pymc-learn.github.io/index.html at master · pymc-learn/pymc-learn.github.io · GitHub

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    <title>Pymc-Learn: Practical probabilistic machine learning in Python</title>

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		  <p class="lead font-weight-normal">
        Pymc-learn democratizes probabilistic machine learning
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        Pymc-learn provides probabilistic models for machine learning,
        in a familiar scikit-learn syntax
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        <h2 class="featurette-heading">Built on top of Scikit-learn and PyMC3</h2>
        <h2 class="featurette-subheading">Built with the broader community</h2>
        <p>Pymc-learn is open source and freely available.  It is built on top Scikit-learn & PyMC3.</p>
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            <h2 class="mt-3">Scikit-learn</h2>
            <img src="https://github.com/scikit-learn/scikit-learn/blob/master/doc/logos/scikit-learn-logo.png?raw=true" class="top-image">
            <p>Scikit-learn is a popular Python library for machine learning providing a simple API that makes it very easy for users to train, score, save and load models in production.</p>
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              <a class="btn btn-outline-secondary" href="http://scikit-learn.org" role="button">Learn More about Scikit-Learn &raquo;</a>
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            <h2 class="mt-3">PyMC3</h2>
            <img src="https://cdn.rawgit.com/pymc-devs/pymc3/master/docs/logos/svg/PyMC3_banner.svg" class="top-image">
            <p>PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI).</p>
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              <a class="btn btn-outline-secondary" href="https://docs.pymc.io/index.html" role="button">Learn More about PyMC3 &raquo;</a>
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            <h2 class="featurette-heading">Familiar for Scikit-Learn users</h2>
            <h2 class="featurette-subheading">easy to get started</h2>
            <p class="lead">You don't have to completely rewrite your scikit-learn ML code.</p>
            <p class="lead">Pymc-learn provides models built on top of the scikit-learn API. So you can easily and quickly instantiate, train, score, save, and load models just like in scikit-learn.</p>
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            <ul><li>Scikit-learn syntax</li></ul>
            <pre><code class="language-python"># Linear regression in scikit-learn
from sklearn.linear_model \
  import LinearRegression
lr = LinearRegression()
lr.fit(X, y)</code></pre>

            <ul><li>Pymc-learn syntax</li></ul>
            <pre><code class="language-python"># Linear regression in pymc-learn
from pmlearn.linear_model \
  import LinearRegression
lr = LinearRegression()
lr.fit(X, y)</code></pre>

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            <h2 class="featurette-heading">Quantify Uncertainty</h2>
