notebooks/feedforward-typing-using-smarts-and-neighbor-features-student-t+cha-df=7-and-per-particle-psis-big-batch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:14.106963Z",
     "start_time": "2019-03-07T03:51:14.104288Z"
    }
   },
   "outputs": [],
   "source": [
    "from bayes_implicit_solvent.continuous_parameter_experiments.gradient_free import mols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:14.273778Z",
     "start_time": "2019-03-07T03:51:14.271060Z"
    }
   },
   "outputs": [],
   "source": [
    "all_mols = mols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:14.420690Z",
     "start_time": "2019-03-07T03:51:14.417624Z"
    }
   },
   "outputs": [],
   "source": [
    "from bayes_implicit_solvent.constants import beta\n",
    "def unreduce(value):\n",
    "    \"\"\"Input value is in units of kB T, turn it into units of kilocalorie_per_mole\"\"\"\n",
    "    return value / (beta * unit.kilocalorie_per_mole)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:14.562226Z",
     "start_time": "2019-03-07T03:51:14.559778Z"
    }
   },
   "outputs": [],
   "source": [
    "from autograd import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:16.519810Z",
     "start_time": "2019-03-07T03:51:14.703120Z"
    }
   },
   "outputs": [],
   "source": [
    "mols = []\n",
    "    \n",
    "from bayes_implicit_solvent.utils import get_charges\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "from simtk import unit\n",
    "elements = []\n",
    "charges = []\n",
    "distance_matrices = []\n",
    "expt_means = []\n",
    "expt_uncs = []\n",
    "vacuum_trajs = []\n",
    "for mol in all_mols:\n",
    "    \n",
    "    expt_mean = unreduce(mol.experimental_value)\n",
    "    expt_unc = unreduce(mol.experimental_uncertainty)\n",
    "    \n",
    "    if (expt_mean > -15) and (expt_mean < 5):\n",
    "        \n",
    "        mols.append(mol)\n",
    "        expt_means.append(expt_mean)\n",
    "        expt_uncs.append(expt_unc)\n",
    "\n",
    "        elements.append(np.array([a.element.atomic_number for a in mol.top.atoms()]))\n",
    "        charges.append(get_charges(mol.sys))\n",
    "        distance_matrices.append([squareform(pdist(snapshot / unit.nanometer)) for snapshot in mol.vacuum_traj])\n",
    "expt_means = np.array(expt_means)\n",
    "expt_uncs = np.array(expt_uncs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:16.528823Z",
     "start_time": "2019-03-07T03:51:16.522664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_mols[0].vacuum_traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:16.534892Z",
     "start_time": "2019-03-07T03:51:16.530744Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "631\n"
     ]
    }
   ],
   "source": [
    "print(len(mols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:16.541231Z",
     "start_time": "2019-03-07T03:51:16.538811Z"
    }
   },
   "outputs": [],
   "source": [
    "from bayes_implicit_solvent.typers import SMARTSTyper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:43.778929Z",
     "start_time": "2019-03-07T03:51:16.544199Z"
    }
   },
   "outputs": [],
   "source": [
    "typer = SMARTSTyper(['[a]', '[A]', '[D1]', '[D2]', '[D3]', '[D4]', '[H0]', '[H1]', '[H2]', '[H3]', '[H4]', '[R0]', '[R2]', '[R3]', '[r0]', '[r3]', '[r4]', '[r5]', '[r6]', '[v1]', '[v2]', '[v3]', '[v4]', '[v5]', '[v6]', '[X1]', '[X2]', '[X3]', '[X4]', '[x0]', '[x2]', '[x3]', '[-1]', '[#1]', '[#6]', '[#7]', '[#8]', '[#9]', '[#15]', '[#16]', '[#17]', '[#35]', '[#53]', '*~[a]', '*~[A]', '*~[D1]', '*~[D2]', '*~[D3]', '*~[D4]', '*~[H0]', '*~[H1]', '*~[H2]', '*~[H3]', '*~[H4]', '*~[R0]', '*~[R2]', '*~[R3]', '*~[r0]', '*~[r3]', '*~[r4]', '*~[r5]', '*~[r6]', '*~[v1]', '*~[v2]', '*~[v3]', '*~[v4]', '*~[v5]', '*~[v6]', '*~[X1]', '*~[X2]', '*~[X3]', '*~[X4]', '*~[x0]', '*~[x2]', '*~[x3]', '*~[-1]', '*~[#1]', '*~[#6]', '*~[#7]', '*~[#8]', '*~[#9]', '*~[#15]', '*~[#16]', '*~[#17]', '*~[#35]', '*~[#53]', '*-*', '*-[a]', '*-[A]', '*-[D1]', '*-[D2]', '*-[D3]', '*-[D4]', '*-[H0]', '*-[H1]', '*-[H2]', '*-[H3]', '*-[H4]', '*-[h0]', '*-[R0]', '*-[R2]', '*-[R3]', '*-[r0]', '*-[r3]', '*-[r4]', '*-[r5]', '*-[r6]', '*-[v1]', '*-[v2]', '*-[v3]', '*-[v4]', '*-[v5]', '*-[v6]', '*-[X1]', '*-[X2]', '*-[X3]', '*-[X4]', '*-[x0]', '*-[x2]', '*-[x3]', '*-[-1]', '*-[#1]', '*-[#6]', '*-[#7]', '*-[#8]', '*-[#9]', '*-[#15]', '*-[#16]', '*-[#17]', '*-[#35]', '*-[#53]', '*=*', '*=[a]', '*=[A]', '*=[D1]', '*=[D2]', '*=[D3]', '*=[D4]', '*=[H0]', '*=[H1]', '*=[H2]', '*=[h0]', '*=[R0]', '*=[R2]', '*=[r0]', '*=[r5]', '*=[r6]', '*=[v2]', '*=[v3]', '*=[v4]', '*=[v5]', '*=[v6]', '*=[X1]', '*=[X2]', '*=[X3]', '*=[X4]', '*=[x0]', '*=[x2]', '*=[#6]', '*=[#7]', '*=[#8]', '*=[#15]', '*=[#16]', '*#*', '*#[A]', '*#[D1]', '*#[D2]', '*#[H0]', '*#[H1]', '*#[h0]', '*#[R0]', '*#[r0]', '*#[v3]', '*#[v4]', '*#[X1]', '*#[X2]', '*#[x0]', '*#[#6]', '*#[#7]', '*:*', '*:[a]', '*:[D2]', '*:[D3]', '*:[H0]', '*:[H1]', '*:[h0]', '*:[R2]', '*:[R3]', '*:[r5]', '*:[r6]', '*:[v2]', '*:[v3]', '*:[v4]', '*:[X2]', '*:[X3]', '*:[x2]', '*:[x3]', '*:[#6]', '*:[#7]', '*:[#16]', '*@*', '*@[a]', '*@[A]', '*@[D2]', '*@[D3]', '*@[D4]', '*@[H0]', '*@[H1]', '*@[H2]', '*@[h0]', '*@[R2]', '*@[R3]', '*@[r3]', '*@[r4]', '*@[r5]', '*@[r6]', '*@[v2]', '*@[v3]', '*@[v4]', '*@[v6]', '*@[X2]', '*@[X3]', '*@[X4]', '*@[x2]', '*@[x3]', '*@[#6]', '*@[#7]', '*@[#8]', '*@[#16]'])\n",
    "computed_smarts_features = [typer.get_matches(mol.mol) for mol in mols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:43.796082Z",
     "start_time": "2019-03-07T03:51:43.781420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3599, 11433)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(np.hstack(charges))), len(np.hstack(charges))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:43.803197Z",
     "start_time": "2019-03-07T03:51:43.798878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = len(computed_smarts_features[0][0])\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:43.807651Z",
     "start_time": "2019-03-07T03:51:43.805245Z"
    }
   },
   "outputs": [],
   "source": [
    "computed_features = computed_smarts_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:43.814346Z",
     "start_time": "2019-03-07T03:51:43.809693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = len(computed_features[0][0])\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.584964Z",
