZaisuHBGFF68egiBvGY4X1qGpshllDap2wa7jhfX4Yj9f+P+60TF4bSMflHz0imTc0EFdYmu54/ih6IiTE4lEkHrwwQeq2UX6gukL9ImiBsx6Zy+u+/hgu31MhennDY3oNAgbbCyEXtOsxZO/ZKGkXoWEYC98dNtou63qEhvkhVV3j8OExCC8c6N5sXtTejQjEjv9Erx6A4u9F6uh0Xf+heDHY0XQGlikRvgiNYKuDJGeEYlEiI+Pd/qxQ4gjmDKXNTakFq83/lBd2E2G7oyUUKSE++LuyQl44/rhAIAIYRpj+2BX2x/AAE1jJP0Tffb0jEZvwJfGYArAB7Vf2HAGLVo9Dl6qwc7zlZj97j58vOcS/vHTKQD8YlK/G7M+7+lBcXRreRmniis1vfdepNTosf1cJQB0WGg/OYz/vpxT1WxRnauS+ha8uOFsu+1PXZWCUF8PiEWMsJJiuaL9DJmv/2o9N6/8cQ5NGj1ig+R49IpkyzrUDXccP5TZ5eT0ej0qqvhsDQp2kb5gyuw6ZyyuWKfUQtGig79xRT+dgcXm03yw6+rhnWcQhfh4AGjCl/vzsD+nBh4SEb68cyzCfG0rANmZGSlhmJES1m67xPhGr2xRQa/XQyJx3rfD93fm4P1duXhi9iD8/bIPRJblsOZIIV7fdAEAcPfkBLssn0zcm16vx759+zBt2jSnHjuEOIKHxFiLxsrVGKubNNh5nl/45bpRXV/lj/SXY+vj08y2mTK7Lq/Z1aTWCXXAogPkKG1Q0TRG0s6X+/NQXNeClxamOawUgjt/9nAc1+Pvb1/sy0N+jRKhvh54fsFgPPHTKazLLMXO81VQqHRm+2YU1OFIXi1+OV4CrZ7F8Bh/jI4L6NHjW8NUF7HFxtqGlth2rgIqnQEJwV4YHuPf7vYBod6QiBgoVDrk1ygxINSng6O0+veWbDRr9BgTH4iHZgzEEz+dwnPzUxEX3JppG+nP/44qbeAvOqh1Bjz58ymcKmlAcV1rAOyksQTLjWNi7Tbe3HH8UHTEyXEcB7Xx6hvV7CJ9IS3aH76XFcMtqmud+vBXbg3qW3QI8ZFh4oCgTo8TbFyRcX9ODQDg71ckIyms7zKOTAXqdSzn1KuStK2PdugSv4pWcV0LbvzsEH4/VYbfTpTiBeNVphAfWZc1XgixFMdxaGpqcuqxQ4ijeBoznlVW/oj75XgJ9CyHUXEB7RZ+sUSYEOwyz+z6ZM8l1Cm1GBDijavS+NIDFOwibZUrVPi/Tefx7aFCnCxpsOg+Wlvm6XbDXT97dmdXIe2lrfgho8jmYxTXteCDXbkAgOcXDMaikdH4700jIWJgFujy9ZDgutH8d8WbPj+Mn4+XgGGAF68e0qcXS72NJVJaNL33XrT+BB/kXzQyusO+eUrFmGhcwMl0oaEzFQo1Nhkv9q+4Jg1XDA7HqZeualfjMTqAfx8ua+ADWx/vuYSNWeVCoCu+TWBMJhHhhrE9n75o4o7jh4JdLsDA8YNTRpldpA/4y6Xtphq2DXa1TmGMhKSL12SEX2sGV4CXtMNC9r1J6iKrMR66VCtcpT9TqgDLcnhxwxlk5Nfh7z+cwLs7LgIAUiN88dWd43pliWhCCCGWM9XsUussDwboDSzWHOHrv9xiY4H8cF9+GmO5Qi382CltUOGrA/zUmWfmpcLXk8/S7u0V0Ihz2XCyDKbfx2fLGrvd/49TZRj0/Gbc+XUGqps6Xv2TdO9iZRMe+eEE7v7mKFq0Bjz322mbj/XqxnPQ6FmkDwgWarcuHBGFj24djWmDQnFnejzCfD3w7k0j8czcVHi3WXH2/qkDMDah8wvYvUHI7Oql96KaZg0O5PIX3Bd3MIXRxFTvd/3JUqh1Bny0OxfPrjsNvcH8/XvNkULoWQ7jE4IwNLp9lpiJaRpjWYMKhy7V4lNjAXoAGJcQiN8emgzTT6xn56UKmWDENu6Rv+biDMJqjBTsIn1j2qBQ7HxiOl7YcAb7c2qEYJdSo8eWM/wqUVcPj+zyGHdOSsC58kbsz6nBE7MHdbh0em8SG6cxOvtqjOtOlAj/btLocbKkAfuM2XIAv2pmsLcM6x6a1G4hAUIIIX3PlNml7aLOIgBsO1uBnKpmPDB9IL7Yn4+SehWCvGW4ZqRti4wkhHhDxPAXqJ759TSWTU3EUz+fgkbPYuKAIMweEo78GiWA3s2mIM6F4zisy2z9rnGuTIELFY04W9qI60Z3nBHzsfEH/N6L1Xhj8wW8c+OIPmuvq1DrDHhg9XHkVSuFbRzHr6a4+XQ5Jg4IFgrIdyerpAHbzlVCxACvLEozO2fzhkVi3jD+O/uKRUOF7a9dOxRP/pyFWalheHpuqp16ZTlvDz7YZW0GrKV+OlYMA8thRIw/EkO8O93visHhePmPczhb1ojUF7YI268ZEYX0gXzWF8tyWGcsdL90UnyXj2uaCnnwUi0OGmdkzBsagY9vGw2AX4nyszvGorJRjdsm9GzlX0LBLqcnFovhIfcGmltoNUbSpxJCvDEqLtAs2LUuswTNGj0GhHhjXDdXgKIC5Ph+2QSodQaHZBtJhMw0kdOuStKibQ0u+nlK0KjWd7hgwIsLh1Cgi9iVWCxGenq6044dQhzJ0/h+3NVMQaVGj/u/Pw4AeGtrtrD9jonxNn9mhvt5YsWioXj597P48VixMAWeYYDnF/BTlLyEbAoKdhHe2bJGXGyzGt2uC1VYf6IMKp0BMYFyTDBO8zI5U6rA+fLW7K/d2VVgWc4udYf642dPuUKFEB8PuycdfLk/zyzQZbJs1VEcya9DTKAcmx6dCj9jNmZnDl2qxSM/nADAT9dLDrdsCvS1o2IwLTkUQd4yh9R6lUv590llLwTe1ToDvjZmtC5NT+hy39ggL9w1KQFrM4rMpubm1yiFYNexwnqUNqjg6yHBlR2sQt+WKbhmurAwOi4A79400uw5nj2k62PYqj+On95G0REnJxKJwDH8aaTMLtLX4oxXlH7IKML2c5X41rjE7tL0eIu/1DhqWp1pvBg4OO2qJFvPVqBFa0B8sJfZlf64IC+8d/NIDAjxxmNXJlOdLmJ3IpEIYWFhTjt2CHEkuWk1xi5qGm07V9Fu2/xhEbh3as9WQ7tjYjyemD1I+HtCYhDWLJsgTLsxXRjpzaLQxLn8doLPWBlmfI1UNmqEbJsLFU3t9v/ZGES9akg4fD0kqFNqkVWqsEtb+ttnz7azFUhfuQtvbr5g1+Nq9azwnfrdm0Yg7/X5woyJI/l1APjM/efWne6y/lKTWodl3x5FTbMG3jIxHpmVZFU7gn08HLaokZDZZeM0xrNlCry04Uy7wvsA/xqtadYiOkBuUabsy9ek4eSLs3Ho2VlYms5nbuXXtAaATWNk7tCIbn/XMAyDG8fGCn+/smhon/0W6m/jpy+4T09dlE6nQ0MjP9hkEipQT/pWXJv06fu+O4bcqmZ4y8S4foz9iin2FlPNMT3LQau1bkWs/mLNYb5Q6eKR0bhpbByGx/jjmXmp2PmP6Vg0Mhq7npyBx64c1M1RCLGeTqfDn3/+CZ2u/ZdIQkjXTDUjWzSdjx9T4WSTe6ck4uPbxgg1tXrib9MG4PaJcbhrUgK+XzYBk5JChNuEzC4N1ewifK0400qdf78iGb6efDDUVCc4r7rZbP8WrR7rjD/8b58YjynJ/Gtr14Wui3tbqr999vxtNZ99+eWBfLsW/d58phzVTRqE+XpgwbAoiEQMpg0KFW4flxAIsYjBxqxy/HK8pIvj8BdFvWVibHtiererCfYnpppdShuyTFmWw4L3D+DbQ4X4YGeO2W16A4vP9uUBAO6fNsDiZBEvmQSR/nIkh/HPoSkzS6tnhcL0XdX+auvWCXGYkhSCx65M7rK+l731t/HTF2heiwvQsxwAhjK7SJ9LCG5fK+C60TF2+TLe29quXqpnOcgc2BZbnCiqx7HCekjFDG6bEIcwP0/8vnyKo5tF3IheTz+GCbGFqWaXupPMrtyqJuzLqQYA3Dc1EbVKLR6fbb8LFxKxCK8tHtbhbV7G+pk0jZEAwP7cGtQ0axDkLcOMlFC8tngoTpcoEBMox8t/nENejfk0u42nytGk1iMuyAtTkkJQUKvE5jMVOGdBUXtL9ZfPnrNlCrSNbx3Kq8XExGCzmQ1VTWp4SsXdTjVsK6+6GS/9zq+ifduEeKFMzfWjYxDoJYO3jF8h8OM9uXh720V8vi8PS9pkCrVlqrX20MwkRAc4V6FzYTVGG96Ltp5tzYz98kA+qps1eGpOCmICvfBDRpFQz/bGTp63riSG8MEu02t/T3YVFCodwnw9hJUbu+Mvl2L1vROsfmx76C/jp69QdMQFmApsi+0wF54Qa4T5eeL5BYNxx8TWYoym9N7+ru1KkQYnW5LxdIkCD67OBMDXXwhrs7IlIYSQ/s1Tagx2dTJV8OPdl8Bx/DSwfy0Ygv/cOLLPFnExZXb1VlFo4lx+MxbdvmZEFKRiERaNjMbzVw/BkCg+GyWvWgmtnkVtM7/iomnF0FsnxEEkYhAbyF8ULW1QOaD1vWu1Mbve5NYvjpitlrg/pxpT3tyN6z8+aFXW1+ubLqChRYcRsQG4f9oAYbtYxGD2kHBMSgqBSMTghjF8oCavRtnhe0lxXQsO59WBYSzPOOpPhPciK6cxfnuwAMuNNcpMNpwsw3+2XcSFika8+ud5AMDDM5OE7DFrJIbyxeyLalugN7BYf7J1jNBv8f6HMrtcgOl3usSN5t+S/uPeqfwH8bxhEdDoWIsLXzqapM0Hks6JlmTkOA7PrMtCRaMaA0K87Xq1nxBCSO/zkHRes6tCocaGU/y0seVW1texB9MPTCVNY3R7tc0aoXbcdaPNgyUDjD/4SxtUGPPqdii1ejw7bzBOlSggFTNYYixnER3IZxOV1rf0qC1FtS1Yf7IUpfUtUFUxuELPQurASQRNah02GIMcc9MisMWYSbTlbAXeuH44yhUq3LPqKHQGDjlVzcivUVo0hbCmWYM92fyUz3eWDO8yGBPu5wF/uRQKlQ6XqpuRFmU+HW69cTpp+oBgp8vqArqexmhgOdz77VHUKbX44f6JQq3B4roW/N+m8zCwHK4aEo5t5yqF+xzJr4NGz0KrZzEzJRR3TUqwqV2Rfp7wkIig0bM4VaLAduNjXDva+QKK7oCiI05OIpFAbHy3p1mMxJEmDQzBzNQwRzfDYm2DXWCcZ/BkFjXgbFkjZBIRfn4g3Sm/wBDnJ5FIMHPmTEgkdM2MEGuZpgpqO7jQsvZIIQwsh/GJQRgeE9DHLWstUK+iaYxu7/P9eVDrWAyL9heK05sEe7cWf2jS6MFywP9t4jNm5g6NRLCPBwAI31Ea1Xo0qm2rE8RxHO7//hj+s/0ifjxWgt+LxNifW2fTsexl/YlStGgNSArzwds3jsANxuBeQ4sOLVo91hwuMruQ+u8t2TicV9vtcTecLIOe5TAiNgBJYV1fPGYYBikR/D7Zly0UwHGcUDvtutH9v45uR7y7eC/65XgxdmdX41SJQqhZdrK4Afd+ewxaPYtJA4Px2R1j8PyCwcJ9ShtU+NNYW+vpuak2rw4qEjFINT7vD6w+Dp2Bw7Bo/3bBxv7IHb+7Oc8vPNIpVpjGSKeTEEu1TTXWO9E0xu8OFQAAFo2IEr5MEuIIcjkFWgmxhYdx5a3Lpx7pDCzWZvAr2d2ZntDXzQLQJrNLq7drwW3iXMoVKnx3kJ+S+Pjs5HYr8jEMg0kD+fpEph/+JvdMThD+7e0hQYAXf1G+tN62qYwnixvarfpYY5w26Qgcx2HNEX4K420T4uDjIcHbS0YIAcDz5Y3431H+9kHhfDbXlrMVuOubjE6nLpuYamxdb2GWUGonwa7Mogbk1yghl4oxb2iEhT3rX+Rt3ova0ugNeHvbReHvrw7kQ6HS4f7vjiG7sgm+HhK8uHAIGIbBvVMHIPu1uUJReQCYOCAIgyP9etS2243lW6qb+NfhTeOsr/3lKO723Y2iI05Or9dDo+WvlIgdtDQsIc6IYRghu0utdY5VSaqa1MKKL3famH5NiD3o9Xps2rTJ7QqdEmIPMuO378szFvbnVKOmWYMQHxmuSgt3QMtag10s1/E0S2udKVUIq5aR/q9Zo8cdXx1B+spdUOkMGBMfiJkpHWft//uG4fjsjjHY9Pep8JfzAa0x8YEYFRdotp8pu8vWYNdPx/gA8LWjonHtqCgAQKOqb1fRZlkOJfUt0OpZrPjjHC5UNMFTKjLLmkoyBlTe35mLmmYtwv088OLVacLtah2LwtrOp3NeqGjE2bJGSMUMFg6Psqhdpsyuy4OBvxqDZvOGRvRZvT976yyza+vZSlQ3aeDnKYGPhwSFtS0YsWIbqpo0CPfzwJ6nZiA1ojWY5SERY2pyqPHfIjw3fzB6avGoaMQYp+jOTAm1qdC9I7jjdzfnfPUTM6YLb5TYRYh1JGIGepaDnu35F/q+8L+MYugMHEbHBfTpUsWEEELsx8O4uppGb/4j7lehGHi0w1bYNk1jBPgfmZ7S9jWDVFoDqps0eGPLedw3dUC74IbJhYpGXP3BAYT4yHDs+dm91mZiP98eLMD+nBoAAMMAK65Ja5fVZRIT6IUYYwH6z+8Yg7UZRXhmXmoH+8lxtqzRpiL1+TX/3959h0V1pm0Av88UOohI71gAFUUUC2piD5ZkNUUTQ6rZZHc1G01MYrK7yWY3iaZ8MdVUd2OK0cRNTNMYsaIGu6goUhSFoDSRDsOU8/0xzJERUGYcGGbm/l2Xl3LmzOF9GB5n5pn3fd46fHtInxdzEsOw8Zj+33Wqzltmuz27FLtyyvHIjb3h5izH5hMl+Dz9LI79XmV03sz4EKnIBwD9AjywL78CO3P0O6nePSICI6J8MDyyJw6cvdQcT61UoLrSt83L8SbFBqCne8f2CI9p7pObW3K52FXTqMbPzX3/bHUJIwBpRqBh9pTBl3v1Mw4fHBOFG/r54pEvDqGiTl/8vH90ZJurHv46sS+i/NyRPDAA/p7Xv6mTUi7DmodH4XDBJUwfFGS1/6/p2ljssgOGt+lsUE9kGn3O6Lr9bozbs0uxP78CX6Trn+Dvs9LyFiIiun6XlzFe/qClsr5JanR8ZTPwriSXCVLz5bomTas33av3ncPf12dKX2cX12Dr4vFtXuvz5ues8tom1Kk0NjvDxFHUNKrxcdoZAEAfP/0GOB39YG1k714Y2btXm7eFeJu3I6Moinj+h0w0aXW4MdoPo3r7YGe2PkeuXNpmKY1qLRauOYLqRg3WH/kdni5KFFS0no11X1IEFk+JMTrWr0WPLYVMwNwRYc39VUfjia8z8N2RIpwua3uWY3WjGmsP6GewGfp/dUSUr36jgPNVjWhU64vTH+48jepGDXr7uiOpT9uPiS0wbIJwsa4Jl+qa0NPdCesOFmJ/fgXkMgF3Dg9DsLcrfll4A97akoPy2iaj3eFb6unu1O5t5grzcUOYj5tFr0mWx2cdO6DjzC4isxiWMXbn3Rh/yCjCoq8zpBmcoT1dMWNwkHUHRUREZnNpntlV1aCGKIoQBAHrDv6OJo0O/YO8MDD4+vrJXC83JzlUGl2r5UMXa1VGhS4AOF1WB41WB8UVMxsamrT4pXnZPQCU1qgQxWJXt7b+SBGqGtTo7euOzY+PM+ptej2kHRlNKHadKavFu9vysCu3HE4KGV5o7sHkLu0W2jkzuzZlFqO6UV9Iu1SvxqV6fZuLKQMCpGJ0Qrg3/j0zrtV9R/XuBblMgCiKWDS5H/y9Ls8gMhSl2lrSW6vSYNnGU6hp1KCfvwcmmrDZk4+7E7xcFKhu1ODcxXr0dFNi5a58AMCSabEWewytwc1JgRBvVxRVNiCvrBaV59TS/z+PTeyH4OblsQFeLlh222BrDpW6MT7r2Di5XA4R+v/I2LOLyDQKuT5nxG66G2OjWovnvs9Eyx7Bfx7Xh9OlyeoUCgWmT5/uUDv6EFlKbFAPOClkKLzUgK8PFOL2YaH4cp9h5m5Eu8vGuoqbkwKX6tWoa9JCrdVJzznLU3PaPP/khWoMDvVGrUqD97fnIalPL6SeLJEKBQBQUt0oveGn7kcURWn2+P2jIy1aJPH10M8OvFTXsT5bNY1q3P7Bb9Lvz9+mxaK3n74flqer/lp1nbRb6Jr9+sbyj07oCxEiDuRfwku3xiE6wBOZRVX4YOdpPJ0c0+Z9YwI9sevpCXBVylvNiIzya13sOlVcjbPldfjst3NIb96p8dGJfU3aJVAQBET5uuPo7/reeOvOVkCl0WFouDduGmCdvn+W1MffA0WVDVizrwA/HtXvVDl9UCD+OrGvtYdmkxzxtZvjRGqnWi6/4jJGItMYXsB312WMm0+WoLpRgxBvV6xIGYojBZcwd0S4tYdFBABoaGiAp+fVt0Ynotb8vVzw6I0RWL4tH/+3OQfnKupx7mI9ergqMXNIxxpTdyZDk/rF32Sg8FID1j4yCm5OcqkQ8NUfR6JBrcV/9+RjT95F7DtTgUAvF/zx84M49nsV3t9xutU1S2ust3seXdu+/ArkltbCzUmOWy28jNbQ16qyvmObAX2efk4qdL00Kw4pIy+/7vFonh1Yp7L8MsYzZbXYl18BmQDcPTJcmjlkEBfSAyvuHnrVa1x5HwNDofdMWS0A/azOuR/vleJ0Usjwym2DMHOI6T/7yOZi15HCS9IukX+d1HoHTVvU188DaTll+O6IvlfbtLhAvHNXgkkFQTLmaK/dWB2xcaoWu8ix1kVkGnk3341xXfMORLcPDcGQMG88OCbKpqekk/3QaDTYvn27Q+3oQ2QpGo0GoXW58HJRoLxWhQ+ai0NLbx1k1CDeWqKbm16fLqtDk0aHpRuy8FZqLnQiMGNQEEb39cWk/gHSLn1vpGZjyptprRp4z0kMxS3x+uJdaXVjp465pLoRNY3d87ncFnzR3PR7VkIIvFyU1zjbNN5u+llOVQ3Xfnwa1Vr8Z7d+Gd6bd8bjnlHGMx1dFPp/13ZCsevr5tdc46L92i1amSvK1x2CoF8aeaGqAe/vyDOa+TgnMdTsZvKRvfSFtI92nkGDWov+QV4YH+1nkXFbm2GHSwCIDfTE8jlDWi2Zpo5zxNdu/G2xcdoW65v4JpjINIaeXZpu2LPr+O9V2JVbDpkA3DHMNrY0JiKijpHLgEmxl9+QJg8M6Db9GJ+6YpnWwXOXsOlEMQBg0eR+0vGZQ0IglwloVOtQ1aDGgCAvo9nHj0+Jhr+nfmc0w8wutVaHeasOYOpbaa16gpnrVHE1xr2+Hfes3GeR6zmaosoG/Jqpf3zvGWnZJt4A4N08s6sjxa5NmcWoqGtCcA8X3DK49SxHD5fOmdnVqNbifwf1uyHe1Qkz6N2cFIgP9QYAfJx2Bv9tLuh5uej7Uv1lvPnL8gyN3A0eGG39pdCWEh1wudj1+h3xcHVqvTss0dVY/+Mjui7ayxv5sNhFZCJDsau7LGPU6kQ89NkBNKq1cFLon9BnDglBeC/u9kJEZG+m9A/A+gx9E/fHJvW7xtldJ9LXHW/fNQSbT5SgUa3F1lOlAIAxfXuhX8Dl5S9+ns6YkxiKNfv1M2I+mzcCXq4KuCrlSAj3RlAPVwR4NRe7mmd2vZmag23N19t/tgLjLDAD5bnvM9Go1uHo71UorKjnDmkmUGm0WLD6MDQ6ESOifDCgEzZH8HbTF7tqVRqjHnBX0ulEfJ5+FoC+4NTWDJ7LDeotW+z6IaMIF+uaEOLtikkmNIg3xbhoP2QUVuLTPWcB6Jvef3zvsOsuTEW3yMmebkr8Id56u7la2tDwnvjTuN6I9vfEoNCO7QxK1BKLXTau5Zt0NqgnMo3hhZRap7vGmV1ja1YJdmSXSV/LBLAJJ3VbjtTglMjSFAoFxkX74taEEET2csfA4O71Rm7mkBDMHBKCRrUWL/58EluySvDElNaNuZ9KjoVGK+K2oaHwa57F9fwtA6Tb/T31O9KV1qhQWFGPj9LOSLcdus5il04nYl9+BQ6cvSQd++10Oe70YW/LjvpwxxlkFFaih6sS/3dHfKd8D08XJQQBEEX97C5fD+dW54iiiKe/PYbDBZVQygXcObztGe3uhp5dFmxQL4qitHTygdGRnbZMblyMH97emgtAv4viy7fGWWQGVv8gL7wxOx4lNY2YEONvV7OfZDIBz07rb+1h2BVHe+3mWNHaIZn88n9onNlFZBrp08VusBujWquTtos2mJUQIu1ARNSdKJVKzJgxw9rDILJJLfPnzTuHWHcw1+CilOPlWwfh5VsHtXm7j7sTXp/dfpHEsIyxpLoRH+w8bfQh7Tvb8rDn9EV8Pm+EVMToqPzyOsxasafV0rjdeRdx53AWuzqi4GI9VuzIA6BvBN9Zs8jlMgFeLkpUNahRWd92seuXzGL879DvkMsEvDFnCAK8XNq8lre7/nidynLFrl255cgpqYW7kxx3jui8thHxod4I93HDxVoVPn1guFQItoTbh5nX74sciyO+drP+Ozy6LprmdYwyAXazPpuoqxiWMao11p3ZtTWrBDPe2YX9ZyukY04KGR6b2H2WtRC1pNPpUFpaCl03mRVJZEscKX/8m5cxni6rw1fNO8W9OCtOuv3QuUtIP33R5Ot+nn7WqND1RnPBbXduGS7WqlDf5DgNmM311pYcNGl0GNvXFzd3cr84w1LGqoamVrfVN2nw0s8nAQALJvTFH+Lb35HUTal/69qk1UGlsUzBa2XzrK45w8Ms3py/JblMwM+PjcWuJRMRH+bdad+HqD2O9NxjwGKXjVOp9U/mnNVFZDrDxC5DHllDXmkNHv78IHJKauHtpsSH9wzFrqcnYONjNyDS1/3aFyCyAq1Wi/T0dGi1lvt0nchROFL+hPZ0M5rJM2NwEO4eEQ6l/PLr1ryyWpOu2aTR4YeM89LXT94UjZlDguHn6YxL9WoMe2kLxr66HZlFVRDF7tGTs7s5XVaL7zOKAABPT43p9A/MDU3qK+uNZ+Llldbgz18exvmqRn2j9nF9rnod5xYr9Cwxu6vgYj3ScsogCMCDo6Ou+3rX4uWihI+7U6d/H6K2ONJzjwGLXTZO1/wkLuOsLiKTSbsxWrFB/TcHf4dOBEb19sHOJydgalwQwnzcjLZbJiIiskUuSjm+/OMI9PFzx8BgLyy9dRDkMgEf3TtMajaeW2JasWtLVgkq6prg7+mMvJen4dGJ/aCQy3Db0MuNuSvqmnDzu7txy3u7LTYDyJ68szUXOhGY3D8Ag5t3CexMXm0Uu/JKazD1rV1Iy9H3Kn3u5v7X7DelkMuglOlfs1miSf26Q/rNFcb29eVmQER2iMUuG2fofaDgzC4ikxmakFprN0aNVofvDus/WZ03Jgo93Dpv+jwREZE1xAZ6YcsT4/DTo2PRo7noMTE2AG/M0S89zCutaXWfqz0vG3bsm50YatRMfPawMBg++/V00fcAyyyqxvbm3R9JL7ekBj8e1c+MWzS5a9oleLvpZzNVtlh6+kX6OenDxrtHhiN5YGCHrmWY3VV7ncUujVaH/x36HQAwJ7HzenURkfWwQb2NM7wWkLHYRWQyQ4P6rpjZVd2oxrKNp+CqlOPukeHo6++BbadKUV6rgq+HEyZ00lbXRJ1BEAR4enqyVySRGRwxfwRBwJXhGmYw55XWQhRF6eexctcZLN2YhdV/HIWkPr2M7pNTUoO9ZyogE4C7R0a0ut4X80bCzVmOhDBvPP/DCXyx9xzWHynC1LjO7UllC47/XoV//piJE+erIYrA1IGBiAvpml1ADcsYq+r1PbvqVBrpw77P543AjR3clVMQBLgpZahViybP7NLpRHyy6wyCvF3xh/hg/HqiBBeqGtHL3Qk3DQww6VpEtsgRn3tY7LJxgkz/Zp09u4hMZ/hEWIfOz5+Pd57Bmv365ryZ56vwzZ+SpK9vHxZ6eWdIIhugUCgwceJEaw+DyCYxf/QierlDIRNQ16RFUWUDQnu6oUmjw0sbsgAAb6bmIKlPktF9vkg/B0C//C7E27XVNcf285X+nTIqHF/sPYdtp0pRVa92+NnTSzdm4XBBpfT1oildtwmOoUG9YWbXiu15qFFpEOXrjrF9fa92VyMKhQJ+3p4ora+WZnblldZizf4CPDgmEqE9216KuCnzArKLa/HmlhwAgJNcwH92nwEApIyKgLPi6ssnieyBIz738N2VjWtq3kVO7kAVWiJL6ardGBvVWnzVXNgCgMyiKhRW1GNHc5+Ku7hNOtkYnU6Hc+fOOdSOPkSWwvzRU8pliAn0BAAsWH0YtSoNtmSVSLdfuUytplGN7w7rl53dlxR5zevHBnqhr78H1FoRu/PKLTdwG3S2vA7pZy7vennz4CDEBnp12fc3LF+9VK9GYUU9Vu7S74D47LRYk1an6HQ6KET970WtSoPs4hpMXr4T/9mdj//uPtvmfbZnl+LPXx6WCl0A8OcvD+NwQSWc5DLcM4qvwcgxOOJzD4tdNk6t4W6MROYybAbV1MnNa386el5qpiuXCahv0mJ5ag5EEUjq3QtR3HWRbIxWq0VGRoZD7ehDZCnMn8v+PTMOPd2UOPp7Fb7adw6f/XZWuu3khWo8tOoA8kr1Dey/PlCIuiYtevu5Y0zfXu1c0dj45uVxC746jH/9dAIareO8yWtp9T79jLgb+vnivw8k4pXbB3fp9w/qoZ+Fl3qyGIu/OYomrQ5j+/piygDTlg9qtVpoGvW/DyXVKjy57qh026ni6jbv85/mwhoAOCtk6N3iNdc9oyLg7+li0hiIbJUjPvew2GXjDIVZ1rqITHd5N8bOe/EriiJWNb94f3BMFPoH6T/FXn9E36virhFsikpERI5pWERPPDMtFgCwdOMp7MuvMNp0aeupUrzyyynUqTT4cOdpAMDDN/TucM+ZcTGXe0F9uucsdjngDK9GtRbfHNTPiHtwTCQmxgbAw7lrO9lMGRCA8TF+aFTrsP9sBQBg/vg+ZvUO6uOp77P64s8ncbyoSjpeWqNqdW5mUZU0q2/++D5Y/ceR+PDeYXBSyODlosCCCX3MCYeIbASLXTbO8CaduzESmU7ajVHbeQ3q089cxInz1XBRyjB3RBj6t1g24O2m7PDuQ0RERPboD/Eh0u6JAHDb0BCjfly/nS7H6n3nUF7bhIhebrhjWGiHrz080sfoNfLe0/qlfKeKq/FJ2hmoOnlmd3fw49HzqGpQI7SnK8ZFW2czHCeFDCvvS8TdI/VLBoeGe7fafKCjRvkbv2Z7YHQkAKCwoh66FhsOiaKIlzacBAD8IT4YT0+NRWKkD6IDPPHLwhuw4bEb0MvD2awxEJFtYLHLxhn+S+dujESmU8oNM7tML3bVqTQ4d7EOANDQpMWBsxUQRePrVDeqseTbYwCA24eGwtvNCdEBntLtC8b3hYuSTVHJ9giCAD8/P4fa0YfIUpg/xlyd5Hh2Wn/09nPHiEgfLJwcjaeSY+Ci1L9NqW/SYunGUwD0z5umbOjiopTj3bkJ6O2nX7qWfuYiMouqMPWtXXh5Yxa2ZZVaPqBu5su9+iWM94yKsGrbE4VchpdnxeHnv47FZ/NGmPX7LwgCIoP98NCYSADAn8f1wd9n9IdcJkCl0RnN7tqRU4a9ZyrgrJDh6akxRtfp4+eBMJ+2m9kT2StHfO7hboy2TtC/UVbIWLckMpWyefcdU3dj1OlE3PuffTj6exW+nz8GSzdmIf3MRXxyX6JR/4n3tuWhsKIBoT1d8fRU/TKNsf18IQjA4JAeeLD5xRqRrVEoFBg9erS1h0Fkk5g/rd09Mlya9QMAIQkhmDkkGPd/egBpzZu5+Ho4Y2ZCsMnXnjYoCEPCvZG0bBuO/V6FlJX7pNvaWvpmT44WVuLY71VwUsgwJ9H6bRMEQUBcSA+z72/InZE6EY+M64MAL32/rWBvFxRWNKCgoh6BPfTH/rtb36vrnlER7e7SSORIHPG5x6oVkrS0NNxyyy0IDg6GIAj4/vvvr3r+jh07IAhCqz/FxcVdM+BuqIkN6onMZmhQrzZxGcMPR4twuKASWp2Iv645LO1wtLfFTkeV9U3Sp6n/njlQ2omof5AXti8ej7WPJEnLKIlsjVarxalTpxyqySmRpTB/OkYQBNw+NKT538DCSX3hrDBvNnRQD1f09fcAAFQ1qKXjdU2a9u5ik6ob1chs0cfK0DP05kFB8HF3stKoLMeQOxB1UqELACJ89DP3CirqAQAZhZXYlVsOQbi8zJHI0Tnic49V32nV1dUhPj4eK1asMOl+2dnZuHDhgvTH398668+7A8OuMqx1EZnOUCRWm7A7k1Yn4s3UXOnrsxfrpX+fPH95J6DPfjuH+iYt+gd5YUKM8f9Rkb7ucHXi8kWyXTqdDtnZ2Q61fTWRpTB/Om7mkBAcff4mZL6QjHuTIq/rWv83Ox69mgs+/p76Xk21jfZV7PrT54dw87u78VteOQor6vFDhn4znAfHRFl5ZJbRXu4YliSeu1iHnTlleODT/QCAaXGBXK5I1MwRn3usuoxx2rRpmDZtmsn38/f3h7e3t+UHZIMMjRhlDrT2lshSFFKxq+M9u7afKkVBRT2UckG6X083JS7Vq3HifBVKqxvh7qzAp7/pp8+bu9sQERERAT3clBa5zpAwb6Q+MQ5FlxqQerIY72zLQ63KfopdRwouSTPN7/3vfuhEEaIIjO3ri0Gh5i8dtAUDg/Wb/7y7LU86lhDujVduH2ytIRFRN2CTPbuGDBkClUqFuLg4vPDCCxgzZky756pUKqhUl9fjV1frZ16o1Wqo1fppzDKZDHK5HFqt1qjSaTiu0WiMGk/L5XLIZLJ2jxuua6BQ6H/MGo2mQ8eVSiV0Op3RFENBEKBQKFodV6kNyxhh9H1tOSbD8fbGzpgYk6ViarmMUa1WXzOmgvJavLE5GwBwf1IEKuvV+OnYBbw1Jx4PfX4I1Y0ajFi6Ff2DPFFZr0ZELzdMifWVYuDjxJjsJSbDdVrmja3HZI+PE2PqnjG1vJ+9xGTQ3R8nTycBcSFe+O10OQCgqr5Jut1WYzKM/ZNdZ6Svtc0fhns6K7Bkakyr820lJsD48TCcI4qi0fkzBwfgrS25KK/Vv+ebOzwUz06NgZtC/0KvO8d0teO2+jgxpu4Zk+G8lrfZckxK5bU/CLGpYldQUBA+/PBDJCYmQqVSYeXKlRg/fjz27duHoUOHtnmfZcuW4V//+ler45s3b4abm35aa3h4OBISEnDs2DEUFBRI58TExCA2Nhb79+9HWVmZdHzIkCGIiIhAWloaampqpONJSUnw9/fH5s2bjX5hJkyYAFdXV2zcuNFoDNOnT0dDQwO2b98uHVMoFJgxYwbKy8uRnp4uHff09MTEiRNRWFiIjIwM6XiBVv9Jjbqpyej6thyTn58fRo8ejdzcXGRnZzMmxtRpMTXU1wIAzhYUYuPGc1eNKW5wPGZ/sBsldVo4y0WE1Ofh1rho/PMPk3H88AHIoAOaG91nXdDHMTFQjV83/cLHiTHZbUypqal2FxNgf48TY+p+MQmCAJlMZlcx2dLj5Na80+PpgiJs3FhokzEV12pQowYiPIDA/onYeNy4h7EAEelLxkGnMX6P0J1j6sjvXmhoKOrr67Fz506jmF6cORTzVx/GjUE6jJSfxfYtZ20mJlv73WNMthmTk5N+GXdqaqpdxDRz5kxciyC2LJtZkSAIWL9+PWbNmmXS/caNG4fw8HB88cUXbd7e1syusLAwlJeXw8tLP+XVlqvDqVmlmP9VBoaGe+Prh0e0GrstxsQqPmPqqpje356L137NwW0JwXj1trirxvTjsQt4/Ouj8PVwwlcPDUeUr7tRTK9sysbK3Wel+3g4K3DwbxOMNo/g48SYGBNjYkyMiTFZP6afjp7HY2szMDKqJ76cN9zmYsoquoTZH+9HrUqDqF5u0Ir65uwz44Pg7abEV/sL8Z/7huKG6ACbickSv3v1KjWULTpS20NM9vg4MSbGxJldHTRixAjs3r273dudnZ3h7Ozc6rhSqWz1A5LL5ZDLWzeNNvwSdPR4ez94U47LZDLIZK33D7jyuKGvtkImtHkdW4zJoL2xMybGZOrxdsfeXIjSicb3u3LsoijikzR9D64HRkciOsi71difmdYf94+OQnZxDZb9koWltw6Ci3PbOx/xcWJMth6TKIo4duwYBg8eLI3L1mOyx8eJMXXPmLRarVH+2ENMLdnC4+TZvENyXZO21e2dEVNhRT3ScsswJzGszeubElOtSoP5a45K/cbymzfK8fd0xpJp/RHo5YKnp/aHu/Pl+9rq43Tlca1Wi4yMDAwePLjN892c275Gd47pWsdt8XG61nHGZJ2YtFotMjMzjV67Xe18W4jpWmy+2JWRkYGgoCBrD8NqNM1VVe7GSGS6yz27rr4rSfrpizh5oRquSjlSRka0eY5CLkOYjxvCfNwweUCApYdK1K3odDoUFBQgLi6uzRcwRNQ+5o/1eTYXgrpiN8YLVQ244TX9EqEerkrcPDjY7GtpdSL+sf448svrENzDBV//KQm788pR36TFHcNC0aO5iNey0GVPmDtE5nPE/LHq/4S1tbXIy7u8a0Z+fj4yMjLg4+OD8PBwPPvssygqKsLnn38OAHjrrbcQFRWFgQMHorGxEStXrsS2bduwefNma4VgdYZN5ORtVFeJ6OoUcn3eqHVXL3Z93Nz0dXZiKHq6tz1bi4iIiGyDh4v+LVBNJxa7jhRcwqniGqw/UiQdO1teZ/b1tDoR81YdwM6cMggC8OadQxDm44a5I8ItMVwiIrtj1WLXwYMHMWHCBOnrJ554AgBw//33Y9WqVbhw4YJRM7OmpiYsXrwYRUVFcHNzw+DBg7FlyxajazgabfObdDlrXUQmUzRPidRoxXbP2Z1bjh3ZZZAJwLwxUV01NCIiIuokHs0zn2pUnVPs+u7w73jqf8ekXRENKurU7dyjfaIo4ou957AlqxRpOWVwVcrx6h2DMbJ3L0sNl4jILlm12DV+/Hij5mNXWrVqldHXTz/9NJ5++ulOHpVtEUX9m3XO7CIynVKhzxttO/8PNWl0eP6HTADAvaMiEOnr3mVjI+rOZDIZYmJi2uzZQERXx/yxPk8X/XK/Jo0OKo0WzgrLLempblTjue8zWxW6AKC8VtXGPa7uu8NFeP6HE9LXf53UF3+IN38ppC1j7hCZzxHzxz4XdDsQUdAXuxSc2kVkMufmBojadlYxfrn3HM6U18HXwwmLk2O6cGRE3ZtcLkdsbKy1h0Fkk5g/1ufRoqdVncqyxa6v9xeirkmLfv4emD+hD9JyyjE03BvP/XACZTWmFbsq65uwdGOW9HW4jxseGB1psbHaGuYOkfkcMX9YIbFxTWr99GsZ2p8hR0Rtc1Hoi8UX61q/