            <h2 class="featurette-subheading">Models should know when they don't know</h2>
            <p class="lead">Quantify the degree of uncertainty in model parameters and predictions.</p>
            <p class="lead">Probability is the fundamental mathematical principle for quantifying uncertainty. Pymc-learn provides probabilistic models that represent and process uncertain values using Bayesian inference.</p>
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            <h2 class="featurette-heading">Scale up to Big Data</h2>
            <h2 class="featurette-subheading">Using Variational Inference</h2>
            <p class="lead">Recent research has led to the development of variational inference algorithms that are fast and almost as flexible as MCMC.</p>

            <p class="lead">Instead of drawing samples from the posterior, these algorithms instead fit a distribution (e.g. normal) to the posterior turning a sampling problem into an optimization problem. ADVI – Automatic Differentation Variational Inference – is implemented in PyMC3.</p>
            <a class="btn btn-secondary" href="https://docs.pymc.io/notebooks/bayesian_neural_network_advi.html" role="button">Learn About Variational Inference &raquo;</a>
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      <section id="supporters">

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          <h2>Supported By</h2>
          <p>Pymc-learn receives generous support from the following institutions, either through direct funding, or by employing core developers.<p>
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              <a href="https://logistify.ai/"><img 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x2YYj5fvtnnH4ZNu0ZtlXOwF6foe+GfgfWj8PFUjNCNdLyO5fX6vj05tT8CrRufUhvvVFu3B7YxUHoKoBA8GVw6Vgfq+D6uh4epzDDOUz3M/xg9d1b9zSH19A4YDj6habNvKqZxjPc5zMmJUX0XZlv119Ms9tN3wV8z96lSel2raPMtlXfm7gvqUa6s8GWVHVLveU2q5vOup459Tv08MhXXV3+vq3G1OXsqfvRGjM7AkPF7V++pH6e4oMK6qXkBlAmBdX8/sG68YLfg+p1M/ODysbcbpvFvavfC15ceSbhJUP0Dp+sACsGTwadjdqSOs3Oi+Mt4vPeKP9DfLkeifK2e3gGDS58lz4uemIq7u/2yJKh/cDp/0sblnPZv9vtdsHLahgPDkXcL0TA67s4v5FFVC+buSws+L2+tsrb6vKM+bydEN1gGdX+o8q7otjBUHOCI+p5dRu/bN3SadyZKmSsqarWqm8l9Ek/HcFlt/i+4mTN1LL0qn3+P/UZtnpdig9brqQ3nzvlLYBsDo6sACsGT4aFjd5KO9z/00MOFWw2v3NdBFG13K/OpAOiQPkOepuPRDhGJYevgu9POi7ChXouTJV6au0PdUN89PNcX9fPm7gvqUSaIPSAVgcCoO30RvET58U7sru0m+sw9VHUD5Xx/B3QPSt3lEwHGc5HK3SpvDKjbd9fXTsV7twkiA/KMPqlH9OgTO2Ca//Y0t8gAim/W7aSyfWAbA6PjAArBk+GjY/gjHfdPpOKuU6uhzB4CNrue/0X93X/jewcMBn1mnGX9qFSuujIA3qFysV6XpwE6qPqP3B1qR5nrZFIqcs70049o9EDH3YEyn+M+lLsvPfDF9Y16LWvr83ZlVZWqPo/E8ed4xarqBEbz9aLeZ/7xv1dQE57ylj2AUgYio6bv/CsVU+sQSMdwCW1WDm7mwmlHEvk7XW1fk2JHvmymNpxb62+BbQyEjgIoBE+Gl47lhWUeAC9b3GrZRq9SMKuev7H+ru4lN4G+o8/KR7Q5VeV1uftSMV8seorBSnqNa+n74KbcHRpPOYLOx2G13H1BfXTcfdf7wlQE/fqdl9O+qLyJ8b1eKnJus1TckfTd1pmq6BwwjskqX0kx+b4WczL2Vksq12DJFJeE/LyI0Wf4HzlGn4zwKJQfBbbrm+TbptTMpb+bpO0ACsET6JhepffB8nr409R6TXJH+2fW89fp9zwIQCR9Rjxc0nfGKllWvqF8/rimnNJzfO7OjKUcAXSByntz9wX1KZcG97XNW3L3pUKeiuTk7v6htmenU2r1dw6WbJ6KJLp1LtmMIab36V16712ihysENeHr0jp+/I4ncvrO5MC6kZ7/LbxmcDPX6bNwyXT+7WwVr7wWGezfWq/