     "start_time": "2019-03-07T03:51:43.817160Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "664"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_types = np.array(sorted(list(set([tuple(f) for f in np.vstack(computed_features)]))))\n",
    "len(all_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.855622Z",
     "start_time": "2019-03-07T03:51:44.587665Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.865794Z",
     "start_time": "2019-03-07T03:51:44.857493Z"
    }
   },
   "outputs": [],
   "source": [
    "from autograd.scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.875638Z",
     "start_time": "2019-03-07T03:51:44.870201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(distance_matrices[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.886279Z",
     "start_time": "2019-03-07T03:51:44.880342Z"
    }
   },
   "outputs": [],
   "source": [
    "N_atoms = np.array(list(map(len, charges)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.892687Z",
     "start_time": "2019-03-07T03:51:44.888559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "631"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.899700Z",
     "start_time": "2019-03-07T03:51:44.894876Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_features = N\n",
    "n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.908390Z",
     "start_time": "2019-03-07T03:51:44.902404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.15    , 0.8     , 0.      , 0.8     , 0.      , 2.909125])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_radius = 0.15\n",
    "default_scale = 0.8\n",
    "default_log_tau = 0.0\n",
    "default_psi1_coeff = 0.8\n",
    "default_psi2_coeff = 0.0\n",
    "default_psi3_coeff = 2.909125\n",
    "\n",
    "baseline_bias = np.array([default_radius, default_scale, default_log_tau, default_psi1_coeff, default_psi2_coeff, default_psi3_coeff])\n",
    "baseline_bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.915396Z",
     "start_time": "2019-03-07T03:51:44.910197Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.5399929762484854e-05, 2.718281828459045)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(-10), np.exp(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.920124Z",
     "start_time": "2019-03-07T03:51:44.917146Z"
    }
   },
   "outputs": [],
   "source": [
    "def relu(x):\n",
    "    return x * (x > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.946096Z",
     "start_time": "2019-03-07T03:51:44.922037Z"
    }
   },
   "outputs": [],
   "source": [
    "import autograd.numpy.random as npr\n",
    "\n",
    "class NeuralNet():\n",
    "\n",
    "    def __init__(self, layer_sizes, scale=0.01):\n",
    "        assert(layer_sizes[-1] == len(baseline_bias))\n",
    "        self.layer_sizes = layer_sizes\n",
    "        self.scale = scale\n",
    "        self.params = self.init_random_params(scale, layer_sizes)\n",
    "\n",
    "    def init_random_params(self, scale, layer_sizes, rs=npr.RandomState(0)):\n",
    "        return [(scale * rs.randn(m, n),  # weight matrix\n",
    "                 scale * rs.randn(n))  # bias vector\n",
    "                for m, n in zip(layer_sizes[:-1], layer_sizes[1:])]\n",
    "\n",
    "    def neural_net_predict(self, params, inputs):\n",
    "        for W, b in params:\n",
    "            outputs = np.dot(inputs, W) + b\n",
    "            inputs = relu(outputs)\n",
    "        return outputs + baseline_bias\n",
    "\n",
    "    def __call__(self, inputs):\n",
    "        return self.neural_net_predict(self.params, inputs)\n",
    "    \n",
    "# also produce a tau parameter for each \n",
    "npr.seed(0)\n",
    "layer_sizes = [n_features, 1000, 100, len(baseline_bias)]\n",
    "neural_net = NeuralNet(layer_sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.950052Z",
     "start_time": "2019-03-07T03:51:44.947685Z"
    }
   },
   "outputs": [],
   "source": [
    "params = neural_net.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.961579Z",
     "start_time": "2019-03-07T03:51:44.952279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.34565705e-01,  7.80200599e-01,  1.04895461e-02,\n",
       "         8.02188311e-01,  1.19935530e-04,  2.91574462e+00],\n",
       "       [ 1.34837788e-01,  7.80075805e-01,  9.96969475e-03,\n",
       "         8.01451753e-01,  4.46502372e-04,  2.91676114e+00],\n",
       "       [ 1.34694812e-01,  7.80126841e-01,  1.04529614e-02,\n",
       "         8.02187055e-01,  2.14175222e-04,  2.91638025e+00],\n",
       "       [ 1.34990493e-01,  7.79836409e-01,  1.02714158e-02,\n",
       "         8.01379370e-01, -3.50431226e-04,  2.91573596e+00],\n",
       "       [ 1.34181397e-01,  7.80190880e-01,  1.03400607e-02,\n",
       "         8.00826457e-01,  4.76846627e-04,  2.91636436e+00],\n",
       "       [ 1.34847141e-01,  7.80019862e-01,  1.01094741e-02,\n",
       "         8.02578552e-01, -8.76196827e-04,  2.91636139e+00],\n",
       "       [ 1.35633877e-01,  7.80492094e-01,  1.01628339e-02,\n",
       "         8.01289625e-01,  4.92504197e-04,  2.91693721e+00],\n",
       "       [ 1.35633877e-01,  7.80492094e-01,  1.01628339e-02,\n",
       "         8.01289625e-01,  4.92504197e-04,  2.91693721e+00],\n",
       "       [ 1.34482733e-01,  7.80266367e-01,  1.00469619e-02,\n",
       "         8.01120749e-01,  3.99295481e-04,  2.91561740e+00],\n",
       "       [ 1.34847141e-01,  7.80019862e-01,  1.01094741e-02,\n",
       "         8.02578552e-01, -8.76196827e-04,  2.91636139e+00],\n",
       "       [ 1.35633877e-01,  7.80492094e-01,  1.01628339e-02,\n",
       "         8.01289625e-01,  4.92504197e-04,  2.91693721e+00],\n",
       "       [ 1.35633877e-01,  7.80492094e-01,  1.01628339e-02,\n",
       "         8.01289625e-01,  4.92504197e-04,  2.91693721e+00],\n",
       "       [ 1.35214988e-01,  7.80668457e-01,  1.04257700e-02,\n",
       "         8.01996518e-01,  7.00323565e-04,  2.91713558e+00],\n",
       "       [ 1.35214988e-01,  7.80668457e-01,  1.04257700e-02,\n",
       "         8.01996518e-01,  7.00323565e-04,  2.91713558e+00],\n",
       "       [ 1.35214988e-01,  7.80668457e-01,  1.04257700e-02,\n",
       "         8.01996518e-01,  7.00323565e-04,  2.91713558e+00],\n",
       "       [ 1.34529089e-01,  7.80276715e-01,  1.03435224e-02,\n",
       "         8.01536328e-01,  6.13245126e-04,  2.91710606e+00],\n",
       "       [ 1.34529089e-01,  7.80276715e-01,  1.03435224e-02,\n",
       "         8.01536328e-01,  6.13245126e-04,  2.91710606e+00]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural_net(computed_features[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.967698Z",
     "start_time": "2019-03-07T03:51:44.964167Z"
    }
   },
   "outputs": [],
   "source": [
    "#from bayes_implicit_solvent.gb_models.numpy_gb_models import compute_OBC_energy_vectorized\n",
    "from bayes_implicit_solvent.gb_models.modular_gb_models import cha_gb_no_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.979876Z",
     "start_time": "2019-03-07T03:51:44.969814Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.13456571, 0.13483779, 0.13469481, 0.13499049, 0.1341814 ,\n",
       "        0.13484714, 0.13563388, 0.13563388, 0.13448273, 0.13484714,\n",
       "        0.13563388, 0.13563388, 0.13521499, 0.13521499, 0.13521499,\n",
       "        0.13452909, 0.13452909]),\n",
       " array([0.7802006 , 0.7800758 , 0.78012684, 0.77983641, 0.78019088,\n",
       "        0.78001986, 0.78049209, 0.78049209, 0.78026637, 0.78001986,\n",
       "        0.78049209, 0.78049209, 0.78066846, 0.78066846, 0.78066846,\n",
       "        0.78027672, 0.78027672]),\n",
       " array([0.01048955, 0.00996969, 0.01045296, 0.01027142, 0.01034006,\n",
       "        0.01010947, 0.01016283, 0.01016283, 0.01004696, 0.01010947,\n",
       "        0.01016283, 0.01016283, 0.01042577, 0.01042577, 0.01042577,\n",
       "        0.01034352, 0.01034352]),\n",
       " array([0.80218831, 0.80145175, 0.80218705, 0.80137937, 0.80082646,\n",
       "        0.80257855, 0.80128962, 0.80128962, 0.80112075, 0.80257855,\n",
       "        0.80128962, 0.80128962, 0.80199652, 0.80199652, 0.80199652,\n",
       "        0.80153633, 0.80153633]),\n",
       " array([ 0.00011994,  0.0004465 ,  0.00021418, -0.00035043,  0.00047685,\n",
       "        -0.0008762 ,  0.0004925 ,  0.0004925 ,  0.0003993 , -0.0008762 ,\n",
       "         0.0004925 ,  0.0004925 ,  0.00070032,  0.00070032,  0.00070032,\n",
       "         0.00061325,  0.00061325]),\n",
       " array([2.91574462, 2.91676114, 2.91638025, 2.91573596, 2.91636436,\n",
       "        2.91636139, 2.91693721, 2.91693721, 2.9156174 , 2.91636139,\n",
       "        2.91693721, 2.91693721, 2.91713558, 2.91713558, 2.91713558,\n",
       "        2.91710606, 2.91710606]))"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radii, scales, log_taus, psi_coeffs, psi2_coeffs, psi3_coeffs = neural_net.neural_net_predict(params, computed_features[0]).T\n",
    "radii, scales, log_taus, psi_coeffs, psi2_coeffs, psi3_coeffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.986835Z",
     "start_time": "2019-03-07T03:51:44.982144Z"
    }
   },
   "outputs": [],
   "source": [
    "npr.seed(0)\n",
    "all_inds = np.arange(len(mols))\n",
    "np.random.shuffle(all_inds)\n",
    "train_inds = all_inds[:470]\n",
    "test_inds = all_inds[470:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:44.994802Z",
     "start_time": "2019-03-07T03:51:44.989781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "473.25"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_inds) * 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:45.003177Z",
     "start_time": "2019-03-07T03:51:44.997250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.1382    ,  0.1522    ,  0.1023    ,  0.1233    , -0.3511    ,\n",
       "        0.37990001, -0.1655    , -0.1655    , -0.37040001,  0.3845    ,\n",
       "       -0.1723    , -0.1723    ,  0.0681    ,  0.0681    ,  0.0681    ,\n",
       "        0.0944    ,  0.0944    ])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charges[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:45.011725Z",
     "start_time": "2019-03-07T03:51:45.005301Z"
    }
   },
   "outputs": [],
   "source": [
    "from autograd.scipy.misc import logsumexp\n",
    "from simtk import unit\n",
    "from bayes_implicit_solvent.constants import kB, temperature\n",
    "\n",
    "kj_mol_to_kT = 1.0 * unit.kilojoule_per_mole / (kB * temperature)\n",
    "\n",
    "def one_sided_exp(w_F):\n",
    "    DeltaF = - (logsumexp(- w_F) - np.log(len(w_F)))\n",
    "    return DeltaF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:45.026170Z",
     "start_time": "2019-03-07T03:51:45.014157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_index = dict(zip([tuple(t) for t in all_types], range(len(all_types))))\n",
    "type_index[tuple(computed_features[0][0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:45.176360Z",
     "start_time": "2019-03-07T03:51:45.028373Z"
    }
   },
   "outputs": [],
   "source": [
    "type_indices = []\n",
    "for i in range(len(computed_features)):\n",
    "    type_indices.append(np.array([type_index[tuple(f)] for f in computed_features[i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:45.182667Z",
     "start_time": "2019-03-07T03:51:45.177985Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.alltrue(all_types[type_indices[0]] == computed_features[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:45.193536Z",
     "start_time": "2019-03-07T03:51:45.184992Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_predictions(params, inds=train_inds, batch_size=25, randomized=True):\n",
    "    all_radii, all_scales, all_log_taus, all_psi_coeffs, all_psi2_coeffs, all_psi3_coeffs = neural_net.neural_net_predict(params, all_types).T\n",
    "    \n",
    "    \n",
    "    predictions = []\n",
    "    for i in inds:\n",
    "        radii, scales, log_taus, psi_coeffs, psi2_coeffs, psi3_coeffs = all_radii[type_indices[i]], all_scales[type_indices[i]], all_log_taus[type_indices[i]], all_psi_coeffs[type_indices[i]], all_psi2_coeffs[type_indices[i]], all_psi3_coeffs[type_indices[i]]\n",
    "        #radii, scales, log_taus = neural_net.neural_net_predict(params, computed_features[i]).T\n",
    "        taus = np.exp(log_taus)\n",
    "        \n",
    "        if randomized:\n",
    "            dmat_inds = np.random.randint(0, len(distance_matrices[i]), batch_size)\n",
    "        else:\n",
    "            dmat_inds = np.arange(min(batch_size, len(distance_matrices[i])))\n",
    "        W_F = np.array([cha_gb_no_threshold(distance_matrices[i][j], radii, scales, taus, charges[i], psi_coefficient=psi_coeffs, psi2_coefficient=psi2_coeffs, psi3_coefficient=psi3_coeffs) for j in dmat_inds])\n",
    "        \n",
    "        w_F = W_F * kj_mol_to_kT\n",