+KyqV+OdbbkAgCemxMDLxfQdQIjslUajwW+//dZqq2oiujbmj/XJZQLcnPQFLks2qVdptPjvHv3uzX+8IQq3JoTizTuHoLefBwDTZ3a9/ms2LtY1oZ+/Bw79YzJ+fmws3Jwcd64Cc4fIfI6YPyx22TjDFGmZwO0YiUzV11+/LPF0WR00V0zvendbLirr1YgO8MCcxFBrDI+o2xJFEWVlZVdtRUBEbWP+dA+X+3aZ3kerPWv2FeBCVSMCvVwwc0iIdNzP0xkAUGZCsevk+Wp8tV/fu/jFWXHo5eHs8B+8MXeIzOeI+cNil40z9BqSy1jsIjJVmLcrnGQimjQ6nL1YDwDQ6UQs+yVL+mT2b9P7c5kwERGRnbH0joxlNSq8s02/y/yjE/vCRXl5aaSvh77YVVmvhrq93glXeGdrLkQRmDE4CKPYjJ6IyGR8B2fjDDO7WOwiMp1MJiDITf/v7OIalNWo8PyPmfho5xnoRCBlZDjGx/hbd5BERERkcYYm9ZZYxqjTiVjy7TFU1DUhNtATcxLDjG73dlVKr9Uv1jYB0M+yOFNWC1EUcbFWhb+vP44DZysAAHvPXMSmE8UQBGDhpH7XPT4iIkfkuIu+7YQINqgnMpdcLkdcqA/OnbqEjccv4PFvMtCk0X/i+uKsONw7KsLKIyTqnuRyOYYMGQK53HJNnYkcBfOne/BsXsZYq7r+Ytc723Kx7VQpnBQyvHXXEDgpjF+Xy2QCfD2cUFKtQlmNCoE9XPDhzjN4ddMpLJjQBxcqG/HdkSKs3leAaXGB2J1XDgC4Y2googM8r3t89oK5Q2Q+R8wfVkhsnGFXY4UDbSFKZCkymQzD+gYBADYcvyAVuh6fHI17RoZbc2hE3ZpMJkNERIRDbV9NZCnMn+7B200/s6sjOySKoohtp0pQWFHf6ra80hq8s1W/oc3Ls+IQG+jV5jUMSxnLa1Uor1Xh1U2nAAArtp/Gd0eKpPN+ySxGTaMG8WHe+PfMONOCsnPMHSLzOWL+cGaXjVNrtAAAgbsxEplMo9FAdvG00bFv/zIawyJ6WmlERLZBo9EgLS0NN954IxQKvpQgMgXzp3uI7KXfpObsxbprnrv+SBGe+OYofNydsOy2QRgV1Qs9motlb2zOgU4EpgwIwOwrli+2ZGhSX1BRjw3HL7S6ffqgQEQHeMLDWYF+AZ5I6t2r1QwxR8fcITKfI+aPY0Rpx7Q6/UwUByrQElmMKIroiTosnRWPf/xwAkl9erHQRdQBoiiipqbGoXb0IbIU5k/3ENFL37TzWsWuWpUGy37Rz8KqqGvCn744BLlMwMM39Mb0QYH4JVPfW+vJm2Kuep3hkT7YkV2Gf/54AgAgCMC7cxOw70wFBoX2wK0JIVCyLclVMXeIzOeI+cNil40zbOgiZ7WLyGyzh4Vg0oBA9HB17C29iYiIHEWUb/PMrvLWSxNbendbLspqVHBRyjA4xBv5F+tQVqPChztP43+HfgcA3DokBDGBV++t9cDoSHy6Jx/lzQ3q/zKuD24eHIybBwdbIBoiIroSi102TtqNkZsxEl2XAC8Xaw+BiIiIukhkc7HrfFUDGtVauChbN23OL6/Df3fnAwBW3D0Uk/oHAAAe+fwgNp8sQXmtCgqZgEWTo6/5/dydFXjltsF4d3seZg8Lxd0j2BuUiKgzcTqQjTNMQlQ40K4KRJYil8uRlJTkULuSEFkCc4fIfMyf7qGXuxM8nBUQReC57zNxqa6p1Tkv/nwSaq2I8TF+mBjrLx1/dGJfAPqliMtuG4Tw5iWR1zJ5QAB+WDAG94yKgEzGT6pNxdwhMp8j5g9ndtk4aTdGrvEnMplMJoO/v/+1TyQiI8wdIvMxf7oHQRDQ012JWpUG6w79jvzyOnz18CipKXzqyRJsO1UKpVzAczcPgCBcLk4NDvXGJ/clwtNFgVG9e1krBIfD3CEynyPmDyskNq7JsBujqLPySIhsj1qtxoYNG6BWq609FCKbwtwhMh/zp/sYEXm5UHXw3CV8nn4WAFBeq8Kz3x0DADw0tjf6+Hm0uu+UAQEsdHUx5g6R+Rwxf1jssnG65t0UOBWayDwajcbaQyCyScwdIvMxf7qHp6fGYNltg/BUsn4nxS1ZJRBFEc98exzltU2IDfTE41P6WXmU1BJzh8h8jpY/LHbZOKlBPYtdREREREQdFuDlgrkjwjEtLhAAcOjcJaz67Sy2ZJXASS7DW3cNgbPCcfrbEBHZExa7bNzl3RhZ7CIiIiIiMlWUrztCvF2h1or4108nAQBPJccgNtDLyiMjIiJzsdhl40Toi1xKfupEZDKFQoEJEyZAoeBeHUSmYO4QmY/50/0IgoAb+vlKX4/q7YOHxkZZcUTUFuYOkfkcMX8cJ1I7xWWMRNfH1dXV2kMgsknMHSLzMX+6n0cn9oVCLiCohyvuTYpgP9xuirlDZD5Hyx/O7LJxam3zbowQrTwSItuj0WiwceNGh2vWSHS9mDtE5mP+dE+hPd3w0qxBWDChL7xclNYeDrWBuUNkPkfMHxa7bJxOp/+bHz4REREREREREbHYZfO0on5Gl4LVLiIiIiIiIiIiFrtsnaFnF/sKEBEREREREREBgiiKDtXsqbq6Gj169EBVVRW8vGx/O+GHPjuArVmlWHbbIMwdEW7t4RDZFFEUodFooFAoIAgsGBN1FHOHyHzMHyLzMHeIzOeI+cOZXTZOp+MyRqLr0dDQYO0hENkk5g6R+Zg/ROZh7hCZz9Hyh8UuG6fWNneoF3XWHQiRDdJoNNi+fbtD7UpCZAnMHSLzMX+IzMPcITKfI+YPi102zjCzS+YgUxGJiIiIiIiIiK6GxS4bx90YiYiIiIiIiIguY7HLxnE3RqLro1AorD0EIpvE3CEyH/OHyDzMHSLzOVr+cDdGG3fb+3twuKASH907DMkDA609HCIiIiIiIiIiq+LMLhunkXp2WXkgRDZIp9OhtLQUOh03eCAyBXOHyHzMHyLzMHeIzOeI+cNil43TNv+yCtyNkchkWq0W6enp0Gq11h4KkU1h7hCZj/lDZB7mDpH5HDF/WOyycdrmGhd7dhERERERERERsdhl8wwzu7gbIxERERERERERi102r7llF+QyPpREphIEAZ6enhAEFouJTMHcITIf84fIPMwdIvM5Yv5wN0YbN+H/diC/vA7r/pyE4ZE+1h4OEREREREREZFVcTqQjdM0N+2SwaFqlkQWodPpcO7cOYfalYTIEpg7ROZj/hCZh7lDZD5HzB8Wu2yc1rCO0bEm6BFZhFarRUZGhkPtSkJkCcwdIvMxf4jMw9whMp8j5g+LXTZO21zkkrNBPRERERERERGRdYtdaWlpuOWWWxAcHAxBEPD9999f8z47duzA0KFD4ezsjL59+2LVqlWdPs7uzDCzi8UuIiIiIiIiIiIrF7vq6uoQHx+PFStWdOj8/Px8zJgxAxMmTEBGRgYWLVqEP/7xj/j11187eaTd1+ViFyfpEZlKEAT4+fk51K4kRJbA3CEyH/OHyDzMHSLzOWL+dJvdGAVBwPr16zFr1qx2z1myZAk2bNiAzMxM6dhdd92FyspKbNq0qUPfx952Y4z/12ZUNaixdfE49PHzsPZwiIiIiIiIiIisSmHtAZgiPT0dkydPNjqWnJyMRYsWtXsflUoFlUolfV1dXQ0AUKvVUKvVAACZTAa5XA6tVmu0O4HhuEajQcuaoFwuh0wma/e44boGCoX+x6zRaDp0XKlUQqfTGTWPEwQBCoWi1fE7hgajpPwS3OQw+r62HJPheHtjZ0yMyVIxqdVq5OTkoE+fPpDL5XYRkz0+Toyp+8XU1NSE06dPo0+fPlAoFHYRkz0+Toype8ak1Wpx+vRpxMbGQhAEu4jJwJ4eJ8bU/WLSarXIz89Hv379jK5hyzFd7ThjYkyWjEmr1SI7O1t632PrMSmVSlyLTRW7iouLERAQYHQsICAA1dXVaGhogKura6v7LFu2DP/6179aHd+8eTPc3NwAAOHh4UhISMCxY8dQUFAgnRMTE4PY2Fjs378fZWVl0vEhQ4YgIiICaWlpqKmpkY4nJSXB398fmzdvNvqFmTBhAlxdXbFx40ajMUyfPh0NDQ3Yvn27dEyhUGDGjBkoLy9Henq6dNzT0xMTJ05EYWEhMjIypOMTfX1RrqtAxYVz2L8rTzpuyzH5+flh9OjRyM3NRXZ2NmNiTJ0aU15eHvLy8uwqJnt8nBhT94wpLy/P7mIC7O9xYkzdLyYAiI6OxqVLl+wmJnt8nBhT94sJ0L//27Vrl93EZI+PE2PqfjEpFAqj9z22HtPMmTNxLTa1jDE6OhoPPvggnn32WenYxo0bMWPGDNTX17dZ7GprZldYWBjKy8ulZYy2XB3WaDTYvHkzkpOTpQqtrcfEKj5j6qqYVCoVNm3ahClTpkCpVNpFTPb4ODGm7hdTY2MjUlNTMWXKFDg5OdlFTPb4ODGm7hmTWq1Gamoqpk+fLo3H1mMysKfHiTF1v5gMuTNt2rRWfYdsNaarHWdMjMmSManVavzyyy/S+x5bj8nuZnYFBgaipKTE6FhJSQm8vLzaLHQBgLOzM5ydnVsdVyqVrX5AcrncqGBkYPgl6Ojx9n7wphyXyWSQyVo3nW/vuFwub/M6th5TW2NnTIzJ1ONXi8lwn5b3s/WY7PFxYkzdKybDcaVSKX0vW4/JHh8nxsSYGBNjau+4LcckCILdxdTeccbEmADLxGQoELdVB7HVmK7FprbwS0pKwtatW42OpaamIikpyUojsj6ZTIbw8PA2f+GI6OqYP0TmYe4QmY/5Q2Qe5g6R+Rwxf6y6jLG2tlZaM5qQkIDly5djwoQJ8PHxQXh4OJ599lkUFRXh888/BwDk5+cjLi4OCxYswLx587Bt2zY89thj2LBhA5KTkzv0Pe1tN0YiIiIiIiIiIrrMqmW9gwcPIiEhAQkJCQCAJ554AgkJCXj++ecBABcuXDBqZhYVFYUNGzYgNTUV8fHxeOONN7By5coOF7rskVarxZEjR4zWzRJRxzB/iMzD3CEyH/OHyDzMHSLzOWL+WLVn1/jx41ttG9vSqlWr2rzPkSNHOnFUtkWn06GgoABxcXFtroUlovYxf4jMw9whMh/zh8g8zB0i8zli/jjOgk0iIiIiIiIiIrJ7NrUboyUYZpJVV1dbeSSWoVarUV9fj+rqarN2KCByZMwfIvMwd4jMx/whMg9zh8h89pg/np6e0i6TbXG4YldNTQ0AICwszMojISIiIiIiIiIiU11r00Gr7sZoDTqdDufPn79mFdBWVFdXIywsDIWFhdxdkshEzB8i8zB3iMzH/CEyD3OHyHz2mD+c2XUFmUyG0NBQaw/D4ry8vOzml5aoqzF/iMzD3CEyH/OHyDzMHSLzOVL+sEE9ERERERERERHZDRa7iIiIiIiIiIjIbrDYZeOcnZ3xz3/+E87OztYeCpHNYf4QmYe5Q2Q+5g+ReZg7ROZzxPxxuAb1RERERERERERkvzizi4iIiIiIiIiI7AaLXUREREREREREZDdY7CIiIiIiIiIiIrvBYhcREREREREREdkNFrts3IoVKxAZGQkXFxeMHDkS+/fvt/aQiKxq2bJlGD58ODw9PeHv749Zs2YhOzvb6JzGxkYsWLAAvXr1goeHB26//XaUlJQYnVNQUIAZM2bAzc0N/v7+eOqpp6DRaLoyFCKreuWVVyAIAhYtWiQdY+4Qta+oqAj33HMPevXqBVdXVwwaNAgHDx6UbhdFEc8//zyCgoLg6uqKyZMnIzc31+gaFRUVSElJgZeXF7y9vfHQQw+htra2q0Mh6jJarRbPPfccoqKi4Orqij59+uDFF19Eyz3UmDtEemlpabjlllsQHBwMQRDw/fffG91uqVw5duwYbrjhBri4uCAsLAyvvfZaZ4fWKVjssmFff/01nnjiCfzzn//E4cOHER8fj+TkZJSWllp7aERWs3PnTixYsAB79+5Famoq1Go1brrpJtTV1UnnPP744/jpp5+wbt067Ny5E+fPn8dtt90m3a7VajFjxgw0NTXht99+w2effYZVq1bh+eeft0ZIRF3uwIED+OijjzB48GCj48wdorZdunQJY8aMgVKpxC+//IKTJ0/ijTfeQM+ePaVzXnvtNbzzzjv48MMPsW/fPri7uyM5ORmNjY3SOSkpKThx4gRSU1Px888/Iy0tDY888og1QiLqEq+++io++OADvPfee8jKysKrr76K1157De+++650DnOHSK+urg7x8fFYsWJFm7dbIleqq6tx0003ISIiAocOHcLrr7+OF154AR9//HGnx2dxItmsESNGiAsWLJC+1mq1YnBwsLhs2TIrjoqoeyktLRUBiDt37hRFURQrKytFpVIprlu3TjonKytLBCCmp6eLoiiKGzduFGUymVhcXCyd88EHH4heXl6iSqXq2gCIulhNTY3Yr18/MTU1VRw3bpy4cOFCURSZO0RXs2TJEnHs2LHt3q7T6cTAwEDx9ddfl45VVlaKzs7O4po1a0RRFMWTJ0+KAMQDBw5I5/zyyy+iIAhiUVFR5w2eyIpmzJghzps3z+jYbbfdJqakpIiiyNwhag8Acf369dLXlsqV999/X+zZs6fR67YlS5aIMTExnRyR5XFml41qamrCoUOHMHnyZOmYTCbD5MmTkZ6ebsWREXUvVVVVAAAfHx8AwKFDh6BWq41yJzY2FuHh4VLupKenY9CgQQgICJDOSU5ORnV1NU6cONGFoyfqegsWLMCMGTOMcgRg7hBdzY8//ojExETMnj0b/v7+SEhIwCeffCLdnp+fj+LiYqP86dGjB0aOHGmUP97e3khMTJTOmTx5MmQyGfbt29d1wRB1odGjR2Pr1q3IyckBABw9ehS7d+/GtGnTADB3iDrKUrmSnp6OG2+8EU5OTtI5ycnJyM7OxqVLl7ooGstQWHsAZJ7y8nJotVqjNxQAEBAQgFOnTllpVETdi06nw6JFizBmzBjExcUBAIqLi+Hk5ARvb2+jcwMCAlBcXCyd01ZuGW4jsldr167F4cOHceDAgVa3MXeI2nfmzBl88MEHeOKJJ/C3v/0NBw4cwGOPPQYnJyfcf//90u9/W/nRMn/8/f2NblcoFPDx8WH+kN165plnUF1djdjYWMjlcmi1Wrz88stISUkBAOYOUQdZKleKi4sRFRXV6hqG21ouz+/uWOwiIru1YMECZGZmYvfu3dYeClG3V1hYiIULFyI1NRUuLi7WHg6RTdHpdEhMTMTSpUsBAAkJCcjMzMSHH36I+++/38qjI+q+vvnmG6xevRpfffUVBg4ciIyMDCxatAjBwcHMHSK6LlzGaKN8fX0hl8tb7YJVUlKCwMBAK42KqPt49NFH8fPPP2P79u0IDQ2VjgcGBqKpqQmVlZVG57fMncDAwDZzy3AbkT06dOgQSktLMXToUCgUCigUCuzcuRPvvPMOFAoFAgICmDtE7QgKCsKAAQOMjvXv3x8FBQUALv/+X+11W2BgYKtNhjQaDSoqKpg/ZLeeeuopPPPMM7jrrrswaNAg3HvvvXj88cexbNkyAMwdoo6yVK7Y02s5FrtslJOTE4YNG4atW7dKx3Q6HbZu3YqkpCQrjozIukRRxKOPPor169dj27ZtrabhDhs2DEql0ih3srOzUVBQIOVOUlISjh8/bvRkkJqaCi8vr1ZvZojsxaRJk3D8+HFkZGRIfxITE5GSkiL9m7lD1LYxY8YgOzvb6FhOTg4iIiIAAFFRUQgMDDTKn+rqauzbt88ofyorK3Ho0CHpnG3btkGn02HkyJFdEAVR16uvr4dMZvyWVC6XQ6fTAWDuEHWUpXIlKSkJaWlpUKvV0jmpqamIiYmxqSWMALgboy1bu3at6OzsLK5atUo8efKk+Mgjj4je3t5Gu2AROZq//OUvYo8ePcQdO3aIFy5ckP7U19dL5/z5z38Ww8PDxW3btokHDx4Uk5KSxKSkJOl2jUYjxsXFiTfddJOYkZEhbtq0SfTz8xOfffZZa4REZDUtd2MUReYOUXv2798vKhQK8eWXXxZzc3PF1atXi25ubuKXX34pnfPKK6+I3t7e4g8//CAeO3ZMnDlzphgVFSU2NDRI50ydOlVMSEgQ9+3bJ+7evVvs16+fOHfuXGuERNQl7r//fjEkJET8+eefxfz8fPG7774TfX19xaefflo6h7lDpFdTUyMeOXJEPHLkiAhAXL58uXjkyBHx3LlzoihaJlcqKyvFgIAA8d577xUzMzPFtWvXim5ubuJHH33U5fFeLxa7bNy7774rhoeHi05OTuKIESPEvXv3WntIRFYFoM0/n376qXROQ0ODOH/+fLFnz56im5ubeOutt4oXLlwwus7Zs2fFadOmia6urqKvr6+4ePFiUa1Wd3E0RNZ1ZbGLuUPUvp9++kmMi4sTnZ2dxdjYWPHjjz82ul2n04nPPfecGBAQIDo7O4uTJk0Ss7Ozjc65ePGiOHfuXNHDw0P08vISH3zwQbGmpqYrwyDqUtXV1eLChQvF8PBw0cXFRezdu7f497//XVSpVNI5zB0ive3bt7f5Puf+++8XRdFyuXL06FFx7NixorOzsxgSEiK+8sorXRWiRQmiKIrWmVNGRERERERERERkWezZRUREREREREREdoPFLiIiIiIiIiIishssdhERERERERERkd1gsYuIiIiIiIiIiOwGi11ERERERERERGQ3WOwiIiIiIiIiIiK7wWIXERERERERERHZDRa7iIiIiIiIiIjIbrDYRURERGQBkZGReOutt6w9DIvZsWMHBEFAZWWltYdCREREZBIWu4iIiIiuobCwEPPmzUNwcDCcnJwQERGBhQsX4uLFi9YemkWMHz8eixYtMjo2evRoXLhwAT169LDOoIiIiIjMxGIXERER0VWcOXMGiYmJyM3NxZo1a5CXl4cPP/wQW7duRVJSEioqKqwyLq1WC51O12nXd3JyQmBgIARB6LTvQURERNQZWOwiIiIiuooFCxbAyckJmzdvxrhx4xAeHo5p06Zhy5YtKCoqwt///nfp3JqaGsydOxfu7u4ICQnBihUrpNtEUcQLL7yA8PBwODs7Izg4GI899ph0u0qlwpNPPomQkBC4u7tj5MiR2LFjh3T7qlWr4O3tjR9//BEDBgyAs7MzVq5cCRcXl1ZLDRcuXIiJEycCAC5evIi5c+ciJCQEbm5uGDRoENasWSOd+8ADD2Dnzp14++23IQgCBEHA2bNn21zG+O2332LgwIFwdnZGZGQk3njjDaPvGxkZiaVLl2LevHnw9PREeHg4Pv74Y+n2pqYmPProowgKCoKLiwsiIiKwbNkysx4XIiIiovaw2EVERETUjoqKCvz666+YP38+XF1djW4LDAxESkoKvv76a4iiCAB4/fXXER8fjyNHjuCZZ57BwoULkZqaCkBfKHrzzTfx0UcfITc3F99//z0GDRokXe/RRx9Feno61q5di2PHjmH27NmYOnUqcnNzpXPq6+vx6quvYuXKlThx4gRSUlLg7e2Nb7/9VjpHq9Xi66+/RkpKCgCgsbERw4YNw4YNG5CZmYlHHnkE9957L/bv3w8AePvtt5GUlISHH34YFy5cwIULFxAWFtbqZ3Ho0CHMmTMHd911F44fP44XXngBzz33HFatWmV03htvvIHExEQcOXIE8+fPx1/+8hdkZ2cDAN555x38+OOP+Oabb5CdnY3Vq1cjMjLSzEeHiIiIqG0Kaw+AiIiIqLvKzc2FKIro379/m7f3798fly5dQllZGQBgzJgxeOaZZwAA0dHR2LNnD958801MmTIFBQUFCAwMxOTJk6FUKhEeHo4RI0YAAAoKCvDpp5+ioKAAwcHBAIAnn3wSmzZtwqeffoqlS5cCANRqNd5//33Ex8dLY7jrrrvw1Vdf4aGHHgIAbN26FZWVlbj99tsBACEhIXjyySel8//617/i119/xTfffIMRI0agR48ecHJygpubGwIDA9v9WSxfvhyTJk3Cc889J8V38uRJvP7663jggQek86ZPn4758+cDAJYsWYI333wT27dvR0xMDAoKCtCvXz+MHTsWgiAgIiKig48EERERUcdxZhcRERHRNRhmbl1LUlJSq6+zsrIAALNnz0ZDQwN69+6Nhx9+GOvXr4dGowEAHD9+HFqtFtHR0fDw8JD+7Ny5E6dPn5au5+TkhMGDBxt9j5SUFOzYsQPnz58HAKxevRozZsyAt7c3AP1MrxdffBGDBg2Cj48PPDw88Ouvv6KgoMCkn0FWVhbGjBljdGzMmDHIzc2FVquVjrUcnyAICAwMRGlpKQD9ksmMjAzExMTgsccew+bNm00aAxEREVFHsNhFRERE1I6+fftCEASpYHWlrKws9OzZE35+fte8VlhYGLKzs/H+++/D1dUV8+fPx4033gi1Wo3a2lrI5XIcOnQIGRkZ0p+srCy8/fbb0jVcXV1bNYwfPnw4+vTpg7Vr16KhoQHr16+XljAC+qWVb7/9NpYsWYLt27cjIyMDycnJaGpqMvOncnVKpdLoa0EQpEb6Q4cORX5+Pl588UU0NDRgzpw5uOOOOzplHEREROS4uIyRiIiIqB29evXClClT8P777+Pxxx836ttVXFyM1atX47777pMKUHv37jW6/969e42WQLq6uuKWW27BLbfcggULFiA2NhbHjx9HQkICtFotSktLccMNN5g8zpSUFKxevRqhoaGQyWSYMWOGdNuePXswc+ZM3HPPPQAAnU6HnJwcDBgwQDrHycnJaHZWW/r37489e/YYHduzZw+io6Mhl8s7PFYvLy/ceeeduPPOO3HHHXdg6tSpqKiogI+PT4evQURERHQ1nNlFREREdBXvvfceVCoVkpOTkZaWhsLCQmzatAlTpkxBSEgIXn75ZencPXv24LXXXkNOTg5WrFiBdevWYeHChQD0uyn+5z//QWZmJs6cOYMvv/wSrq6uiIiIQHR0NFJSUnDffffhu+++Q35+Pvbv349ly5Zhw4YN1xxjSkoKDh8+jJdffhl33HEHnJ2dpdv69euH1NRU/Pbbb8jKysKf/vQnlJSUGN0/MjIS+/btw9mzZ1FeXi7NxGpp8eLF2Lp1K1588UXk5OTgs88+w3vvvWfUD+xali9fjjVr1uDUqVPIycnBunXrEBgYKC25JCIiIrIEFruIiIiIrqJfv344ePAgevfujTlz5qBPnz545JFHMGHCBKSnpxvNSFq8eDEOHjyIhIQEvPTSS1i+fDmSk5MBAN7e3vjkk08wZswYDB48GFu2bMFPP/2EXr16AQA+/fRT3HfffVi8eDFiYmIwa9YsHDhwAOHh4dccY9++fTFixAgcO3bMaAkjAPzjH//A0KFDkZycjPHjxyMwMBCzZs0yOufJJ5+EXC7HgAED4Ofn12Y/r6FDh+Kbb77B2rVrERcXh+effx7//ve/jZrTX4unpydee+01JCYmYvjw4Th79iw2btwImYwvSYmIiMhyBLGjHVeJiIiIiIiIiIi6OX6MRkREREREREREdoPFLiIiIiIiIiIishssdhERERERERERkd1gsYuIiIiIiIiIiOwGi11ERERERERERGQ3WOwiIiIiIiIiIiK7wWIXERERERERERHZDRa7iIiIiIiIiIjIbrDYRUREREREREREdoPFLiIiIiIiIiIishssdhERERERERERkd34f+/VU5YjeR8oAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -608,16 +726,39 @@ } ], "source": [ - "_, ax = plt.subplots(figsize=(12, 3))\n", - "ax.plot(x, nus, label=rf\"Implied $\\nu$\")\n", - "ax.grid(linestyle=\"--\")\n", - "ax.set_title(r\"Implied $\\nu$ parameter by the HGF learning rate\")\n", - "ax.set_ylabel(r\"$\\nu$\")\n", - "ax.set_xlabel(\"Observations\")\n", - "ax.legend()\n", + "_, axs = plt.subplots(figsize=(12, 5), nrows=2, sharex=True)\n", + "axs[0].scatter(x, y, color=\"grey\", alpha=0.6, s=10)\n", + "axs[0].plot(x, mean, label=\"Mean\")\n", + "axs[0].grid(linestyle=\"--\")\n", + "axs[0].set_title(r\"Filtering sufficient statistics using a HGF informed learning rate\")\n", + "axs[0].set_ylabel(\"Mean\")\n", + "axs[0].legend()\n", + "\n", + "axs[1].plot(x, jnp.sqrt(variance), label=\"Standard deviation\")\n", + "axs[1].grid(linestyle=\"--\")\n", + "axs[1].set_xlabel(\"Observations\")\n", + "axs[1].set_ylabel(\"Standard deviation\")\n", + "axs[1].legend()\n", + "\n", "sns.despine()" ] }, + { + "cell_type": "markdown", + "id": "5e7cfb60-a0f2-4e58-a12f-75b048d2bc1e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "```{note}\n", + "In this model, each sufficient statistic is filtered separately, resulting in an implied learning rate for each of them. While this approach has the advantage of dynamically learning the various components of a distribution (see also how this can be done by value and volatility parents in the case of a continuous input {ref}`example_2`), this can result in invalid sufficient statistics (e.g. where the equality $\\xi_1^2 = \\xi_2$ does not hold anymore). Here, and in the rest of the examples, we simply average the learning rate to ensure consistent updating of the predictive distribution. \n", + "```" + ] + }, { "cell_type": "markdown", "id": "a74a4730-0811-4d77-a698-aeb73a80d185", @@ -629,21 +770,15 @@ "tags": [] }, "source": [ - "#### Bivariate normal distribution" + "### Multivariate Gaussian distribution" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "id": "781b08fc-a2c7-4856-a103-beffd7787325", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:04.900348Z", - "iopub.status.busy": "2025-01-10T13:54:04.899801Z", - "iopub.status.idle": "2025-01-10T13:54:04.911618Z", - "shell.execute_reply": "2025-01-10T13:54:04.911004Z" - }, "slideshow": { "slide_type": "" }, @@ -655,38 +790,69 @@ "N = 1000\n", "theta = np.sort(np.sqrt(np.random.rand(N)) * 5 * np.pi)\n", "r_a = -2 * theta - np.pi\n", - "input_data = np.array([np.cos(theta) * r_a, np.sin(theta) * r_a]).T\n", - "input_data = input_data + np.random.randn(N, 2) * 2" + "spiral_data = np.array([np.cos(theta) * r_a, np.sin(theta) * r_a]).T \n", + "spiral_data += np.random.randn(N, 2) * .75 + np.random.randn(N, 2) * 1.5 * np.tile(np.repeat((0, 1, 0, 1, 0, 1, 0, 1, 0, 1), 100), (2, 1)).T" + ] + }, + { + "cell_type": "markdown", + "id": "0d23e4fb-763f-49b0-8951-3307912d73a9", + "metadata": {}, + "source": [ + "#### Generalised Bayesian Filtering: using a fixed $\\nu$" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "ea1ce29f-dd89-44cb-aabd-6d93ecbf128b", + "execution_count": 18, + "id": "95e816bf-ad17-49e1-a6ea-9ac4e0f28939", + "metadata": {}, + "outputs": [], + "source": [ + "bivariate_normal = Network().add_nodes(\n", + " kind=\"ef-state\", \n", + " nus=8.0,\n", + " learning=\"generalised-filtering\", \n", + " distribution=\"multivariate-normal\", \n", + " dimension=2\n", + ").input_data(input_data=spiral_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "17e7a126-7d6f-4467-a019-87cf0352a30a", + "metadata": {}, + "outputs": [], + "source": [ + "# get the sufficient statistics from the first observation to parametrize the model\n", + "means, covariances = jnp.apply_along_axis(\n", + " MultivariateNormal().parameters_from_sufficient_statistics, 1, bivariate_normal.node_trajectories[0][\"xis\"], dimension=2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a6909d46-2fe0-4c5b-8914-2469c65ac701", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:04.914018Z", - "iopub.status.busy": "2025-01-10T13:54:04.913348Z", - "iopub.status.idle": "2025-01-10T13:54:05.570842Z", - "shell.execute_reply": "2025-01-10T13:54:05.568629Z" - }, "slideshow": { "slide_type": "" }, "tags": [] }, - "outputs": [], "source": [ - "# get the sufficient statistics from the first observation to parametrize the model\n", - "sufficient_statistics = jnp.apply_along_axis(\n", - " MultivariateNormal().sufficient_statistics, 1, input_data\n", - ")" + "```{figure} ../images/multivariate_normal.gif\n", + "---\n", + "name: multivariate-normal\n", + "---\n", + "The animation above displays the mean and covariance tracking of a bivariate normal distribution. The ellipse represents the 95% confidence interval of the covariance matrix. We can see that uncertainty is increasing in noisier sections of the distribution trajectory.