zIPXhicA2+l5bF+wETzDCQ8rK5Em+W9fq/eCpCGfr+Z/ul+H7QF3KqSLfScOzDKiTzE7W6144FVN6GjPFr5y+4QuW6CUJ0SDliKOfpMEKnozmAMjc7U6pLYOIXygLgRPk4LxTUQEUJ1v+cq5zT/D0nQdVzg+qGy9wmoLoFRH3n94/lFPdvCLPMYHtexGDLdILKwBhDC0DKARPMK0yG7SnHDjp1wItnr6SyvlOyKi/eyy8c0Af0OfBCZk9VWSN3H3JwLlF5tc+2FDfCf/K3ZlySVYHhAmeDJHyx4xHQ707d1+CeUrtc2UQpdVIFCevXq+GPgHTc5bKwypzB9TtqXofVrk8oO52vF/lTUF1n6TP97+D6kaaes54gzbrBzdzu8qPWzznRJV9UuyU7x30er/LDILpGzeAQvAE06NjfafeHz4R/UzlnS2e7uc5meQqJCbCsNPnwCMxfIIc5oSMXmloNu2LNXOeoMtRQGeoLJ6rD8jGuXk+lbsTNfEot6dUds7dEWA8Oh88o+9lJ0LdJqiJdVO+AArTd/rbjqmYHhnpwFYjpHzNpM/IIWmckSoV8O/+DVWODmyjr003gELwBK3omN+j98kyqciJ0uoHiPMKXKbnr6S/uy++d0DzlMuk+gf7MAdPRnxMZYr2yWcyjkRxojaOxZDRe+5tafpJ+gbVTnrd9+iz9u3cHQFaOC7FBVA+o8/BNnUH7oOn71zfgOS4A03Hz0vFbx7czD2p/VWUvGKjp2jOHtedtIte97F6bz0b2EbfGjOAQvAE7dKx/6veL8ulYh55q6W1nPvgCj1/BScLC+8c0CDlBdTkxAovo31c5Tjtm/U7TXTZK08r1GaHOttEYxyaipw8w8ajQQmgoNF0Lvi1vp+9hOsSAdU7v4OD9z8MqHs8S6vMH1T394PqxQsc0JsluI1vtnszSc97VJ8Rjw6JHFX41lQE/U4NbKNv/U8AheAJOlV+kJ3rxHNXP9ri6f5Aei3zFfV3t8f3DmgMD7dcO3cnGshDqu9Oxd2UWpRJY52s8CV1tYlmcABfm5Vz9yODcxLfP+gf/n6OCKCYzzl1B1Cipu88k/iBG6pczj1qRNQI5/3pNDGsg+FecjhyWtGuev2n1X2Dqx+8KIBC8ATd0nvhSb1/VtfDKSqrt3i6EzFdruev7DsN8b0D8vJy3qnI3o6x7aZ99Gt9H5xeU3uePzxvTW2hWWoL1DXIuSo5p8oBnTolFauARIwU+5jON3P65l9A3f9DbXnVls8EVX+WXscjQXWj4Kk7EUmNRzvUv6M6+QM9/94yX9DGQX0yp2dYNRWzDDDK8wGUDoMnvlv4Mv3NvuM8x9Gqe1V+5GkePfUS2egYvzQVXx5ekvhV5f/2yeBlZZlh1H97224k9NUql6r+1fT+uKbSTgMNove4V3eJXHJuEHgkyLFlEOWOyIbKKYd13on3RfrZqTi/3qxyl8oTKv5unVPF594lU7F0p8vLa+zbUCmX0F6upuZ8DfQblStVPB3hDyp/V3GCwJlTEcBbQGUhlQ+kYjWgiOUxfeFL8AR9xUEBfV5/pIfrBFTvVfAc0PheQN1j8fT2NwTVPTmoXqTnV0zcMbiZf6h8t8u/dZBxoxQ7mnb3RADlf0wNoOgN8pbUfvDEvBTYnm0+90DVv5a+DH/aRf+QUZmz4TSVNYOa8I+HC/2DRu+PXwa1AWRTrrjjO2kzZ+yGh4b6B9zvVP5S/reH/foH/Gwqr1HxOWDRVPyYi/gR1w4Pkz1F++z/on7sld9p34ioewx3quyncopez9Nj/LuXnHygLFeofFP987HwiikeljtXDX10vw5s8ZxnauhHXdatoY37VY5UOVHH/U/t/lF57D3ffLNU3dLKvu7KutIV0ANP44kIoJi/C+oKoERN33HS0YuC6kbByxbPF9zGsd2uUKq/+63OHZ6e+YmK+zTah9TGh9TWVYFt9J2RESheU7rd4EmnnCH4VO38t2rnPxzUBmJ4Kk5U8GSEfzQ58toqAS3QjyapLJKh3dtSkc3dQ/dvbnf+qr6n50hFHiN/7tdI9Qd+POfdwfmvBtXvkXTvCap7hH+sfi0VyxF2FAjS8x/UZqKOw3dSsUrMpin2zpIDfNeq3fMD22iSyItMB5omqhzipVg7/ePy2Du57aFlnpZ9Um/nxZ+pfKqbvgAN4Ru7f0xFgL9qH9HnbH59Pv4SUPfzgqfvnMAKKXHKEfjRU699I+WbPdZxUIo9t5lHoXwsuI2+4mk4C6Ri+GgkjzTwHKp2l2dCM7RKCFuVpfU+nEUngn/U1B4QTu/ppVIxkqBOF7ppfZau6OaP9XePpSK53g/Vf4+A8PQ9Z3l/1bh/WC3nQzlDfbk5oO5dA+oc7c+puON/Qy+VlDcbPq/94GGzk1PsUoU+vgMfQCkTBy8UVP19Kh+rKqeX6rlI/b04FXfJv5U6//z5rvQnpzPyaTwOvF7W5nP9/VDVSJnRbknFVKeqdbovmsbXR19q87kOgC9dcfuekt/NCk4/77ZBB/71OfAKM+OlC+iWAxse3RK9nPkHU8wNat8UmRxQL17gz9GCwW14hOqfe6nAI0P0OfHn7IMV9Wksq6qNxYKuy/qSR6DMWVNbr62pHVRn1pracQT9vzW1BYQr71x4GP9La2ryepUdqxxiWSamO0Cv5bBUzAH2HYiZqqp/HM4B4hEYy1ZZqV6HbxS8r8o6p+F8F06MfW9VFaquH6nfd+nhBanIGxVhOedAi8490wA+9hGjeZz4z8f91iorLUeNnaRj46Cof0S2u/y579p3Ezxxm55ytl87z1W/fIMlYvrArkM0Iqpt5Uiig9t5ro7N21L1AZQH1Ye22q/Y8akY2RVxLnWAMjqAEjV95+c6Hr8PqnvoldN9dwtuxt/xrabQtsujUCIDKN4fvgZkJbeSAyi+4+Akd3UFUoBpXdjNxR7QYB65sXgN7fhz4ykvh0QN5S0zw++jCwovlehkuB+JaGcay5S5s6pclWfTCuua1l0qK6m/91Vdseq8UfvCIzgvS0XOmqr5wsjzvL8SUHeTvCOo3r2qDp6M5iT85Qp3Pj4T0/hBoMtVPq6/eSqqP0CdPMVG739PR1sloPpFI++ql9N3oqbBfz+oXhS81H30dN9z9N77TVV1qdyu8s6K6hvLBL2nlE9dxgAAIABJREFU91Sf7wxso2+8zMnFtEMcZTsyd2cwlLyi0xdzdwKoir5P/SO3jiXenaDS00VurKEtX8jeode2fCruUHsoeWRuDvua2vuh2v1PrxWpHq8O9ukK+jQW5zxZMyJ4MkJ1/0qv4fN6eGpQE56jP+gBlIhVMHzsjw6o90XK0SgOYvqO8+Q09mp3nrb3MabCYgA5mWxEAMU8CiVqWsKHVV4XUK8/41MC6sULdq+hjcpGP+l7/786P3gUyrFV1TmGkZwwWwS20TemJpHVjj9KO/6hVFwU+y5Nt8so1jXlA/l5Gc7zevh7J066KRXZpx+tpktAI2yfiiVKI12n4iXAI3IFTFc5ymVXnS+cp8B3wF7W4k964bnH66dq7rQtk+JGWe7tAEdQ3c9TG6dpv388xawk806vxqc2/hhQd1NE5PG5pRyhVQu15VWqfO49IxVLsY7w/HeCJxhUXgbeq5REfIbX1mdqd/8ADag7avqOc4Q9EVT30Cun+0aPtL1Cx/Dqiut0nlHnC4paFMY21P7ZO/KGUb94/uJXO2Nq4sBuKyqHqpENenjcp/cMc+GAUcrRJzsEN+MfSw6ePB7cznSpbedm8PQhL9HcbcC9HbuoneMruLhdvpLe/C+PCKhrWWTbKRWrI80SULdzWgxyAOUVrZ/SsdqnyuizcK4+E59LxR1oX8Ndo7JqnYEcoE5ezUzvea8WGnFunT8VAfZLq6y0zIMWNX1nclC9KNQx+mT/qissZ5QckmLz+vg86puE0asTNV7k3UMAGDYe2jhXYP3OtZA1eDJCfTiznFYyObAZz+f10nnn9FhPVHK1SZ0uVdwLL3WrfX54an81jk541ahjAuptiraW8u7QO5xssN1lwqui9s5Su9vp4QapCJ5wNxqD7rgUd3PCo/oqDaCkIijzmorrNAe5Lw+oF2lq4GuxFL9cr0ffXxBU91EqX1aZI6h+21L76evlQgNDiwAKAFSgvOO0dWATf01FgsjswZMR6svxet2earNHYDNfSD0EUMrRkREJfb3UcFROkvEckYqlh6vOQROdMC+3iNEi/oHki+1zA+oelz57h+u9fVy5Ogsw0JyoWe93T119f0D1a6rurSv+LE2osK7Rjq87YDtkdk3x+d0OiDqGqvcxvZcdRIkcIeKR1s5d+bXANhqPAAoAVMPLjL4pqG6fbDfUyfGuoPp78dVUjPBYNqj+lXRBsEAPr93zgSNWr/mRh8wG1Dsutfkn7Y9rU/XLlC5YcX1N81BQvYfoeFyt4/JwUP3TRfAEQ8YJMiMCKM6P5UDomVVUVt5MiUha7qmsxwfUizT1uL0lxeWtGeERRNEJgD2Nx9Nsxko2XpXttL++Ncx5twigAEA1Ngms+zCdqKKGfPakzP6+YSqGpUYkavUIknVSsfpPN95cYV9Guzio3nbbrjqAMpuO4zx1Jyau0V1B9fqi+3ztOydx/VtQGwBSOk3lWyozB9TtaTyVBFBkOZVXV1TXaJfoO+bPAfWi4Kmx0b+LD65iZcHxOMGrzkcnpdhrUid03lTlO4FtNBoBFADokU5WDhysGlT9A6mY09pYOmHfrX0wUQ+/HdSE7wp1G0CJuJC1qKUv23FLUL3eV4MaQPltYN2+K+6lpjfWZyFnYA0YWOX0BK9AtUFA9aup7rkqyusQNX1nclC9Q0/H/rXabBTczIOpmlUF23FwKl7PDIFt7KT9doQ+M/8ObKOxCKAAQO+8vGzEKh+2R58kiXRy061U3hFQ97t7mMYTlUwt553AqLZnD6q3CbzUtC/0olaN8moeF+l96tUMJ+q9GhXkAoaZp/FEBFB8/nbg4+heKtHn37+rIqbvPJZ6WCkVLTlB8UzBbRxS17RLtXOb3ovOHffJwGbemIqRW5MD22gsAigA0LuorO1eJrcv5jz7LkQ5CuWUoCZWUTmyi7+LCGx5LnrOub9RAbWoIGB2XuZX789f6OGHg5vyj6c11JanA3xT7f4iuD1gmFyZivNiRM4m/xjsKYAiy6diekPVTtN3ydMB9Q69cgTxlsHNOPn/EcFtTOugFBtAsV21/07wVO7gdhqHAAoA9KBc5WXFoOr9A+zZoLojeHi1p9pE5B35aOougBIx39hZ+n3ccx2bqGG5g766g+/gRgdQzMfHd7Mn6PvhhlSMzjrdQZwa2gYGllcv8epTevj1gOo/pLrf6CmpPdQRNX2nrqkfw8grykSPvjxS76tHg9t4EbX3c72ff56KJP9R3qmyRhrC0VEEUACgNwurzB1Qr1f16IvRJyOcHE0n7EP18BsB1X+gy7+LWL7WARRPDap95ZVSRLJeG/RVXX6gsn+qd6TNe1Mx7eCQcnqP+3BxnwVGgSY5QWXfVP1vmJGE5ft388f6fHt64Kcq7VHhNkayxdAxc0Li7YKb8Xk1Kj9cKweq/Di4jd0SARQAQIe6/WHfSr8O2XX29wNS9eeX13d5dzBqustbU74AytuC6s31emqh985Deg9N1sMtMjQ/aypyN7jcr374otbFK2v8K0N/gL5UrjLiVelWD6je03i6CqCkYpTkPBX2ZcTkgDpR8Eoy8wa34Sku9we3MT3Og+IE6gsFtvE+fR5X0Gu8KLCNxiGAAgC9WTyo3qqWVKyVTqJ/1cn0ilTMBa/aoiqdBlD+EtAP88iC64PqbmWJgDo9fee+gHqbZp9U/EiaNWMfXpeKOfcuj+vzcl4q7uBdwDQfoC0e1RURQFlEn8fF9Tn8dRd/GzF9x1NQTwqod+iVI4Z2Cm7GIw0PCm5jusopb16R57jgpnZXIYACAGjbogF1+kfUlQH11sV3ByMCKIuonNfh39wV0A9z3ptucrJU1XbV7u/TEU8dKe9eO3/CpNx9KXnu/dpleUZ9OzcVU/ccTInI3wMMAp8HvCzsawLqdoC1owCKPrczpiIXRNV+6u+sgHpRTNd6U3AbZ+r4/SG4jVY8bdRT3uYLbGN5fQaW0mu9NrCNRhmqAIoO7sqpiFhHzR+vgoeb/1TlbEcOc3cGQEsRqwH8os+H9V8eVO9bOv0D7cdH9N3vC9DXV9yX1VTvXK6/4nrHpTY