    "        pred_free_energy = unreduce(one_sided_exp(w_F))\n",
    "        predictions.append(pred_free_energy)\n",
    "    return np.array(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:48.826157Z",
     "start_time": "2019-03-07T03:51:45.195825Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions = make_predictions(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:48.833500Z",
     "start_time": "2019-03-07T03:51:48.827728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8.63061487058597"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def log_prior(params):\n",
    "    return - sum([np.linalg.norm(W) + np.linalg.norm(b) for (W,b) in params])\n",
    "log_prior(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:48.842079Z",
     "start_time": "2019-03-07T03:51:48.836747Z"
    }
   },
   "outputs": [],
   "source": [
    "from autograd.scipy.stats import t as student_t\n",
    "\n",
    "def log_likelihood(params):\n",
    "    predictions = make_predictions(params)\n",
    "    return np.sum(student_t.logpdf(predictions, loc=expt_means[train_inds], scale=expt_uncs[train_inds], df=7))\n",
    "    #return np.sum(norm.logpdf(predictions, loc=expt_means[train_inds], scale=expt_uncs[train_inds]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:48.847794Z",
     "start_time": "2019-03-07T03:51:48.844611Z"
    }
   },
   "outputs": [],
   "source": [
    "def log_posterior(params):\n",
    "    return log_prior(params) + log_likelihood(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:49.058520Z",
     "start_time": "2019-03-07T03:51:48.850142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a296e87f0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(predictions, expt_means[train_inds])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:51:52.887957Z",
     "start_time": "2019-03-07T03:51:49.060936Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3509.5638440842367"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_posterior(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:52:59.101921Z",
     "start_time": "2019-03-07T03:51:52.889604Z"
    }
   },
   "outputs": [],
   "source": [
    "from autograd import grad\n",
    "g = grad(log_posterior)(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T03:52:59.108572Z",
     "start_time": "2019-03-07T03:52:59.104128Z"
    }
   },
   "outputs": [],
   "source": [
    "def rmse(x, y):\n",
    "    squared_errors = (x - y)**2\n",
    "    mean_squared_errors = np.mean(squared_errors)\n",
    "    root_mean_squared_errors = np.sqrt(mean_squared_errors)\n",
    "    return root_mean_squared_errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:35:56.423815Z",
     "start_time": "2019-03-07T03:52:59.110336Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joshuafass/anaconda3/lib/python3.7/site-packages/autograd/numpy/numpy_vjps.py:444: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return lambda g: g[idxs]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0\n",
      "\ttrain RMSE = 9.230\n",
      "\ttest RMSE = 8.063\n",
      "Iteration 1\n",
      "\ttrain RMSE = 4.271\n",
      "\ttest RMSE = 4.380\n",
      "Iteration 2\n",
      "\ttrain RMSE = 4.030\n",
      "\ttest RMSE = 4.103\n",
      "Iteration 3\n",
      "\ttrain RMSE = 4.034\n",
      "\ttest RMSE = 4.028\n",
      "Iteration 4\n",
      "\ttrain RMSE = 4.557\n",
      "\ttest RMSE = 4.438\n",
      "Iteration 5\n",
      "\ttrain RMSE = 5.302\n",
      "\ttest RMSE = 5.041\n",
      "Iteration 6\n",
      "\ttrain RMSE = 4.731\n",
      "\ttest RMSE = 4.554\n",
      "Iteration 7\n",
      "\ttrain RMSE = 3.660\n",
      "\ttest RMSE = 3.572\n",
      "Iteration 8\n",
      "\ttrain RMSE = 3.077\n",
      "\ttest RMSE = 2.938\n",
      "Iteration 9\n",
      "\ttrain RMSE = 2.918\n",
      "\ttest RMSE = 2.745\n",
      "Iteration 10\n",
      "\ttrain RMSE = 2.833\n",
      "\ttest RMSE = 2.679\n",
      "Iteration 11\n",
      "\ttrain RMSE = 2.799\n",
      "\ttest RMSE = 2.655\n",
      "Iteration 12\n",
      "\ttrain RMSE = 2.689\n",
      "\ttest RMSE = 2.559\n",
      "Iteration 13\n",
      "\ttrain RMSE = 2.566\n",
      "\ttest RMSE = 2.450\n",
      "Iteration 14\n",
      "\ttrain RMSE = 2.467\n",
      "\ttest RMSE = 2.367\n",
      "Iteration 15\n",
      "\ttrain RMSE = 2.410\n",
      "\ttest RMSE = 2.333\n",
      "Iteration 16\n",
      "\ttrain RMSE = 2.429\n",
      "\ttest RMSE = 2.366\n",
      "Iteration 17\n",
      "\ttrain RMSE = 2.297\n",
      "\ttest RMSE = 2.269\n",
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      "\ttrain RMSE = 2.152\n",
      "\ttest RMSE = 2.174\n",
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      "\ttrain RMSE = 2.078\n",
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      "\ttrain RMSE = 2.045\n",
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      "\ttrain RMSE = 2.050\n",
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      "\ttrain RMSE = 1.923\n",
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      "\ttrain RMSE = 1.984\n",
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      "\ttrain RMSE = 0.949\n",
      "\ttest RMSE = 0.990\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 157\n",
      "\ttrain RMSE = 1.037\n",
      "\ttest RMSE = 1.112\n",
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      "\ttrain RMSE = 0.960\n",
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      "\ttrain RMSE = 0.896\n",
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      "\ttrain RMSE = 0.819\n",
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      "\ttrain RMSE = 0.796\n",
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      "\ttrain RMSE = 0.796\n",
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      "\ttrain RMSE = 0.788\n",
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      "\ttrain RMSE = 0.780\n",
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      "\ttrain RMSE = 0.819\n",
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      "\ttrain RMSE = 0.788\n",
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      "\ttrain RMSE = 0.757\n",
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      "\ttrain RMSE = 0.746\n",
      "\ttest RMSE = 0.935\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 312\n",
      "\ttrain RMSE = 0.740\n",
      "\ttest RMSE = 0.934\n",
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      "\ttrain RMSE = 0.729\n",
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      "\ttrain RMSE = 0.690\n",
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      "\ttrain RMSE = 0.683\n",
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      "\ttrain RMSE = 0.675\n",
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      "\ttrain RMSE = 0.659\n",
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      "\ttest RMSE = 0.982\n",
      "Iteration 418\n",
      "\ttrain RMSE = 0.664\n",
      "\ttest RMSE = 0.966\n",
      "Iteration 419\n",
      "\ttrain RMSE = 0.654\n",
      "\ttest RMSE = 0.969\n",
      "Iteration 420\n",
      "\ttrain RMSE = 0.677\n",
      "\ttest RMSE = 0.976\n",
      "Iteration 421\n",
      "\ttrain RMSE = 0.660\n",
      "\ttest RMSE = 0.993\n",
      "Iteration 422\n",
      "\ttrain RMSE = 0.661\n",
      "\ttest RMSE = 0.975\n",
      "Iteration 423\n",
      "\ttrain RMSE = 0.661\n",
      "\ttest RMSE = 0.976\n",
      "Iteration 424\n",
      "\ttrain RMSE = 0.655\n",
      "\ttest RMSE = 0.998\n",
      "Iteration 425\n",
      "\ttrain RMSE = 0.672\n",
      "\ttest RMSE = 0.999\n",
      "Iteration 426\n",
      "\ttrain RMSE = 0.651\n",
      "\ttest RMSE = 1.002\n",
      "Iteration 427\n",
      "\ttrain RMSE = 0.658\n",
      "\ttest RMSE = 0.993\n",
      "Iteration 428\n",
      "\ttrain RMSE = 0.673\n",
      "\ttest RMSE = 0.974\n",
      "Iteration 429\n",
      "\ttrain RMSE = 0.648\n",
      "\ttest RMSE = 0.972\n",
      "Iteration 430\n",
      "\ttrain RMSE = 0.650\n",
      "\ttest RMSE = 0.983\n",
      "Iteration 431\n",
      "\ttrain RMSE = 0.676\n",
      "\ttest RMSE = 0.995\n",
      "Iteration 432\n",
      "\ttrain RMSE = 0.669\n",
      "\ttest RMSE = 1.023\n",
      "Iteration 433\n",
      "\ttrain RMSE = 0.661\n",
      "\ttest RMSE = 0.985\n",
      "Iteration 434\n",
      "\ttrain RMSE = 0.647\n",
      "\ttest RMSE = 0.968\n",
      "Iteration 435\n",
      "\ttrain RMSE = 0.659\n",
      "\ttest RMSE = 0.973\n",
      "Iteration 436\n",
      "\ttrain RMSE = 0.665\n",
      "\ttest RMSE = 0.966\n",
      "Iteration 437\n",
      "\ttrain RMSE = 0.670\n",
      "\ttest RMSE = 0.977\n",
      "Iteration 438\n",
      "\ttrain RMSE = 0.661\n",
      "\ttest RMSE = 1.007\n",
      "Iteration 439\n",
      "\ttrain RMSE = 0.672\n",
      "\ttest RMSE = 0.991\n",
      "Iteration 440\n",
      "\ttrain RMSE = 0.656\n",
      "\ttest RMSE = 0.970\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-54-4ce3c4671e11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Iteration {}\\n\\ttrain RMSE = {:.3f}\\n\\ttest RMSE = {:.3f}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_rmse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_rmse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mtraj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_iters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/autograd/misc/optimizers.py\u001b[0m in \u001b[0;36m_optimize\u001b[0;34m(grad, x0, callback, *args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0m_callback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0munflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_grad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_x0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_callback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_optimize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/autograd/misc/optimizers.py\u001b[0m in \u001b[0;36madam\u001b[0;34m(grad, x, callback, num_iters, step_size, b1, b2, eps)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_iters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mb1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mg\u001b[0m      \u001b[0;34m+\u001b[0m \u001b[0mb1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mm\u001b[0m  \u001b[0;31m# First  moment estimate.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/autograd/misc/optimizers.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x, i)\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_optimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0m_x0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munflatten\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m         \u001b[0m_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0m_callback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-54-4ce3c4671e11>\u001b[0m in \u001b[0;36mgrad_loss\u001b[0;34m(x, i)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgrad_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/autograd/wrap_util.py\u001b[0m in \u001b[0;36mnary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0munary_operator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munary_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mnary_op_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnary_op_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnary_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnary_operator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from autograd.misc.optimizers import adam, sgd\n",
    "\n",
    "def loss(x):\n",
    "    return - log_posterior(x)\n",
    "\n",
    "def grad_loss(x, i):\n",
    "    return grad(loss)(x)\n",
    "\n",
    "\n",
    "traj = []\n",
    "prediction_traj = []\n",
    "def callback(x,i,g):\n",
    "    predictions = make_predictions(x, inds=np.arange(len(mols)), batch_size=50, randomized=False)\n",
    "    prediction_traj.append(predictions)\n",
    "    train_rmse = rmse(predictions[train_inds], expt_means[train_inds])\n",
    "    test_rmse = rmse(predictions[test_inds], expt_means[test_inds])\n",
    "    print('Iteration {}\\n\\ttrain RMSE = {:.3f}\\n\\ttest RMSE = {:.3f}'.format(i, train_rmse, test_rmse))\n",
    "    traj.append((x,i,g))\n",
    "result = adam(grad_loss, params, callback=callback, num_iters=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:02.261260Z",
     "start_time": "2019-03-07T12:35:58.688010Z"
    }
   },
   "outputs": [],
   "source": [
    "np.savez('feedforward_typing_march6_result_student_t_loss_with_cha_correction_df=7_with_neighbors_and_per_particle_psis_big_batch.npz',\n",
    "         prediction_traj=prediction_traj,\n",
    "         train_inds=train_inds,\n",
    "         test_inds=test_inds,\n",
    "         expt_means=expt_means,\n",
    "         expt_uncs=expt_uncs,\n",
    "         traj=traj[::10],\n",