\n", + "```" ] }, { "cell_type": "markdown", - "id": "edc7c5e6-57bb-4965-b0b1-ab656af86273", + "id": "6f43648b-c1f5-4849-a9b2-59e033878a3d", "metadata": { "editable": true, "slideshow": { @@ -695,26 +861,179 @@ "tags": [] }, "source": [ - "Filtering the sufficient statistics of a two dimensional multivariate normal distribution requires tracking the values of 5 parameters in paralell. Our model therefore consist in 5 independent two-level continuous HGF." + "::::{note} Code to create this animation ๐Ÿ‘ˆ\n", + ":class: dropdown\n", + "\n", + ":::{code} python\n", + "\n", + "from matplotlib.animation import FuncAnimation\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import chi2\n", + "from matplotlib.patches import Ellipse\n", + "from pyhgf.model import Network\n", + "import jax.numpy as jnp\n", + "from pyhgf.math import MultivariateNormal\n", + "\n", + "# simulate an ordered spiral data set\n", + "N = 1000\n", + "theta = np.sort(np.sqrt(np.random.rand(N)) * 5 * np.pi)\n", + "r_a = -2 * theta - np.pi\n", + "spiral_data = np.array([np.cos(theta) * r_a, np.sin(theta) * r_a]).T \n", + "spiral_data += np.random.randn(N, 2) * .75 + np.random.randn(N, 2) * 1.5 * np.tile(np.repeat((0, 1, 0, 1, 0, 1, 0, 1, 0, 1), 100), (2, 1)).T\n", + "\n", + "bivariate_normal = Network().add_nodes(\n", + " kind=\"ef-state\", \n", + " nus=8.0,\n", + " learning=\"generalised-filtering\", \n", + " distribution=\"multivariate-normal\", \n", + " dimension=2\n", + ").input_data(input_data=spiral_data)\n", + "\n", + "# get the sufficient statistics from the first observation to parametrize the model\n", + "means, covariances = jnp.apply_along_axis(\n", + " MultivariateNormal().parameters_from_sufficient_statistics, 1, bivariate_normal.node_trajectories[0][\"xis\"], dimension=2\n", + ")\n", + "\n", + "def plot_confidence_intervals(mean, cov, confidence_level=.95, plot=True):\n", + " \n", + " # Chi-squared value for the confidence level\n", + " chi2_val = chi2.ppf(confidence_level, df=2)\n", + " scaling_factor = np.sqrt(chi2_val)\n", + " \n", + " # Eigenvalues and eigenvectors of the covariance matrix\n", + " eigenvalues, eigenvectors = np.linalg.eigh(cov)\n", + " \n", + " # Calculate the ellipse parameters\n", + " width = 2 * scaling_factor * np.sqrt(eigenvalues[1]) # Semi-major axis\n", + " height = 2 * scaling_factor * np.sqrt(eigenvalues[0]) # Semi-minor axis\n", + " angle = np.degrees(np.arctan2(eigenvectors[1, 1], eigenvectors[0, 1])) # Use the largest eigenvalue's eigenvector\n", + " \n", + " # Return the ellipse\n", + " if plot:\n", + " return Ellipse(xy=mean, width=width, height=height, angle=angle, alpha=.2, color=\"#c44e52\")\n", + " else:\n", + " return mean, width, height, angle\n", + "\n", + "fig, ax = plt.subplots(figsize=(5, 5))\n", + "scat = ax.scatter(\n", + " spiral_data[0, 0],\n", + " spiral_data[0, 1],\n", + " edgecolor=\"k\",\n", + " alpha=0.4,\n", + " s=10\n", + ")\n", + "scat2 = ax.scatter(\n", + " means[0, 1],\n", + " means[0, 0],\n", + " edgecolor=\"#c44e52\",\n", + " s=25\n", + ")\n", + "plot = ax.plot(\n", + " means[0, 1],\n", + " means[0, 0],\n", + " color=\"#c44e52\",\n", + " linestyle=\"--\",\n", + " label=\"Belief trajectory\"\n", + ")[0]\n", + "\n", + "# Confidence intervals\n", + "ellipse = plot_confidence_intervals(means[0, :], covariances[0, :])\n", + "ax.add_patch(ellipse)\n", + "\n", + "ax.grid(linestyle=\"--\")\n", + "ax.set(\n", + " xlim=[-35, 35],\n", + " ylim=[-35, 35],\n", + " xlabel=r\"$x_1$\",\n", + " ylabel=r\"$x_2$\",\n", + " title=f\"Bayesian Filtering \\n of a Bivariate Stochastic Process\",\n", + ")\n", + "plt.tight_layout()\n", + "\n", + "\n", + "def update(frame):\n", + " frame *= 3\n", + " # update the scatter plot\n", + " data = np.stack([spiral_data[:frame, 0], spiral_data[:frame, 1]]).T\n", + " scat.set_offsets(data)\n", + "\n", + " data2 = np.stack([means[frame, 0], means[frame, 1]]).T\n", + " scat2.set_offsets(data2)\n", + "\n", + " # update the belief trajectory\n", + " plot.set_ydata(means[:frame, 1])\n", + " plot.set_xdata(means[:frame, 0])\n", + "\n", + " # update the confidence intervals\n", + " mean, width, height, angle = plot_confidence_intervals(\n", + " means[frame, :], covariances[frame, :], plot=False\n", + " )\n", + " ellipse.set_center(mean)\n", + " ellipse.width = width\n", + " ellipse.height = height\n", + " ellipse.angle = angle\n", + "\n", + " return scat, scat2, plot, ellipse\n", + "\n", + "ani = FuncAnimation(fig=fig, func=update, frames=333, interval=100)\n", + "ani.save(\"anim.gif\")\n", + ":::\n", + "\n", + "::::" ] }, { - "cell_type": "code", - "execution_count": 16, - "id": "d4ac6695-29c0-4ed4-b079-ddf073bac272", + "cell_type": "markdown", + "id": "32b98e41-fba5-4a5e-a85e-2de6cb39fee5", + "metadata": {}, + "source": [ + "#### Using a dynamically adapted $\\nu$ through a collection of Hierarchical Gaussian Filters" + ] + }, + { + "cell_type": "markdown", + "id": "edc7c5e6-57bb-4965-b0b1-ab656af86273", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:05.574050Z", - "iopub.status.busy": "2025-01-10T13:54:05.573318Z", - "iopub.status.idle": "2025-01-10T13:54:05.665274Z", - "shell.execute_reply": "2025-01-10T13:54:05.664334Z" - }, "slideshow": { "slide_type": "" }, "tags": [] }, + "source": [ + "Filtering the sufficient statistics of a two-dimensional multivariate normal distribution requires tracking the values of 5 parameters in parallel. Our model therefore consists of 5 independent two-level continuous HGF." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c16026ce-fab5-4753-82ef-39eb7a136fa8", + "metadata": {}, + "outputs": [], + "source": [ + "bivariate_hgf = Network().add_nodes(\n", + " kind=\"ef-state\", \n", + " learning=\"hgf-2\", \n", + " distribution=\"multivariate-normal\", \n", + " dimension=2\n", + ")\n", + "\n", + "# adapting prior parameter values to the sufficient statistics\n", + "# covariances statistics will have greater variability and amplitudes\n", + "for node_idx in [2, 5, 8, 11, 14]:\n", + " bivariate_hgf.attributes[node_idx][\"tonic_volatility\"] = -2.0\n", + "for node_idx in [1, 4, 7, 10, 13]:\n", + " bivariate_hgf.attributes[node_idx][\"precision\"] = .01\n", + "for node_idx in [9, 12, 15]:\n", + " bivariate_hgf.attributes[node_idx][\"mean\"] = 10.0" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "39a4137d-e549-4b21-ab25-1ae2abfe9475", + "metadata": {}, "outputs": [ { "data": { @@ -725,293 +1044,234 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "hgf-nodes\n", - "\n", + "\n", "\n", "\n", "x_0\n", - "\n", - "0\n", + "\n", + "EF-0\n", "\n", "\n", "\n", "x_1\n", - "\n", - "1\n", + "\n", + "1\n", + "\n", + "\n", + "\n", + "x_1->x_0\n", + "\n", + "\n", "\n", "\n", "\n", "x_2\n", - "\n", - "2\n", + "\n", + "2\n", + "\n", + "\n", + "\n", + "x_2->x_1\n", + "\n", + "\n", "\n", "\n", "\n", "x_3\n", - "\n", - "3\n", + "\n", + "3\n", + "\n", + "\n", + "\n", + "x_3->x_2\n", + "\n", + "\n", "\n", "\n", "\n", "x_4\n", - "\n", - "4\n", + "\n", + "4\n", + "\n", + "\n", + "\n", + "x_4->x_0\n", + "\n", + "\n", "\n", "\n", "\n", "x_5\n", - "\n", - "5\n", + "\n", + "5\n", "\n", - "\n", - "\n", - "x_5->x_0\n", - "\n", - "\n", + "\n", + "\n", + "x_5->x_4\n", + "\n", + "\n", "\n", "\n", "\n", "x_6\n", - "\n", - "6\n", + "\n", + "6\n", "\n", - "\n", - "\n", - "x_6->x_1\n", - "\n", - "\n", + "\n", + "\n", + "x_6->x_5\n", + "\n", + "\n", "\n", "\n", "\n", "x_7\n", - "\n", - "7\n", + "\n", + "7\n", "\n", - "\n", - "\n", - "x_7->x_2\n", - "\n", - "\n", + "\n", + "\n", + "x_7->x_0\n", + "\n", + "\n", "\n", "\n", "\n", "x_8\n", - "\n", - "8\n", + "\n", + "8\n", "\n", - "\n", - "\n", - "x_8->x_3\n", - "\n", - "\n", + "\n", + "\n", + "x_8->x_7\n", + "\n", + "\n", "\n", "\n", "\n", "x_9\n", - "\n", - "9\n", + "\n", + "9\n", "\n", - "\n", - "\n", - "x_9->x_4\n", - "\n", - "\n", + "\n", + "\n", + "x_9->x_8\n", + "\n", + "\n", "\n", "\n", "\n", "x_10\n", - "\n", - "10\n", + "\n", + "10\n", "\n", - "\n", - "\n", - "x_10->x_5\n", - "\n", - "\n", + "\n", + "\n", + "x_10->x_0\n", + "\n", + "\n", "\n", "\n", "\n", "x_11\n", - "\n", - "11\n", + "\n", + "11\n", "\n", - "\n", - "\n", - "x_11->x_6\n", - "\n", - "\n", + "\n", + "\n", + "x_11->x_10\n", + "\n", + "\n", "\n", "\n", "\n", "x_12\n", - "\n", - "12\n", + "\n", + "12\n", "\n", - "\n", - "\n", - "x_12->x_7\n", - "\n", - "\n", + "\n", + "\n", + "x_12->x_11\n", + "\n", + "\n", "\n", "\n", "\n", "x_13\n", - "\n", - "13\n", + "\n", + "13\n", "\n", - "\n", - "\n", - "x_13->x_8\n", - "\n", - "\n", + "\n", + "\n", + "x_13->x_0\n", + "\n", + "\n", "\n", "\n", "\n", "x_14\n", - "\n", - "14\n", + "\n", + "14\n", "\n", - "\n", - "\n", - "x_14->x_9\n", - "\n", - "\n", + "\n", + "\n", + "x_14->x_13\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "x_15\n", + "\n", + "15\n", + "\n", + "\n", + "\n", + "x_15->x_14\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "bivariate_hgf = Network().add_nodes(precision=0.1, n_nodes=5)\n", - "\n", - "for i in range(5):\n", - " bivariate_hgf.add_nodes(\n", - " value_children=i,\n", - " node_parameters={\n", - " \"tonic_volatility\": -6.0,\n", - " \"mean\": sufficient_statistics[0][i],\n", - " },\n", - " )\n", - "\n", - "for i in range(5):\n", - " bivariate_hgf.add_nodes(\n", - " volatility_children=[i + 5],\n", - " node_parameters={\"mean\": 