98iViqehbA+psKt+R893iqNFj3fIymp8pi6f5HKPtUfyAAl6sXPXtRD3cOaD6z6nuXTtcWWSFFJMLbXJAnUOvTP6/a3Az/g3X7XSwyvhaUq/3kFTkQ4nkXCgROYAaaWgCKHrzeGmyz+fuR5u2UjldfV57GJeGAvpFmYAsYv7sLwPqrNNNqRh6XPU5ptu7Rdel6u8O+seu508fXHG9rWwdVG+u6Ui1Ky8o10vFa35l7v5Mh6f5fFVld/V1irb7qd+/ydwnoEk8jScigPIGlWVVLungbyKm7/w9FTksUD0v7/uu4DYu1Hf2jcFttOsolT1Skfw+yid0rlpIr/m3gW00xlAEUHRAfeHcL8GTEWulYhg8S5cBzeVRDS8JqLevT0A6gf5T37t3pupHSry2y7+7OsUMr95Jr9MjBKIS1b6I2vJop3WDqu/nKWMdczBC+3OzVOQXiPgMV8Xz9D3U3HfFz9J2T/X9tsx9ArLT5+B2fSb83R6RyN3fs20FUAKn75zsc2lAvShGS0Q7oIY22qL3kXNtecpx5KibkVE9Gwa20RhDEUBJMV9sdfBQKAIoQHNFZNy3u4LqrdNdqfoASrf723klIoavOqCzt8qOAXW/iC5+/CPfS0S/PKB65z4ZqgCK6aLyZO1Xr2i0d+6+tMEXp59OxV2+72m7lxM2Z+4TkJtHoUQEUNbU5+yL+oy1s7T7SikmNQDX/wF0XL1a0vuDm7lW751Lg9volKfxbK/yisA21tb+/ape+58D22iEYQmgRP3IiRYxnxJAdaLyKQ3CD6MHA+rsavip79jrpH6HHr694v7Ydqr7fLXxs4C6R9smFRfqEZyo8KmguhtNr3sfHb9Z9HCX3H1pk6/bvHrPWs7ToO2x5EvDEDs9FT8Mq15Vy+eaj6tMaeO5EdN3bmrQ9I9BM1SjT0Z4KWWdMzzictPAZnyDx9PqtglsoxGGJYACABGiIvmPBdVbp8cD6uxlf5+g8rWqOjKKRwacoguTD0dNrVDdDpxE5lo5ObDuxtNx21X72KNwnHOkydN5RvMNFieZ9dSeDfUa7s3dIaBuXvZb738HUTYJqN7TeMYNoKhtn5M+GdA2o08C6Hi9LxUJfyP5OuDHwW10y9cRG6fiuiXKptrP+w76CEkCKADQvRmD6u3nFXhGRLyGXqav+IJ0Yoo573mU44Ve6a3qBGqq00sWn5lipu7YQypnB9XdN3TcJpZ5e5xwfqbc/emAh6P/ugyi/CR3Z4AMPI0nIoCyqj5Xc+tz9fA4z/HqnlUn5vS5c6iD2oHqGH3ipXw91bKpbldZKLB+J2bfLhVJawcWAZSU/qHyp4zte1WJ2TK2D6B7zwbV+9KgeusU8Rq63t9eClYXNKeluCSsXrnhSrWxflU/ZFWXV9z5RooL1NlRJCosaD+cqH3uu4enqLwtd3868CqVc8qlV+teFQrISu/5q/Xe94/Cd1Zctb93/TP4qHGeE/Ez+Vy9pocC6h1qeo941Z06cmJuVJZh9gXt7wOcvDZ3R6IQQEnpVzrAH8nVuN5gHub1iVztA+hJ1EgR52To9wuoWQLq7HV/T1JZO8UNX/W0inP1ve5RDLvp3PJoN5Xo79+aioSxq1TZuTE478l3gtvoKzpmv9T+X0IPv52Koc79MqXH7+mD1Hfn+dlSr+O/uTsE1MijUA4KqNcB9zEDKPqseaRaxPX75IA6UeS5ipy6ghc4P+BWqYG5YKpCAAUAuvdkUL2eEnJXUN11iUje3VOi0zKZrJOobVBRf8biH9xbqExQW05ueIyTt7Xzh3r+u1ORfM39i5qyM9p3uNP5v8plqTct3yuHp+rvbEf6vMpM6vvGeh1RI+SApjlRZb9U/ffmh/RZWkCfpbvG+DcHuGevuL0HVM6vuM6hp2P4xhQ3+hRj297XQG2uZNV3CKAAQPfGmxvdi/lVbgiquy7zB9RZxf72HGgvER89ddKjUbw87ld0EXGFtl7S8NZUBMacJNgX+nOpLKjiEQ9ObLdocJ9Gc4K3/Wtsr+94GcoyqLV5KuZzvzZzl9q1voqnZX0+d0eAOuiz+qA+q+el6qdoOCC+TiqCM9Naq+K27CS9lv8E1Dvsdkr13JTAC3y+9CjOI3J3JAIBFADo3t+C6o1Ybrc2upD1ReeCAVX3vL/Lpfy+rIffraA/7fB5dvmyNMku2heDsNpTKO0jTxs7VO8ZJyH2kOTtVebL26u2bKY+/0H9H9gh1MA0PI0nIseFRy68KICiz5YTZa4e0Bar71RMx2pebTbL3Y8htbP2/zGDGBQkgAIA3XswFXd6q17O+N0V11e3BVL1KxPY3RXVc1gqLn5Xrqi+fnOBLmiOz92JfqL95YTzB+ti0DljPqvyBZWl8/aqpf3U32vV98tydwSowQUq96m8vuJ6F9Ln6D36HN046v+tmqofxXhd1au4YaptVWbO3Ykh9ZZUnC9/kLsjVSOAAgBdcqJGXVj5R33Voy0+UHF9dYvq/5+rqETH7Tkdt4308PpUrJ4zTBz02zh3J/pVOSLF+Ra8Ys/iqdiXTkw8b9aOjc0JE4/3NKRuExoD/cJ3ufVePyHFLFXrUSijAygR03cYfVIxvR8c5Ppi7n4MuV11HE72dVfujlSJAAoA9MbLnlYdQFlAJ5wFdcL5fcX11mXFoHpvr6oi7dsHtI8/rYfOTzJTVfU2nIfRfs6vPXdHBoH246+12U7vo521XSkV0we8Ksers3bsxZw8cd9UJCcGBt1xKrum6lfPWrtcJvxZbT2aoerpO0+rnFpxnUhpy1TkGkM+zu3mz8s5uTtSJQIoANCbW1LMUoafVDk4oN5Qurj0eeVjQdXfWmVluhi+Xv1dTw9PU3lplXU31HZM56ie9um/tXECy/P0fvIF+4dT8fl1eXPOvpW2VL+OYHoABp1vOui9fqUefqTiqj0taDmVi1RWU5ml4vrPYpRYtcplpnfI3Q9M5VFhBFAAAM/7ZVC9/mHfdwGUVCzt+KqAej11otIAiumi9UxdaDmfxZGp+ruWTTJJr/Xw3J0YdOXSwZeVZYdyms+aqZjm89ZM3fK13j4qn8nUPlAnJ5OtOoBinsbjAErE9J3JAXUOuw1VXpe7E5jqAzoXLqPz4+W5O1IVAigA0Jurg+p9t044H9QJ5+dB9Uf5QlC9v9K+eCaiYtV7dLlykAMMM0S0kdnBeo175u7EMCqn+fxa76+vavt/qfgR5hKRZHk8n1IfFlB/7qq5XaBuZ6g42XPVn7FP6zP0pVSMQKnSX1IRmEFFdJw8ovRLufuBF/EoFAIoAICpP5D+qpO186C8K6B6XwD0TQBF+2GRVIxAiXBlUL1T6Tgepf57pRXfvZwxsq0aOWnbRL22fXJ3ZNiVCfQcbL1a77NdUhFEcbCxrhW3HBj0qLav1dQekIU+a0/pM+ZpmZtXXPXsqRipWPX0nROckL7iOoedRwnlGvGHsa08xmpWfYsACgD0zssnRgRQPq4TzqI64dwSUHeEL6e4aTA/Dar3edrPJ2l/35OKO5jzRLcXzMtrb6XXxMoODVMuiXy0i95vDjiOjE6J5iS3BFAwDBwIrzqAYmtWXJ8Dq5MrrnOolaNJI1ZiQm9Gjstnc3ekCgRQAKB356aYZGW+a/zNFLeqTWV00bK0Np8Lqv5xlauC6n4RJ1nVa1kiFSsi1PGjNoKXe15br+Wa3B3B+HSMHHy9oAykHKLy9sDm3qN25lKbjwS2AWSn9/h1eq87Z9YiufvSwlXq6x9yd2LAeIrVYrk7gTGt2ecrTD6PAAoA9M7zOh9UeU1A3St4uV2dcH4YUHclyvnG/vEXNfrkbL3+fwbV/T/U1t1OeOaHqRhV8/K62q7AySpb8yO5vziQUiacPUxl46BmHJB1cPDiCur6dwV1AJE8CuVbuTvRAiMEq8fok+bytaKnsH4+d0d6RQAFAHrklTf04+d0PdwmqIkjVP/P1c6DQfX3aneV9wfWf2pg3WMql6adqP1+Vip+1H6g7j50yFOPHDj5ce6OoDs6dk9rs4necw5+7RjUzDtSNQGUf1VQx1iGYTlx1OMklQNSc3NaPakyJXcnBom+O72E/Idy9wPj2kDHybnZ7s3dkV4QQAGAahyT4gIor1b5vk46Hy+XSW0M9cnTXL4a2IQDA+H5T6bHq6joNfqCzNOTJqbYKRbd8EW4p3kdWObWQEV03GfXPn08Q9NOHv0+lQ8H1P2GiuqJCqDMHFQvhow+u3/TZ/js1Nzlu89QH5/M3YkBs3vuDqAlBzR9g2Cn3B3pBQEUAKiAE716lIgefjCoiVVVDkwNOul4WVRtfpRip7gcp337n8D6W/IKKnqtHgXzV5XjVebL2Z+Sf9g7EelBXgkqd2cGjY73StqcqO3y2r+/qbNtr8ihdj11LGLlqbkrqidqSl3ENEgML0/jaWoAZXLuDgyScgpk1CqAqNbmOl776Vz399wd6RYBFACojkcCRAVQbEeddO7WSeeQwDbaon54VIyT50b+4HlG5fDA+tui1+pj+g2VpXL3Rbwi0/dUjtf74LHcnRlEOt4raOOpW69UOc/HP8NwYwdjH1KZt+J6X1FRPVE5dpo2wgv97Wcqf1GZP3dHpnGnyhW5OzFgnPskKg8bqjWrytYqe+fuSLcIoABAdfyj63epyDMQ5dv6QTejftAdFNjGuNT+67W5KMUs3Tza5Jx5X8rlEPdJRSLZGTJ1478qN6r8JBVDvm/O1I+hoGO+nDbOI/PK8n+9SeXCciRKbe/FctSTV1OqOoBS1WiuhyuqZ1r9uvIVGqjMT+ZRg3vm7ss0HAB/LncnBoWO8dtSPSONHEy/uoZ2sipzyUQH+LZRO9/o12lsBFAAdGtuJ4LK3YkaHNHuD6dy6L0j6icH9+lAteM7ajuVyU5rUy7x6xWB3hTclKcIfD24jVaOVNm8xvZ8QX23ym0qv1a5TuVKz+WvsQ9Dq1x56Zz0v3k4FlK5XP++so7Fn2vsUlWjRUar5GJV++EZ7Q/n3JmlivpGWUL1vln1/6niejG8vNKNV1RryugEB8WPz92JAeOVXaITUF8xDMET0+u8Ut/D16TYgPY8qViNp+krZY2JAAqAbs2lslfuTtTAwYJO7jyfprKryrtjuvM8J6xdXCe59bzsbnBbU6mtTbX5bnrh7nyko+p6XWPRa/1iig+eeNUV57nwqigOlvxar/nR4DYxBh3vj2hzXpp+QMCjyq7W89bUMfpFDf3xZ2zBgKqrHEXzgMpbK6zP/CPXeZ62rrheDCl9Xv+oz9Olerh87r6ULsl5bhs05YjYDWpo6oAa2mgS59z7UXAbO+n4HabPQ1RS8jAEUACgQuUolB308JIamvMwy1vLhJOHu+2IRnxHWJujVFaMqH8Mnh6wT01t/Q+9Xk+biBz9crvKwSpTMq3yglHKHDfjBU9G+EL9Mj3fdzu/GzwE/9MqMwXU+8cK6/IokaoDKOYEg04e/ateKyqn4TnYfKvqq+M7Gc3kZLJNCaB8P3cHBoxXdIkYrTfaTSrnB7fRNF7Bytcq7wxswwn510/F57OvEEABgIrpQv1SXbifrodr1dDcbKkYFbJVOX3ojKoCKeWdHS8L6GGW0Rcoo30lc3b2jVOxX6vmpLj+8X1405ajHlblMty+MJ61zT/x58BJnD+uv91Sx/HOgD69Spv9q663dGuFdUVNs/GqXmdqPyzby5SpMjDmXFE+xqtV1Tn0Jd9Jd+LjuTL347EUf1d/aOgz7lXFtqihqQOHLWdNeTPQN3q+F9zULmpncr9dExFAAYAY26l4NY+qlg1txXkaPH3odzoZ+YR3qk5I93Raif7W5wXngthIZUKqN3BiXn3kyJrbnFbESJunVFbx3OKAutEFvde9qpKDJ90Ey/zZ9uivb6diKelKkqqWuY2cxPYNVdQ3Df+AvL3C+v5QYV3TWkDleu2PbbU9vd2gsJ7v3EwfU9lQ5f1x3UM/0fvnab03TtHDL2Tuis/LT2fuwyDxVL92g9/d8qi904PbaKqTUjEa+PWBbXjlNY+4nBLYRuUIoABAAF0kPaALNgdRTqy5aedq8F1XJ5r9pbaXqXgovH84eTnHR0Z+jOjfnSzTyxG/RWVRlQ+ofDQVyb1ycJBhs6ipSB2YL6DO/QieNIfe+/5x/VOVOXqoxlNsvHTmF1Wfh+Ufo2Pc1QgP/b2DOFumItllL30az0UVf7Z+XWFdY/FUOv/o3Vf7xyuc3ZCK5V8dCPLrmDMVy9