    "         layer_sizes=layer_sizes,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:03.432260Z",
     "start_time": "2019-03-07T12:36:03.411353Z"
    }
   },
   "outputs": [],
   "source": [
    "train_rmses = [rmse(snapshot[train_inds], expt_means[train_inds]) for snapshot in prediction_traj]\n",
    "test_rmses = [rmse(snapshot[test_inds], expt_means[test_inds]) for snapshot in prediction_traj]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:04.303232Z",
     "start_time": "2019-03-07T12:36:04.135925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35883800317534864 0.6148984986562431\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MUE')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mue(x,y):\n",
    "    return np.mean(np.abs(x-y))\n",
    "\n",
    "train_mues = [mue(snapshot[train_inds], expt_means[train_inds]) for snapshot in prediction_traj]\n",
    "test_mues = [mue(snapshot[test_inds], expt_means[test_inds]) for snapshot in prediction_traj]\n",
    "\n",
    "plt.plot(train_mues, )\n",
    "plt.plot(test_mues)\n",
    "\n",
    "print(train_mues[-1], test_mues[-1])\n",
    "plt.xlabel('Adam iteration')\n",
    "plt.ylabel('MUE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:08.898354Z",
     "start_time": "2019-03-07T12:36:08.738151Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.03617901350936859 -0.07283700981756588\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'avg errors')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def average_error(x,y):\n",
    "    return np.mean(x-y)\n",
    "\n",
    "train_avg_errors = [average_error(snapshot[train_inds], expt_means[train_inds]) for snapshot in prediction_traj]\n",
    "test_avg_errors = [average_error(snapshot[test_inds], expt_means[test_inds]) for snapshot in prediction_traj]\n",
    "\n",
    "plt.plot(train_avg_errors, )\n",
    "plt.plot(test_avg_errors)\n",
    "\n",
    "print(train_avg_errors[-1], test_avg_errors[-1])\n",
    "plt.xlabel('Adam iteration')\n",
    "plt.ylabel('avg errors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:09.945681Z",
     "start_time": "2019-03-07T12:36:09.809142Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RMSE (kcal/mol)')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_rmses, )\n",
    "plt.plot(test_rmses)\n",
    "\n",
    "plt.hlines(2.425900278028874, 0, len(train_rmses), linestyles='--')\n",
    "\n",
    "plt.xlabel('Adam iteration')\n",
    "plt.ylabel('RMSE (kcal/mol)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:11.246803Z",
     "start_time": "2019-03-07T12:36:11.241997Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6558019089717088, 0.97020511440037)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rmses[-1], test_rmses[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:12.158333Z",
     "start_time": "2019-03-07T12:36:12.153614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6465405526590177, 0.9207564300293462)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(train_rmses), min(test_rmses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:14.700951Z",
     "start_time": "2019-03-07T12:36:12.448145Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b25103128>]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior_traj = np.array([log_prior(t[0]) for t in traj])\n",
    "plt.plot(prior_traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:15.546657Z",
     "start_time": "2019-03-07T12:36:15.481801Z"
    }
   },
   "outputs": [],
   "source": [
    "likelihood_traj = np.array([np.sum(norm.logpdf(predictions[train_inds], loc=expt_means[train_inds], scale=expt_uncs[train_inds])) for predictions in prediction_traj])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:16.577742Z",
     "start_time": "2019-03-07T12:36:16.442754Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b257fc630>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(likelihood_traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:17.752213Z",
     "start_time": "2019-03-07T12:36:17.612298Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b25089208>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(prior_traj + likelihood_traj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:18.591431Z",
     "start_time": "2019-03-07T12:36:18.423847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b253294e0>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((prior_traj + likelihood_traj)[150:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:19.281138Z",
     "start_time": "2019-03-07T12:36:19.273685Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-51.50729949663214, 6.906153451435357)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minval, maxval = np.min(prediction_traj[3:]), np.max(prediction_traj[3:])\n",
    "minval, maxval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:20.741375Z",
     "start_time": "2019-03-07T12:36:20.737154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-15.5, 5.5)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minval, maxval = -15.5, 5.5\n",
    "minval, maxval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:21.422022Z",
     "start_time": "2019-03-07T12:36:21.418799Z"
    }
   },
   "outputs": [],
   "source": [
    "train_color = 'lightblue'\n",
    "test_color = 'green'\n",
    "train_style = '--'\n",
    "test_style = '-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T13:07:14.746185Z",
     "start_time": "2019-03-07T13:02:33.547224Z"
    }
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "from bayes_implicit_solvent.utils import remove_top_right_spines\n",
    "diag = np.arange(minval, maxval)\n",
    "\n",
    "s = 10\n",
    "\n",
    "for t in range(len(prediction_traj)):\n",
    "    plt.figure(figsize=(6*1.5,3*1.5))\n",
    "    ax = plt.subplot(1,2,1)\n",
    "    remove_top_right_spines(ax)\n",
    "    \n",
    "    plt.plot(train_rmses[:t], c=train_color, linestyle=train_style, label='train (n={})'.format(len(train_inds)))\n",
    "    plt.plot(test_rmses[:t], c=test_color, linestyle=test_style, label='test (n={})'.format(len(test_inds)))\n",
    "    plt.hlines(2.425900278028874, 0, len(train_rmses), linestyles='-', label='OBC2 (n=631)')\n",
    "    plt.hlines(1.642, 0, len(train_rmses), linestyles='--', label='SMIRNOFF+TIP3P (n=642)')\n",
    "    plt.hlines(0.98, 0, len(train_rmses), linestyles='dotted', label='OPLS3e (n=418)')\n",
    "    \n",
    "    plt.ylabel('RMSE (kcal/mol)')\n",