10.0, \"tonic_volatility\": -2},\n", - " )\n", "bivariate_hgf.plot_network()" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "5afba8ab-ee69-4747-9fa0-4c12e4d09d8f", - "metadata": { - "editable": true, - "execution": { - "iopub.execute_input": "2025-01-10T13:54:05.667871Z", - "iopub.status.busy": "2025-01-10T13:54:05.667323Z", - "iopub.status.idle": "2025-01-10T13:54:05.673401Z", - "shell.execute_reply": "2025-01-10T13:54:05.672425Z" - }, - "slideshow": { - "slide_type": "" - }, - "tags": [ - "hide-cell" - ] - }, + "execution_count": 22, + "id": "0f53579b-e772-45d2-ad5f-27f5b93ecc49", + "metadata": {}, "outputs": [], "source": [ - "# run this code to create the animation\n", - "\n", - "# fig, ax = plt.subplots(figsize=(5, 5))\n", - "# scat = ax.scatter(\n", - "# input_data[0, 0],\n", - "# input_data[0, 1],\n", - "# edgecolor=\"k\",\n", - "# alpha=0.4,\n", - "# s=10\n", - "# )\n", - "# scat2 = ax.scatter(\n", - "# means[0, 1],\n", - "# means[0, 0],\n", - "# edgecolor=\"#c44e52\",\n", - "# s=25\n", - "# )\n", - "# plot = ax.plot(\n", - "# means[0, 1],\n", - "# means[0, 0],\n", - "# color=\"#c44e52\",\n", - "# linestyle=\"--\",\n", - "# label=\"Belief trajectory\"\n", - "# )[0]\n", - "# ax.grid(linestyle=\"--\")\n", - "# ax.set(\n", - "# xlim=[-35, 35],\n", - "# ylim=[-35, 35],\n", - "# xlabel=r\"$x_1$\",\n", - "# ylabel=r\"$x_2$\",\n", - "# title=r\"Filtering a bivariate stochastic process\",\n", - "# )\n", - "# plt.tight_layout()\n", - "#\n", - "#\n", - "# def update(frame):\n", - "# # update the scatter plot\n", - "# data = np.stack([input_data[:frame, 0], input_data[:frame, 1]]).T\n", - "# scat.set_offsets(data)\n", - "#\n", - "# data2 = np.stack([means[frame, 0], means[frame, 1]]).T\n", - "# scat2.set_offsets(data2)\n", - "#\n", - "# # update the belief trajectory\n", - "# plot.set_ydata(means[:frame, 1])\n", - "# plot.set_xdata(means[:frame, 0])\n", - "# return scat, scat2, plot\n", - "#\n", - "# ani = animation.FuncAnimation(fig=fig, func=update, frames=1000, interval=30)\n", - "# ani.save(\"anim.gif\")" + "bivariate_hgf.input_data(input_data=spiral_data);" ] }, { - "cell_type": "markdown", - "id": "a6909d46-2fe0-4c5b-8914-2469c65ac701", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "![multivariate_hgf](../images/multivariate_hgf.gif)" - ] - }, - { - "cell_type": "markdown", - "id": "fa3af903-9f8d-4d4f-9e8a-e2ae3a67d429", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "cell_type": "code", + "execution_count": 23, + "id": "c24aedfa-00f3-4f8a-b437-2d599f27406b", + "metadata": {}, + "outputs": [], "source": [ - "```{note}\n", - "The animation above displays the mean tracking of a bivariate normal distribution. This is equivalent to tracking the mean of the x and y axis using two separate HGFs. However, the generalized filtering process does more than that under the hood by tracking the whole sufficient statistic vector, which incorporates information about the covariance of the implied multivariate distribution. The full visualization of this distribution requires derivating the posterior predictive distribution of the multivariate normal, as parametrized by the vectors $\\nu$ and $\\xi$.\n", - "```" + "# get the sufficient statistics\n", + "xis = jnp.apply_along_axis(\n", + " MultivariateNormal().sufficient_statistics_from_observations, 1, spiral_data,\n", + ")" ] }, { @@ -1024,22 +1284,15 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 24, "id": "98fb7497-1284-42cf-930e-5c318cbacb6b", - "metadata": { - "execution": { - "iopub.execute_input": "2025-01-10T13:54:05.677157Z", - "iopub.status.busy": "2025-01-10T13:54:05.676059Z", - "iopub.status.idle": "2025-01-10T13:54:05.694163Z", - "shell.execute_reply": "2025-01-10T13:54:05.693269Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Fri Jan 10 2025\n", + "Last updated: Tue Jan 14 2025\n", "\n", "Python implementation: CPython\n", "Python version : 3.12.3\n", @@ -1049,13 +1302,13 @@ "jax : 0.4.31\n", "jaxlib: 0.4.31\n", "\n", - "matplotlib: 3.10.0\n", - "pyhgf : 0.2.1.post4.dev0+d49aafe9\n", - "seaborn : 0.13.2\n", "numpy : 1.26.0\n", "sys : 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:38:13) [GCC 12.3.0]\n", - "IPython : 8.31.0\n", + "pyhgf : 0.2.1.post4.dev0+d49aafe9\n", "jax : 0.4.31\n", + "IPython : 8.31.0\n", + "seaborn : 0.13.2\n", + "matplotlib: 3.10.0\n", "\n", "Watermark: 2.5.0\n", "\n" @@ -1066,19 +1319,11 @@ "%load_ext watermark\n", "%watermark -n -u -v -iv -w -p pyhgf,jax,jaxlib" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "65bcd439-2583-4c2e-8db0-b845cc6ba201", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "pyhgf_dev", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/pyhgf/math.py b/pyhgf/math.py index 80859d665..e62c4be18 100644 --- a/pyhgf/math.py +++ b/pyhgf/math.py @@ -18,15 +18,67 @@ class MultivariateNormal: """ @staticmethod - def sufficient_statistics(x: ArrayLike) -> Array: - """Compute the sufficient statistics for the multivariate normal.""" + def sufficient_statistics_from_observations(x: ArrayLike) -> Array: + """Compute the expected sufficient statistics from a single observation.""" return jnp.hstack([x, jnp.outer(x, x)[jnp.tril_indices(x.shape[0])]]) + @staticmethod + def sufficient_statistics_from_parameters( + mean: ArrayLike, covariance: ArrayLike + ) -> Array: + """Compute the expected sufficient statistics from distribution parameter. + + Parameters + ---------- + mean : + Mean of the Gaussian distribution. + covariance : + Variance of the Gaussian distribution. + + Returns + ------- + xis : + The sufficient statistics. + + """ + return jnp.append( + mean, + (covariance + jnp.outer(mean, mean))[jnp.tril_indices(covariance.shape[0])], + ) + @staticmethod def base_measure(k: int) -> float: """Compute the base measures for the multivariate normal.""" return (2 * jnp.pi) ** (-k / 2) + @staticmethod + def parameters_from_sufficient_statistics( + xis: ArrayLike, dimension: int + ) -> Tuple[Array, Array]: + """Compute the distribution parameters from the sufficient statistics. + + Parameters + ---------- + xis : + The sufficient statistics. + dimension : + The dimension of the multivariate normal distribution. + + Returns + ------- + means, covariance : + The parameters of the distribution (mean and covariance). + + """ + mean = xis[:dimension] + covariance = jnp.zeros((dimension, dimension)) + covariance = covariance.at[jnp.tril_indices(dimension)].set( + xis[dimension:] - jnp.outer(mean, mean)[jnp.tril_indices(dimension)] + ) + covariance += covariance.T - jnp.diag(covariance.diagonal()) + + return mean, covariance + class Normal: """The univariate normal as an exponential family distribution. @@ -38,14 +90,28 @@ class Normal: """ @staticmethod - def sufficient_statistics(x: float) -> Array: - """Sufficient statistics for the univariate normal.""" + def sufficient_statistics_from_observations(x: float) -> Array: + """Compute the expected sufficient statistics from a single observation.""" return jnp.array([x, x**2]) @staticmethod - def expected_sufficient_statistics(mu: float, sigma) -> Array: - """Compute expected sufficient statistics from the mean and std.""" - return jnp.array([mu, mu**2 + sigma**2]) + def sufficient_statistics_from_parameters(mean: float, variance: float) -> Array: + """Compute the expected sufficient statistics from distribution parameter. + + Parameters + ---------- + mean : + Mean of the Gaussian distribution. + variance : + Variance of the Gaussian distribution. + + Returns + ------- + xis : + The sufficient statistics. + + """ + return jnp.array([mean, mean**2 + variance]) @staticmethod def base_measure() -> float: @@ -53,13 +119,13 @@ def base_measure() -> float: return 1 / (jnp.sqrt(2 * jnp.pi)) @staticmethod - def parameters(xis: ArrayLike) -> Tuple[float, float]: - """Get parameters from the expected sufficient statistics. + def parameters_from_sufficient_statistics(xis: ArrayLike) -> Tuple[float, float]: + """Compute the distribution parameters from the sufficient statistics. Parameters ---------- xis : - The expected sufficient statistics. + The sufficient statistics. Returns ------- @@ -69,6 +135,7 @@ def parameters(xis: ArrayLike) -> Tuple[float, float]: """ mean = xis[0] variance = xis[1] - (mean**2) + return mean, variance diff --git a/pyhgf/model/add_nodes.py b/pyhgf/model/add_nodes.py index f55c55cdf..656d383e9 100644 --- a/pyhgf/model/add_nodes.py +++ b/pyhgf/model/add_nodes.py @@ -128,7 +128,6 @@ def add_ef_state( "learning": "generalised-filtering", "nus": 3.0, "xis": jnp.array([0.0, 1.0]), - "mean": 0.0, "observed": 1, } @@ -136,6 +135,20 @@ def add_ef_state( node_parameters, default_parameters, additional_parameters ) + # the size of the sufficient statistics vector of a multivariate normal + # distribution is given by d + d(d+1) / 2, where d is the dimension + d = node_parameters["dimension"] + n_suff_stats = d + d * (d + 1) // 2 + node_parameters["mean"] = jnp.zeros(d) if d > 1 else 0.0 + node_parameters["observation_ss"] = jnp.zeros(n_suff_stats) + if node_parameters["distribution"] == "normal": + node_parameters["xis"] = jnp.array([0.0, 1.0]) + elif node_parameters["distribution"] == "multivariate-normal": + node_parameters["xis"] = ( + MultivariateNormal.sufficient_statistics_from_parameters( + mean=jnp.zeros(d), covariance=jnp.identity(d) + ) + ) network = insert_nodes( network=network, n_nodes=n_nodes, @@ -147,21 +160,40 @@ def add_ef_state( # loop over the indexes of nodes created in the previous step for node_idx in range(network.n_nodes - 1, network.n_nodes - n_nodes - 1, -1): - if network.attributes[node_idx]["learning"] == "generalised-filtering": - - # create the sufficient statistic function and store in the side parameters - if network.attributes[node_idx]["distribution"] == "normal": - sufficient_stats_fn = Normal().sufficient_statistics - elif network.attributes[node_idx]["distribution"] == "multivariate-normal": - sufficient_stats_fn = MultivariateNormal().sufficient_statistics - - network.attributes[node_idx].pop("distribution") - network.attributes[node_idx].pop("learning") + # create the sufficient statistic function and store in the side parameters + if network.attributes[node_idx]["distribution"] == "normal": + sufficient_stats_fn = Normal().sufficient_statistics_from_observations + elif network.attributes[node_idx]["distribution"] == "multivariate-normal": + sufficient_stats_fn = ( + MultivariateNormal().sufficient_statistics_from_observations + ) + else: + raise ValueError( + "The distribution should be either 'normal' or 'multivariate-normal'." + ) - # add the sufficient statistics function in the side parameters - network.additional_parameters.setdefault(node_idx, {})[ - "sufficient_stats_fn" - ] = sufficient_stats_fn + # add the sufficient statistics function in the side parameters + network.additional_parameters.setdefault(node_idx, {})[ + "sufficient_stats_fn" + ] = sufficient_stats_fn + + if "hgf" in network.attributes[node_idx]["learning"]: + + # create a collection of continuous state nodes + # to track the sufficient statistics of the implied distribution + for i in range(n_suff_stats): + network.add_nodes(value_children=node_idx) + network.add_nodes(value_children=network.n_nodes - 1) + if ( + "-2" in network.attributes[node_idx]["learning"] + or "-3" in network.attributes[node_idx]["learning"] + ): + network.add_nodes(volatility_children=network.n_nodes - 1) + if "-3" in network.attributes[node_idx]["learning"]: + network.add_nodes(volatility_children=network.n_nodes - 1) + + network.attributes[node_idx].pop("distribution") + network.attributes[node_idx].pop("learning") return network diff --git a/pyhgf/updates/posterior/exponential.py b/pyhgf/updates/posterior/exponential.py new file mode 100644 index 000000000..4418dcc20 --- /dev/null +++ b/pyhgf/updates/posterior/exponential.py @@ -0,0 +1,82 @@ +# Author: Nicolas Legrand + +from functools import partial +from typing import Dict + +import jax.numpy as jnp +from jax import jit + +from pyhgf.typing import Attributes, Edges + + +@partial(jit, static_argnames=("edges", "node_idx", "sufficient_stats_fn")) +def posterior_update_exponential_family_dynamic( + attributes: Dict, edges: Edges, node_idx: int, **args +) -> Attributes: + r"""Update the hyperparameters of an ef state node using HGF-implied learning rates. + + This posterior update step is usually moved at the end of the update sequence as we + have to wait that all parent nodes tracking the expected sufficient statistics have + been updated, and therefore being able to infer the implied learning rate to update + the :math:`nu` vector. The new impled :math:`nu` is given by a ratio: + + .. math:: + \nu \leftarrow \frac{\delta}{\Delta} + + Where :math:`delta` is the prediction error (the new sufficient statistics compared + to the expected sufficient statistic), and :math:`Delta` is the differential of + expectation (what was expected before compared to what is expected after). This + ratio quantifies how much the model is learning from new observations. + + Parameters + ---------- + attributes : + The attributes of the probabilistic nodes. + edges : + The edges of the probabilistic nodes as a tuple of + :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as the node + number. For each node, the index lists the value and volatility parents and + children. + node_idx : + Pointer to the value parent node that will be updated. + + Returns + ------- + attributes : + The updated attributes of the probabilistic nodes. + + References + ---------- + .. [1] Mathys, C., & Weber, L. (2020). Hierarchical Gaussian Filtering of Sufficient + Statistic Time Series for Active Inference. In Active Inference (pp. 52โ€“58). + Springer International Publishing. https://doi.org/10.1007/978-3-030-64919-7_7 + + """ + # prediction error - expectation differential + pe, ed = [], [] + for parent_idx in edges[node_idx].value_parents or []: + + pe.append( + attributes[parent_idx]["mean"] - attributes[parent_idx]["expected_mean"] + ) + + parent_parent_idx = edges[parent_idx].value_parents[0] + ed.append( + attributes[parent_parent_idx]["mean"] + - attributes[parent_parent_idx]["expected_mean"] + ) + + # implied learning rate + attributes[node_idx]["nus"] = (jnp.array(pe) / jnp.array(ed)).mean() + + # apply the Bayesian update using fixed learning rates nus + xis = attributes[node_idx]["xis"] + (1 / (1 + attributes[node_idx]["nus"])) * ( + attributes[node_idx]["observation_ss"] - attributes[node_idx]["xis"] + ) + + # blank update in the case of unobserved value + attributes[node_idx]["xis"] = jnp.where( + attributes[node_idx]["observed"], xis, attributes[node_idx]["xis"] + ) + + return attributes diff --git a/pyhgf/updates/prediction/dirichlet.py b/pyhgf/updates/prediction/dirichlet.py index 298fbd2b9..8658c2fc0 100644 --- a/pyhgf/updates/prediction/dirichlet.py +++ b/pyhgf/updates/prediction/dirichlet.py @@ -44,7 +44,9 @@ def dirichlet_node_prediction( if value_parent_idxs is not None: parameters = jnp.array( [ - Normal().parameters(xis=attributes[parent_idx]["xis"]) + Normal().parameters_from_sufficient_statistics( + xis=attributes[parent_idx]["xis"] + ) for parent_idx in value_parent_idxs ] ) diff --git a/pyhgf/updates/prediction_error/dirichlet.py b/pyhgf/updates/prediction_error/dirichlet.py index 395bd7d5b..b6ed3dda1 100644 --- a/pyhgf/updates/prediction_error/dirichlet.py +++ b/pyhgf/updates/prediction_error/dirichlet.py @@ -205,8 +205,8 @@ def create_cluster(operands: Tuple, edges: Edges, node_idx: int) -> Attributes: # initialize the new cluster using candidate values attributes[value_parent_idx]["xis"] = jnp.where( cluster_idx == i, - Normal().expected_sufficient_statistics( - mu=candidate_mean, sigma=candidate_sigma + Normal().sufficient_statistics_from_parameters( + mean=candidate_mean, variance=candidate_sigma**2 ), attributes[value_parent_idx]["xis"], ) diff --git a/pyhgf/updates/prediction_error/exponential.py b/pyhgf/updates/prediction_error/exponential.py index a84b31b53..a7d240d35 100644 --- a/pyhgf/updates/prediction_error/exponential.py +++ b/pyhgf/updates/prediction_error/exponential.py @@ -10,7 +10,7 @@ @partial(jit, static_argnames=("edges", "node_idx", "sufficient_stats_fn")) -def prediction_error_update_exponential_family( +def prediction_error_update_exponential_family_fixed( attributes: Dict, edges: Edges, node_idx: int, sufficient_stats_fn: Callable, **args ) -> Attributes: r"""Update the parameters of an exponential family distribution. @@ -49,10 +49,14 @@ def prediction_error_update_exponential_family( Springer International Publishing. https://doi.org/10.1007/978-3-030-64919-7_7 """ - # update the hyperparameter vectors + # retrieve the expected sufficient statistics from new observations + attributes[node_idx]["observation_ss"] = sufficient_stats_fn( + x=attributes[node_idx]["mean"] + ) + + # apply the Bayesian update using fixed learning rates nus xis = attributes[node_idx]["xis"] + (1 / (1 + attributes[node_idx]["nus"])) * ( - sufficient_stats_fn(x=attributes[node_idx]["mean"]) - - attributes[node_idx]["xis"] + attributes[node_idx]["observation_ss"] - attributes[node_idx]["xis"] ) # blank update in the case of unobserved value @@ -61,3 +65,64 @@ def prediction_error_update_exponential_family( ) return attributes + + +@partial(jit, static_argnames=("edges", "node_idx", "sufficient_stats_fn")) +def prediction_error_update_exponential_family_dynamic( + attributes: Dict, edges: Edges, node_idx: int, sufficient_stats_fn: Callable, **args +) -> Attributes: + r"""Pass