P6O2+xVIw0eUtwn9C/PE0gdwCF6TsV0XeCp1xuU0NTXimmqtXL+ope9z+1nz3iMjr/y25q54x+GuVDAAUAguhkcJJX7tDD9TI077n/7yvLaF4e1Sv3eFnepp0DdtA+q/IOebf+EVDnLwPqRBf0/l8y9R48Gc3BD4+U2FZ1e5npC1KRHPgGvZ/vn04f/Pn0aImlUzFiYo0Ufyf1zIrr6zlHSZu8ROnONbWFAeWcOvrcOei3eKYu3KY+XJup7UHkqcWvCm7DSbeHPejl/HfOsxcV2Dev7rhSKs7LfaFpF88AMGh8x8snh4Vyd6TkH24z5u7EGE7RxeXRuTtR8p3uJSuu81Opjy4OBlW5DLePw5xBTSxWll3K9p7QxkEUJwv2SgP+7Hla3+tSPatZjXBOoR9XWaGX19brcx6UN1dZLxDIo1C+m6ntYf8hXhl97/h7dMcamjpk2Kdc6fU/pv3tIMouwU05317fXCMRQAGAQDr5PKGTj388+85T1I+2fnejyma5OzGK5/1WnZhuC70PfDdrn35LljYotP+9tPiFqb68RDZbiklI3CknLn4moN7LEwEU9I+TUzHFNWKFq/F4CshJNbc5yDyqd/7gNhz0PiK4jX7haTyekh6ZE28ZJ3XXeeqawDYqQwAFAILphHCHTgxrpiJhZRNHf+TkvCyf0D56KndHRqgvV+l4+U7IyhVX/VWV1VW3l0g+a1jnVeegfe4cPxenfPl9cnKC228F1e3PyUZBdQOVcrLnMp/O52pu+oLpTedDZ3T8PP04ejSEHalj9mgN7TSe9sN92u8OAG4a3JRHoXwiuI1KEEABgBroBHSJTkAbpSKp7Eszd6cpPK1g1W5WC6qBE3pel4pEllXyFBJnm/eFvANqV6cil8QfPCWi4rbwgidV+iZBXcW8LPgjQXX7PfzPVP9qXUC3PI2n7gDK5JrbG2ReseUdwW34O+3bwW30Gy9pvHEq8udF8Q2mRbpNxl4nAigAUBOdFE7RycE/NHwBF3kS6ge+K76i9slvcndkLOrXXTpWXqr23BQzVNjTSNYty1RlvgyPyLmnLH7sHBO3pSIB4RMB/RgK2nd/0v79jB7+TOXluftToytS4LLg5fz4c/TwM1FtABW7ROWuVCyVXQcHxs+pqa1hsFsNbRzPiKEXc4J/fdefnYqE51Gco8/HN8fCCx0hgAIANdJJaLJOQs6BcVwa3u9g5wJZRfsiehnUnqh/N+tYecUGT7lxjpbooJdzZSyUxk447NWTHFBxvphflOV69TFixaCBpH11+ahRYMMQwHSQcoMalgV3gIYACvqCPw/6HpishxNravJktfmvmtoaaDpuXqnlvcHN+PrsoOA2+tWBKTaAYp/VcfaoyT8Ft9OTYb14B4BsdGI4USeIx1KR0G6W3P2p2R9SMW3nD7k70g7PmU9FAlgnUfMc4Pdk6orvzLyxLJ8s/9+/1a+fp2IaxU/6YdhrbtpHJ2ufefTPd1KxTweVfwSsq9f75xra8h19B/ZyfTaATnlFHOekqiOQyuo71alj9MmZ/XJ9UjcneNX580o9/HBgM45NfCkVK1g2FgEUAMhAJ6KzdSJaJhVLi86Xuz81uVTlM2VQovHKH9peQcl3XJZLzQt2eSrKsmU5QP29Q9vYOA+IAAAU0UlEQVQfqJykffzHjP1qNO2bQ8tRYIelwQ2ibKPXeUEdDakdj47yj1GmKaAv6D17t96zF+nhSsFN/brpIy37hY7X0qk4D0c7oIY2+plHoUQGUGxjHW+vWPhAcDtdI4ACAJno5HCDThIejnpKqufCIBcn7/SQ2D2avvKMjofPi6unItu8L677adWkt6vsrTJRr8OjAr6pcr5/4ObtVvNonxyhfeSVn45Jg5cTZWe/vjobVHvnan86YLNKne0CPXAusugACqNPqlPH6JML9V32qxra6WfnqTh33cKBbXiZ8e1TPce8KwRQACAjnawf1A+PFfVwz7IM2vfyfSqb6XWen7sj49ExcP6RrVS2UXlD5u70yqMqPlqW2/TaJml7Sg25MPqK9sfx2jdO1nuGypy5+1MBj6rZpu7gyShbqNyUBmNfYvB59KdXgota2tx5T04Oqnuo6HvaP9brWN52/xra6GvliEPfEJsc3NRWamf/pi4lPWgX6gDQd3SC8A+fvXWy+EkqkssukrlLVXHOkO2aPGVH+9wjTHynY5cUdyGd07tScRx20mvdXsfiitwdahLtj4u1X5bUw9NSfHLCSP6MraPX89NcHSinRWySioDUMCTpRR/T+/Wfer96yuO2QU2cw9L0ldk1xU+3vE7H69LgNgaFR01/LcXebJo9FXlQ9gtso2sEUACgIXTyvl4XdEvo4c7+z9S8nBvt8rK7W+v1XJK7I+PRvvYIjcNTMfVl0DnB52V6zUdr+yWWRH6B9sWd2i8fTMUF4Q4qL83cpU45KLa+Axi5O6I+/Ej70t9f38zdF6ANnsYTFUCZHFTvUNH3yQLarF1DU4w+aZNXldJx+ZYefiO4qe3cjtp7OridjhFAAYAG0Yni39p8XSeN47XdV2WD1D/f1U745Rwc32tyrhPt21ek4q6GR54M051y38HzNIsVtA/WdsAud4eawnejtfmS9otHT3hZ3sUzd6kdXsnL0/4Ob9L0LPXlW9qP/h77duq/YBSGSLlU/S/1cMmKq75fpZYkzkPAAdnoa6DbUzGlC+1z/jCff+YKbOPVqchHd2hgG13pl4tyABgqurBz7pBNdXH39VScpNZJzU12ea/KwSpHNfFOwWjan56mc5bKh3L3JaO3qlyhfbG5l9TO3Zkm0f64tpzSs7HKXqmZ+XAcnJy6DGtTVykoVzryUqAnqMybuz/AODwKpeoAyklNvonQL/Qd4h/Qm9TQ1IFNCkL3A49i1fFxvq0vBze1s9o5qry52Bh1BlA+qh3gtbXvqrFNAOhr+s70j5CN9P3pIIoTnPpi4lV5e/W8a1NxZ+B0D+nM3ZlWtA9fq81lKu/I3JUmcJZ7J1F9jY7dwbk70yRlTqLvad/4x/9GqZjW886snSr8IxX5bA4uvxcazcsol8kf/f5aL+UZ7eVROp6adXGGttEfTk3FlLNXVlgnq+9Uw6NEqzwuY3Ei8R8EtzGovqOyYyquJ6K8KRVTuE4IbKNjdQZQVlW5o0zYdIBOrLfX2DYwrJznoJEZrPtII+5K6DvTJ/ld9R36VW0/qbKuysoqr6i5K86z4GkOk9WnW2puu2vabx5memEieDKap/QcpH3znI5lFXOZfcc14vsmy6imMih4tPaPhyovk4qhxGuozFpzV25W8ZS+45q6IsH0qL8PabOh9qF/oHpJyjVTPSPpPILvMJUj1IdHuqzDAauI/f1sQJ2j+fNSdb8HNmeSP1N6f3r6xucqqvJa1XlbRXUNLR0T54DzyNvo77yD+uEGUBOVq0j6/Lh+cFObq50TvQJQB3/j903UuebZuqfw+IVspLJBOc94P+2Mm2ruA9qg4+ML67ovElExfb5WzN0HVKvM1XC6iz6nc2i7UlmcEPXNAU36Yty5MnwH13O6r+/wJJad9pPvfPvuxaK5+9JQDqLcq+N6ai+V6O8vSrHzobMo3++XpSIJry/qV1FZLRUBzPkCmvTF/HWp+LxNUft3BLRRq/Jab22PeErFjyIHUpZK1d7IG8k74ffxxeVIoq7p79eppFc1U789YmqH3P3oM1VO1WP0SQX0PnYAc4Hc/cD4dJychDkqEXPX1K9XR9Zf2YnLc8d0YnxQD1/TxtN9MbuWygT9zXmpCKRcU1Vf0L3yB5kjiVuqLJy5OwDGoe9ND0+fUpaRKSr+UeJAgaccONfFAqlIxNVq+PxTqRhd8meV36ViJZ0bXQbg7oyHAa+euxMN5oD5sXr/3Kxj/dvcnWmy8qL+zLL4M/cWbT6QilWOvPy4Rzg5qNLu9ZUDon9UuaUsnhb3c7XzVLU9bwbfsdTGqzd8q7ze8PeVl4/2vvO+XCAVOVPGSz7rwIiDJQ4seTTzDanYb7/tt+Au8tP78I3afLCi6nzDoadANIDmq3oEiqOuu3XwfF+0+aJ2dX2BebnLSU1f9nJQaf97xYGtUjEtoF+XTgWGWplQ8sdpmmzy5QiMOVXmVpkxFd/9nprkwIgv+P4+qD/Y9No9Kmef3P3oAzOrnKT99X6SH7ZP+8rBD5eTRv6f9qE/X69TccJij8jxOdVzxH3N48+bP2seXuxpeQ8N64/+MgB8YVmep/3n4Im/q2ZLxX4b+b56JhVTSf7W6+gSYBTf0H1JRXX9qHxfAxhgVQdQJqq8KxXz8zu1vItOnL6L4OUlzxnWi4q6aF/7wsQnDgdOlu7wz5+pvkcAIpTZ5R8uy7CZlAgKt8ujKLZOxfKz6FIZgPpLWdChMjjyUFmAaFXlPrHJFdYFoKEqDaCUc/PX0A9zJ1vzskYrdVGNh3P67ulNqseBlDNYWqpa2q9v02aLVCzTOE+X1VxfXY8AoHr6rltIm8/m7kcqkqt66oLvTD6ZigD0yCgP5wbznXaPVPAU2Mhs9u3YU/vtWC9RmLkfABBK33ULpmIKWRU8BZbVnoAhEJJEVhdel2tzuYcC+z9VPp46Hx73bpXTVG5XPQdo+4OmrQHdT8ohxZ4u5cCJA1u9LCfojPZfr6JfABDIo+vqXjr1TypXpiLIfKvK71UeaHfKgb6rPXXBQW7n0lgyvZBfY7ycEFVyUH1zlSpW5QGAJqty9MkJ3PAFhkPoKjz6InEW+U/qgtDJwXZPxZ3ATi8CnQjRuVUmqp4DU7GMH9NH2lQmldxM5fMqb+yxOk+p+qnKTuW8bwBoJH33OdfLujU156DJZJXT9d14ey8V6e89zeq6spzo/6fX4qCGp8aup7Jsqm6+/vRs4WVnmUYLYMBVFUDxd+XkiuoC0HC1LGOsizDfhVtXF2QTU5Fk1qu8dLo285tUDkvF8OJvanuk6n2y0o4OiHIJYk+j8t3XT6Xe18H2iBMHsbzPf99jXQBQh4+k+CV171TZMxVLzYYltVTdf9fmOJfyhsS+KmtEtSce1u7RL0zVBDCQ9F26mDYLVVTdlfqevrOiugA0XC0BlBHlj+9Ny0DKzqkYFfHKDqtxZvuDVHZTPd/R9ruq95FKO9qnyiUBN0zFEsTvqqBK3wE9QuU07eOnK6gPAOqycnD9R6vsUPfqReUNiU/p+35CKgLbUQlyP5YIoAAYXGtXWNf3K6wLQMPVGkAZoQtAZ6bfrkwSu2MqRkrM1mE1HtK8t8pOqudwbb+teh+stqf9Qa9/iVTsQ58Mer2Y9o+Bk1Mx2uSGXvsGAJm8L7DuvfX9ODGw/pbU/hR999+vhz9LMYlnPxJQJwBkV+YF3KCi6jwa/oyK6gLQB7IEUEaUAY9dyySx26psozJ3h9XMnoppQQ7IHKPtwWWAZqDptXrkjnPKOClsp0sQj8Xz9o9SOZ4RPQAGwOJB9Z6diuB9dvquvkrnAp//IpYdfk9AnQDQBB7B9/qK6ppCSgFguGQNoIwok+Y5Sayz/nskxQ4qr+2wGgcUHITZUvWcoO2Bg5ivo6IliEd4VSMvGe1pOpeSMBDAIND35JzazBFQtVdY2LFh35XODbaTyvwV1zun9uNrhnVkJ4DBpO81L2bx5QqrnFxhXQD6QCMCKCN0ofaENgfqy+272m6aijwpb+qwGq+84FVnNlY9Xga575fbrXgJYrtX5Xsqx2if39tjXQDQNG8IqveqpiUKVH/+o3OEh4/vEFD9fCoEUAAMEudfXKSiuv6QimXrAQyRRgVQRpQJSw/VRaGnlHjFHg9RXrDDahxhXicVS5T1awLU2bUPvMJDVUsQX5yK0SZn+6K7184BQEPNGlTvtUH19uqXQfVGr2IEALXRNfUC2hxQYZXHN2xEIoAaNDKAMkJfSp5i4mUbj0/FfMXdVRbrsBqP1ohapSDaomXpBUsQAxg2na7u1q6/BtXbq78F1fuKoHoBoFb6LeHA+g9TkTuxCp7SeXxFdQHoI40OoIzQD/9ntTm1nJLzcf8vlffn7VXjsQQxgGH1bFC9TQ0oRPWLkYoA+p5+PzgnlhOAV5kc++JhWLQCwP/qiwDKiHKYnL8Az9aXoXOBeETKslk71Sz/UDklsQQxgOEWtSJCp1NJ6/LmoHqfCKoXAMak6/uXlyPQq6rPN1xPVHl7VXWWv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