    "    plt.xlabel('adam iterations')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.xlim(0, len(train_rmses))\n",
    "    plt.ylim(0, 4)\n",
    "    \n",
    "    plt.title('RMSE trace')\n",
    "    \n",
    "    ax = plt.subplot(1,2,2)\n",
    "    remove_top_right_spines(ax)\n",
    "    \n",
    "    plt.plot(diag, diag, linestyle='--', c='grey')\n",
    "    plt.scatter(prediction_traj[t][train_inds], expt_means[train_inds], c=train_color, alpha=0.8, s=s)\n",
    "    plt.scatter(prediction_traj[t][test_inds], expt_means[test_inds], c=test_color, alpha=0.8, s=s)\n",
    "    plt.xlim(minval, maxval)\n",
    "    plt.ylim(minval, maxval)\n",
    "    plt.xticks([-15,-10,-5,0,5])\n",
    "    plt.yticks([-15,-10,-5,0,5])\n",
    "    \n",
    "    plt.xlabel(r'predicted $\\Delta G$ (kcal/mol)')\n",
    "    plt.ylabel(r'measured $\\Delta G$ (kcal/mol)')\n",
    "    \n",
    "    plt.title('train RMSE: {:.2f}\\ntest RMSE: {:.2f}'.format(train_rmses[t], test_rmses[t]))\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('neural_radii_scales_and_per_particle_psi_from_atom_and_neighbor_smarts_student_t_loss_df=7_big_batch/{:04}.png'.format(t), bbox_inches='tight')\n",
    "    \n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:28.266659Z",
     "start_time": "2019-03-07T12:36:28.082003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in train_inds[:10]:\n",
    "    plt.plot([p[i] for p in prediction_traj])\n",
    "    plt.hlines(expt_means[i], 0, len(prediction_traj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:29.327086Z",
     "start_time": "2019-03-07T12:36:29.129011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in test_inds[:10]:\n",
    "    plt.plot([p[i] for p in prediction_traj])\n",
    "    plt.hlines(expt_means[i], 0, len(prediction_traj))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:37.712793Z",
     "start_time": "2019-03-07T12:36:36.065933Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MMvBbM6qvz/G6GnhD92mXM4EHDw4x97IO7/K+hsFvoYeA+4DPdstfy+ANx1sYvCz6+WXbfIjuUx0MxuOuB/Z0j8fPoLbfB74H3Lzs58Qhtf0tcBtwa3dgNs6i77p172TwCYQ7gVcuW34t//cJnVMYBP1e4O+AY6Z8rP8aeNMhy54BXLusnlu6n9sZDDXM6jwceqyW19fNn8Pg0xJ3zbi+vQzGUg+eb5ccWt969N+w/gD+gMEvHoDHd+fW3u5cO2WGffbTDIYnbl3Wb+cAbzp4HgJvXpYzXwReNMP6hh6vQ+oLgz8cdFd3fi6sZB9+U1SSGnHEDLlIktbGQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqRH/C72oeiGg+TGxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3ge8At6z4OWVIbX8H3A7c1h2YTbPou27d2xl8AuEu4OUrll/HDz6hcwaDoN8H/ANw3JSP9d8Abzhk2anAdSvqubX7uYPBUMOszsOhx2plfd38BQw+LXH3jOvbx2As9eD5dtmh9W1E/w3rD+APGPzhATi+O7f2defaGTPss59iMDxx24p+uwB4w8HzEHjjipz5PPCCGdY39HgdUl8Y/MdBd3fn5+Jq9uE3RSWpEUfMkIskaX0MdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGvH/P2lpKbs57SwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in train_inds[:10]:\n",
    "    plt.figure()\n",
    "    plt.hist([p[i] for p in prediction_traj[100:]])\n",
    "    plt.vlines(expt_means[i], 0, 10)\n",
    "    plt.xlim(minval, maxval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:39.338486Z",
     "start_time": "2019-03-07T12:36:37.715366Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1eJf3lQyehR4C7gM+1S1/FYM3HG9m8LLol5ZtcxndpzoYjMddB+zrbk+eQW1/DHwXuGnZ36lDavtH4Fbglm7HbJxF33Xr3srgEwh3AC9btvwafvgJnTMYBP1+4J+AE6a8r/8eeN1hy54MXLOsnpu7v9sYDDXM6jgcuq+W19fNn8/g0xJ3zri+/QzGUg8db5ceXt969N+w/gD+hMETD8Cju2Nrf3esnTHDPvs5BsMTtyzrt/OB1x06DoHXL8uZzwHPm2F9Q/fXYfWFwQ8H3dkdnwsreQy/KSpJjThqhlwkSWtjoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1Ij/B2vBflnN8f7QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gO8Aty35OHVLb3wK3A7d1B2bTLPquW/c2Bp9AuAu4YNny6/jBJ3TOYBD0B4C/A06Y8rH+a+D1Ryx7OnDdsnpu7X7uYDDUMKvzcOixWl5fN38hg09L3D3j+g4wGEs9fL5dfmR969F/w/oD+AMGf3gAHt+dWwe6c+2MGfbZTzEYnrhtWb9dCLz+8HkIvGFZznwBeOEM6xt6vI6oLwz+46C7u/NzcSX78JuiktSIDTPkIklaGwNdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RG/B+QSWja9EJirgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+qo5U1VpfmlrPHuBgt3wQuLJ/VX9vtfqq6r6qOvUdgYeBFyQ5Z5rHHsca47cH+GhVPVdVXwCOMrhFxddk8O2XVwO3dE1TH79RuuP+S+CmWR9rBjblNh9V9emqOtmt3sXgOyqbbZyxGP73eQtwWTbwras+qup4Vd3bLf8lcITn3zf19wAfqYG7gK1Jto+78xkT9OvYmeS+JL+X5AdX6fPiqjoOgxMLnD+/8r7mx4D7quq5Vba/o/uz64ZpTy2tYZzbUXw78JWhAJnXLSt+EHimqh5bZXsBn+6mxPat0mdW1jtXZ8JtPt7K4CpvlHmO3Thj8bU+3evsWQavu7nqpoxeCdw9YvP3J3kgyaeSvHyuha1/vnq93ub6/4xN8j+B7xix6dqqunWV3Y4D/6iq/izJ9wG/neTlVfUXZ0h9p/Z9OYM/pX9olS4fAH6JwQn9JQZTFm+dQ33r3o5izD4bMmatb2btq/lXVdXTSc4Hbk/ySFV9tk9d49THeOdq6mM2Tm2nxi7JtcBJ4MZVnmZmYzfCprzGNirJtwC/BfzsiPy4F/jOqvqr7j2Z3wZ2zbG89c5Xr/Gba9BX1Wsm2Oc54Llu+Z4kjwP/GDj9DYtnkmyvquPdnzQn5lEfQJIdwCeAt1TV46s89zND/X8D+OSc6lv3dhQM7qWxNcmW7mprVJ8NWa/WJFuAfwF83xrP8XT3eCLJJxhMEUwlrMYdyzXO1TjjOpExxm4v8HrgsuomcEc8x8zGboRxxuJUn2Pduf+HwJdnVM/XSfJNDEL+xqr6+Onbh4O/qm5L8utJtlXVXO6DM8b56vV6O+OnbpIsZHB/e5JcxOC37BMjuh4C9nbLe4E1r8CnWN9W4DBwTVX9/hr9hufTfhR4aNa1dQ4BV3WfetjJYPz+cLhDFxZ3Am/smuYxfq8BHqmqY6M2JnlRkm89tczgL6W5jNmY52pTbvOR5HLgXcAbquqrq/SZ99iNMxbD/z7fCPzuar+kpq17L+B64EhV/eoqfb7j1HsGSS5hkI1/Nqf6xjlfh4C3dJ++uRR49tRU9Vg2613mEe8q/yiD31rPAc8Av9O1/xiDNzkfYPDn1T8f2udDdJ8wYTDfdwfwWPd43pzq+3fAXwP3D/2cP6K+/w58DniwO2nb51Fft+1aBp+KeBR43VD7bfz9J4YuYvAL4Cjwm8A5Mz7fHwbedlrbS4Dbhup5oPt5mMG0xbxeiyPP1XB93foVDD7B8fi86uvOz1NDr7UPnl7bZozdqLEA/j2DX0gAL+heV0e719lFczyf/5TBNMeDQ+N2BfC2U69B4B1DOXMX8ANzrG/k+TqtvjD4nzk93r02lzZyDL8ZK0mNO+OnbiRJ/Rj0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ17v8BXjC7QVdg0TAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TLX8Vgzccb2bwsugXl23zAbpPdTAYj7sO2Nc9njiD2v4Q+B5w07Kfk4fU9vfArcAt3YHZNIu+69a9jcEnEO4AXrZs+TX88BM6pzEI+v3APwDHTflY/y3w+sOWPRW4Zlk9N3c/tzEYapjVeTj0WC2vr5s/j8GnJe6ccX37GYylHjrfLjm8vo3ov2H9AfwRgz88AI/tzq393bl22gz77GcYDE/csqzfzgNef+g8BN6wLGe+ADx/hvUNPV6H1RcG/3HQnd35ubiaffhNUUlqxBEz5CJJWh8DXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRvwfXn91cX5cyg8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in test_inds[:10]:\n",
    "    plt.figure()\n",
    "    plt.hist([p[i] for p in prediction_traj[100:]])\n",
    "    plt.vlines(expt_means[i], 0, 10)\n",
    "    plt.xlim(minval, maxval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:39.362054Z",
     "start_time": "2019-03-07T12:36:39.340879Z"
    }
   },
   "outputs": [],
   "source": [
    "snapshot = traj[-1][0]\n",
    "radii, scales, log_taus, psi_coeffs, psi2_coeffs, psi3_coeffs = neural_net.neural_net_predict(snapshot, all_types).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:39.540046Z",