the expected sufficient statistics to the implied continuous nodes. + + When updating an exponential family state node without assuming that :math:`nu` is + fixed, the node convert the new observation into sufficient statistics and pass the + values to the implied continuous nodes. The new values for the vector :math:`nu` + are recovered in another posterior update, by observing the learning rate in the + continuous nodes, usually at the end of the sequence. + + Parameters + ---------- + attributes : + The attributes of the probabilistic nodes. + edges : + The edges of the probabilistic nodes as a tuple of + :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as the node + number. For each node, the index lists the value and volatility parents and + children. + node_idx : + Pointer to the value parent node that will be updated. + sufficient_stats_fn : + Compute the sufficient statistics of the probability distribution. This should + be one of the method implemented in the distribution class in + :py:class:`pyhgf.math.Normal`, for a univariate normal. + + Returns + ------- + attributes : + The updated attributes of the probabilistic nodes. + + References + ---------- + .. [1] Mathys, C., & Weber, L. (2020). Hierarchical Gaussian Filtering of Sufficient + Statistic Time Series for Active Inference. In Active Inference (pp. 52โ€“58). + Springer International Publishing. https://doi.org/10.1007/978-3-030-64919-7_7 + + """ + # retrieve the expected sufficient statistics from new observations + attributes[node_idx]["observation_ss"] = sufficient_stats_fn( + x=attributes[node_idx]["mean"] + ) + + # pass the expected sufficient statistics to the continuous parent nodes + for parent_idx, value in zip( + edges[node_idx].value_parents or [], + attributes[node_idx]["observation_ss"], + strict=True, + ): + + # blank update in the case of unobserved value + attributes[parent_idx]["observed"] = attributes[node_idx]["observed"] + + # pass the new value + attributes[parent_idx]["mean"] = value + + return attributes diff --git a/pyhgf/utils/get_update_sequence.py b/pyhgf/utils/get_update_sequence.py index 5beee61a8..889f185c0 100644 --- a/pyhgf/utils/get_update_sequence.py +++ b/pyhgf/utils/get_update_sequence.py @@ -10,6 +10,9 @@ continuous_node_posterior_update, continuous_node_posterior_update_ehgf, ) +from pyhgf.updates.posterior.exponential import ( + posterior_update_exponential_family_dynamic, +) from pyhgf.updates.prediction.binary import binary_state_node_prediction from pyhgf.updates.prediction.continuous import continuous_node_prediction from pyhgf.updates.prediction.dirichlet import dirichlet_node_prediction @@ -20,7 +23,8 @@ from pyhgf.updates.prediction_error.continuous import continuous_node_prediction_error from pyhgf.updates.prediction_error.dirichlet import dirichlet_node_prediction_error from pyhgf.updates.prediction_error.exponential import ( - prediction_error_update_exponential_family, + prediction_error_update_exponential_family_dynamic, + prediction_error_update_exponential_family_fixed, ) if TYPE_CHECKING: @@ -75,6 +79,12 @@ def get_update_sequence( ) ] + # do not update continuous nodes that are parents of an ef state node + for i in nodes_without_posterior_update: + for child_idx in network.edges[i].value_children or []: + if network.edges[child_idx].node_type == 3: + nodes_without_posterior_update.remove(i) + # prediction updates --------------------------------------------------------------- while True: no_update = True @@ -166,7 +176,7 @@ def get_update_sequence( ] # if this node has no parent, no need to compute prediction errors - # unless this is an exponential family state node + # unless this is an exponential family state node with fixed learning rate if len(all_parents) == 0: if network.edges[idx].node_type == 3: @@ -180,7 +190,7 @@ def get_update_sequence( # create the sufficient statistic function # for the exponential family node ef_update = Partial( - prediction_error_update_exponential_family, + prediction_error_update_exponential_family_fixed, sufficient_stats_fn=sufficient_stats_fn, ) update_fn = ef_update @@ -199,6 +209,25 @@ def get_update_sequence( update_fn = binary_state_node_prediction_error elif network.edges[idx].node_type == 2: update_fn = continuous_node_prediction_error + elif network.edges[idx].node_type == 3: + # retrieve the desired sufficient statistics function + # from the side parameter dictionary + sufficient_stats_fn = network.additional_parameters[idx][ + "sufficient_stats_fn" + ] + network.additional_parameters[idx].pop("sufficient_stats_fn") + # create the sufficient statistic function + # for the exponential family node + update_fn = Partial( + prediction_error_update_exponential_family_dynamic, + sufficient_stats_fn=sufficient_stats_fn, + ) + + # add the posterior update here + # this will be moved at the end of the sequence later + update_sequence.append( + (idx, posterior_update_exponential_family_dynamic) + ) elif network.edges[idx].node_type == 4: update_fn = dirichlet_node_prediction_error elif network.edges[idx].node_type == 5: @@ -232,7 +261,10 @@ def get_update_sequence( # move all categorical steps at the end of the sequence for step in update_sequence: if not isinstance(step[1], Partial): - if step[1].__name__ == "categorical_state_update": + if step[1].__name__ in [ + "posterior_update_exponential_family_dynamic", + "categorical_state_update", + ]: update_sequence.remove(step) update_sequence.append(step) diff --git a/tests/test_math.py b/tests/test_math.py index 9afa21c30..5c6a4b126 100644 --- a/tests/test_math.py +++ b/tests/test_math.py @@ -22,25 +22,40 @@ def test_gaussian_surprise(): def test_multivariate_normal(): - ss = MultivariateNormal.sufficient_statistics(jnp.array([1.0, 2.0])) + ss = MultivariateNormal.sufficient_statistics_from_observations( + jnp.array([1.0, 2.0]) + ) assert jnp.isclose(ss, jnp.array([1.0, 2.0, 1.0, 2.0, 4.0], dtype="float32")).all() bm = MultivariateNormal.base_measure(2) assert bm == 0.15915494309189535 + mean = jnp.array([0.0, 1.0]) + covariance = jnp.array([[2.0, 3.0], [3.0, 4.0]]) + ss = MultivariateNormal.sufficient_statistics_from_parameters(mean, covariance) + assert jnp.isclose(ss, jnp.array([0.0, 1.0, 2.0, 3.0, 5.0], dtype="float32")).all() + + mean, covariance = MultivariateNormal.parameters_from_sufficient_statistics( + ss, dimension=2 + ) + assert jnp.isclose(mean, jnp.array([0.0, 1.0], dtype="float32")).all() + assert jnp.isclose( + covariance, jnp.array([[2.0, 3.0], [3.0, 4.0]], dtype="float32") + ).all() + def test_normal(): - ss = Normal.sufficient_statistics(jnp.array(1.0)) + ss = Normal.sufficient_statistics_from_observations(jnp.array(1.0)) assert jnp.isclose(ss, jnp.array([1.0, 1.0], dtype="float32")).all() bm = Normal.base_measure() assert bm == 0.3989423 - ess = Normal.expected_sufficient_statistics(mu=0.0, sigma=1.0) + ess = Normal.sufficient_statistics_from_parameters(mean=0.0, variance=1.0) assert jnp.isclose(ess, jnp.array([0.0, 1.0], dtype="float32")).all() - par = Normal.parameters(xis=[5.0, 29.0]) + par = Normal.parameters_from_sufficient_statistics(xis=[5.0, 29.0]) assert jnp.isclose(jnp.array(par), jnp.array([5.0, 4.0], dtype="float32")).all() diff --git a/tests/test_nodes/test_exponential_family.py b/tests/test_nodes/test_exponential_family.py index 82c2e552c..488070bb7 100644 --- a/tests/test_nodes/test_exponential_family.py +++ b/tests/test_nodes/test_exponential_family.py @@ -1,5 +1,6 @@ # Author: Nicolas Legrand +import jax.numpy as jnp import numpy as np from rshgf import Network as RsNetwork @@ -7,7 +8,7 @@ from pyhgf.model import Network as PyNetwork -def test_1d_gaussain(): +def test_gaussian(): timeseries = load_data("continuous") @@ -31,3 +32,35 @@ def test_1d_gaussain(): assert np.isclose( py_network.node_trajectories[0]["nus"], rs_network.node_trajectories[0]["nus"] ).all() + + +def test_multivariate_gaussian(): + + # simulate an ordered spiral data set + np.random.seed(123) + N = 1000 + theta = np.sort(np.sqrt(np.random.rand(N)) * 5 * np.pi) + r_a = -2 * theta - np.pi + spiral_data = ( + np.array([np.cos(theta) * r_a, np.sin(theta) * r_a]).T + + np.random.randn(N, 2) * 2 + ) + + # Python --------------------------------------------------------------------------- + bivariate_normal = ( + PyNetwork() + .add_nodes( + kind="ef-state", + learning="generalised-filtering", + distribution="multivariate-normal", + dimension=2, + ) + .input_data(input_data=spiral_data) + ) + assert jnp.isclose( + bivariate_normal.node_trajectories[0]["xis"][-1], + jnp.array( + [3.4652710e01, -1.0609777e00, 1.2103647e03, -3.6398651e01, 3.3951855e00], + dtype="float32", + ), + ).all()