     "start_time": "2019-03-07T12:36:39.364437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b25d00438>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(radii, scales, s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:39.860759Z",
     "start_time": "2019-03-07T12:36:39.707672Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1aab205ef0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(log_taus, scales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:40.673213Z",
     "start_time": "2019-03-07T12:36:40.360883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a79277eb8>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(psi_coeffs, psi2_coeffs)\n",
    "plt.figure()\n",
    "plt.scatter(psi_coeffs, psi3_coeffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:41.647694Z",
     "start_time": "2019-03-07T12:36:41.643599Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.15    , 0.8     , 0.      , 0.8     , 0.      , 2.909125])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline_bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:42.523159Z",
     "start_time": "2019-03-07T12:36:42.518648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(664, 229)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# greedily merge pairs of types?\n",
    "\n",
    "all_types.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:43.370192Z",
     "start_time": "2019-03-07T12:36:43.198310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a792a7668>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JEZG0MGOCu6Iwm0c+u5lXj3Vw5xOHY49vr2njZNcgD+1q4KHohRcOfuN3yM2cMT+6iMwyaX2ukjNtWDyHd68av6LlwVcnHlH5i92NU1WSiMhbbsZNOy+uKGBFeR5XriyjvX+YX+1tmrBNfA9cRCTdzLjg9vmMp/7i3ZgZL9a08au9TVy5spTtNadP/9rSOzSNFYqIvDkzLriB2AqSzSvLePGOLdS19Y8L7tZeLREUkfQ1I4M7XmVxzrj7Kyvyaek5e3AfbOzRyhMR8bQZtXPybCqLc/jS1tVcVJHPxsXFtPQOsedEF10DwxO2/fR91dz4r9vYdbxzGioVEUlsVgQ3wGeuWcGTf/FuFs7JpXNghN+7ezu3P7h7wna9QyEAth3RtSxFxJtmTXCPmVeYHbv9wpE2nHPjnh8NR+639akPLiLeNOuC+6b1lfzBxoXkZvqByDm8xwyHwrEZt4JbRLxq1gV3Tqaff/rD9bx4xxYKsgP83t3buXfbMX6w7Rj7Tp4+VL6td2L/W0TEC2ZdcI8pzs3kqzeuJRR2fOOxg3zzsYP8wXdfAiDgs3Ez7v5giL/55QE6+4e55/lamnumZh24c45gaHRK3ktE0sesDW6AP6xaxJe2rgZgQdyywYvnFtAaF9w/qz7Bj16s489+spt/ePwwm+58OtYLT6WfVp9g853PMDQySmg0zNI7fsW9246l/H1FxNtmdXADfPiyxXxwwwIe+szm2GMXz82ndyjEr/Y2MTQySnt/pG2ysz6yRDDs4PsvHOUD39lGZ/8wLx9tn7CTczJf+cU+ntjXREvvEFvvep79J7snbNM9OMIXf/4aXQPDHGzsob1/mNdOdNHUHZnl/+OvIyfQ6guGON5++tD9lt6hKfljIiLTb9YHd1FuBv/8R+9gXlE2K8rzALikMnLwzece2MWV/+cZnj7UAsDAcKRt4TO484nD7G3o5nfv3s7N97wc26YvGOLWH+2gpqVv3Pv0BUPc9/JxPnP/Lh7ZdZLDp3p5+Wg7Z7rv5Xp+Wt3AD7fXcSrakqmu7+RE9PwqY6cV/8S9r3L1/32WcNjR1hfksr9/mq13Pa/wFpkFZvyRk+fjvz69iReOtHLDuvksmJODz4w/uW9nbMYNkdn44pJcnooG9dgJqx7f18SS0lwONvXw9OEWeodChMJhbr/uIho6B8nJ8Mde45HdJwGobx/gZ9Un2LK6guLcTPw+YzD6x2EoNMqp6BGe1XUdlOVnAuCLJvfY7L+pZyg28z7S0kdNSx8Bv/HT6hPc9PZKLqks5PF9p3jnsjlUFJxeCiki6UvBHacsP4vf37AQgPe9bT4AH71iMY/ubuRT71rOd549wuYVZXz2mhUEQ6+xbkERTx5sprFrkId3n+Th3Scpj160+NW6DgA++cMdE97n8KleAH5z4BT/+XI96xYU0tk/wrWryxkIRoK7uXuIluiMe2d9J2srC4HIOvOR0XDstWpa+mjsGozdP9LSyyO7TvL04RZae4LccvliPvdA5Pqbb/zdDWQGUvshazTsGBgOUZCdkdL3EZnNFNwJ/O0H1vG1Gy8hM+DjT7esxGeRk1j9562XA/ClravpGRrhs/ftYltNW9InsLpqZRnbaiJHZ+4/GVlLft/Lx2PP/2JP5Jzh84uyaeoe4pFdkVl6MBTmxy/Vx7arbekbt8plV30X1dHZ+MGmHl6LuxrQG829PL6viVDY8b/ft+a8x2IyQyOjZMd9mrjz8UN8f9sxDn9z67jHReStk8xV3u8FbgRanHPrUl+St/h9hj96lXj/Wa4WX5idwX2fuhznHP/xYh0l+Vk0dg1y6ZI5tPcFCYbCFGQH6BoY4YrlpbT1BfntgeZYcI/56BWLx4U3wOYVZTy0q4HG7iHWzC/kUFMP33zsID6L7CR9sbadnfUdrKzIp6alj3u3R1adlBdkcfhUL7890Bx7rUf3nOTfX4g8f/uWlXT0D1NekBW7GpBz7ryuzfnfrzVy+4O7ee6vrmFpWWT/wE92RC5c8dLRdq5dVUH34AiF2YFpveZnR/8wWQEfeVmap8jMkMxv8o+A7wA/Tm0p6c/M+OMrlyXcrrI4h5K8TPqCITavKCU/O0AwFObaVRV8fNNScjL87Kjr4GuPHuDjm5Zw4/r5+My4+qIyjrX1851nanjP2rk8f6Q1doWf69bM5YrlJdz38nEyAz4+ddUy7nziMK/WdXDd6gperG2PhTbAZ++PfEK4uKKAz167gvtfPs6JzgF+/fmrKcwZH7RdA8MU52aO+xmCoVEejl4K7rnXW+jcPUJNSx+F2QH6giGeOtjMaye6uOupI3z1xrVcv6aCOx7ax7dv2RBrJwHsqOvAZ3DpkpI3NfbnsvGbT7KsLI9n/+oaBodHOdbWH2s9iaQjS2YZm5ktBR5LdsZdVVXlqqur31xlklBT9yBf+MkePrF5Kb9zyTz8PsM5R9hFgvXuZ2sozM7gmlUV/ObAKfaf7ObP33Mxdz31Br+Jm4nHy8v044CqpSUU52Twy9ciLZvr11Rw0/pK3mju5aXado40R3aCdg6MTPo6mX4fw3G9+DFfef8a3n1xOS8dbeeP3rmIVV/5NQB7/+a99A6FWFCcQ21rHxUFWUn1yZu6Izt+z/zD8sKRVg409uDc6SWUr33tvVz7T8/R0T/MC1+8lkUluQlfX7ypd2iEH26vY2VFPsvL81g9r5D+YIid9Z2866Ky8/6Ed65Pm6HRMMFQOPaJrS8Y4jvP1PDhyxazuDTyO3Sqe4ji3Iw31R40s53OuaqktlVwz05jLYxgKMx9L9ezdn4hO+o6+bfnarh4bgH7Jlljfi5r5xcyryibZw638MGNC3g42pP/9NXL+d7zR2PbXbpkDgcbexgcGeWSysLYuWLMwDmoWjKH6vpOLl0yh/e/bT7zi7LZum5e7H+q10/1MjAcYjTsWFtZyNqv/YZlZXncetUy8rMCvH1hESOjjt+56/lz1vutm99BXzDE/pPd3PnBt3PvtmN0DY5womOAnfWd/NE7F3Hb1cvZUdfB5ctK+f4LR3l0TyOP/umVZPinZxVtfzDEs6+3cP2auQkD4nj7AOUFWYw6R06GP9bmC4cd979Sz+aVZawoz5/w787cZzHZ6/YMjXDx3AIyAz6Otw+wraaNd11UNuEPYdfAMD6fURj3B9g5R2P3EOX5WbEd5YdP9bDneBd17QOc6Bjg8uUlbFw8h70N3ZTlZ3L9mrk0dg9y77Y6TnQO0D04wqvHOsa91+euXcHdz9YCsHpeAR+5fDGbV5bRET0OIjczwI3r5xMcCfPE/iYe2nWS61ZX8JHLF/Pd52r5yY4TLC7J5YMbF7BhcTE/3dHAyGiY3mCIpw414xxcu6qcG9bN5z9equNAYw+r5xXw1+9fQ99QiM//1x6WlOTynQ9vZNW8gnP+tzmbaQluM7sNuA1g8eLFl9bX159tU/GwcNjh8xl7TnSxcE4OO+s7WVmRT2jUUdPSx6p5BTy8q4E18wt5+lAzt161nENNPVy+vISC7Az+4fFDfHHrKl4+2kF1XQdfu3EtPjPue6Wepw618Pwbrfh9xmjYsbw8j0sXz+FnOyMtl/WLitnX0MWZS9HNYGlpHgvn5PDCBZ5u9+OblvDjl+opycuko3/8eWjeu3Yuvz048RNIZVE2jd1DrJ5XEFsJ9JX3r6FncATM6B4YJuxg45Ji/uf1Vpp7gly2rITygiwKczK4eG4+hdkZ7G3o5ulDzQwMj7JxyRxeONLK/KIchkNhyguyyPAbj+1tYuu6ebxrZRmVxTmMjIZ5sbadR3afZF5hNr+/cQH3bjvGK8c6yM8KcNP6SgaGQ+RlBdiwqBgHHDjZTWtfkA2L5vD3jx8iL9OPmVFekMVN6yu5bGkJj+9v4oFXjpOd4aNqSQmbVpSyZn4B7X3DPPDqcXYf72JRSQ7Ly/K5amUZI+EwZflZ+Mx47vUWHotewzXgM4pzM2jrG46N1V++dxUDI6Mcae6luWeIF2vbycsM8HsbFlCYE+CVox3srO+kLxgiw2+sKM8nM+Bjb8PpSUJBVoDeYGjcf4d5hdm09QUJRX8xinMz6DrLJ71k5WdFWnrxvwMNnYMcbOoZt50ZfGLTUnIy/fxw+zGGRsIsnJPDO5eW8N+vNcZqWjgnh6GRMKPhMNu+tOWC9qdoxi2eFAyNcrJzkLKCrHGzsP0nu8nO8LGyooCugWGauoe4/cHdHGvr56KKfDYsLuZExyANnQNcs6qC9YuK6BsKcfeztSwvzyPsHJdUFrGjrgMDXosGwYryPPqDo3QNDnPoG1upbe0nK+DjD7/3UuxI1MksKM6hODeDA409BHzG2ugng7fy4Kay/Ewy/T6ae4NJv252ho/3v62SYGiUx/Y2Ma8wm/5gKBZ0PosEUk/0DJf5WQEKsgP0DI7QP3z6nDc3ra/kYGM3ta39415/9bwCLl0yh7a+IG8093Gsbfzzfp/xJ+9eHttJ3tITZM38QpaV5fHVR/fT0DkYe98FxTlkBny09A7R0T/MyKijsiibLWsqWFGeT0tvkMNNPXQPjrB13Tw2ryhjaVkeuRl+dh7vpL1vmIVzcqhp6ePxfU0sKsnl1quWMRwKM68om4bOQboHR9jb0MXrp3r52KYlhEYdcwuzCYXDvHCkjb0N3axbUMiW1RW09ga5+9kainIyuGHdfK5ZFWnX7TjWyVUXlcb2sVTXddDeP8zGxXNinwLH9sm09A7R1DXEJZWFBPw+alv7qG3pYygU5tpV5QyOjHKgsYdrV1VcwG+EgltmgLHfywtZjdLcM0RdWz+XLy+d9HUONHZzvH2AUNixsiKftr4gGX4fBdkB5hVmE/D7yPAb+0/28PaFRWRn+BkZDTMadjyxv4n8rAwWzslhNOzoGRqhvW+YsvwsVlTkken3MTgySnNPkBdr2xgOhdm0vJSW3iBZAR8d/cP8/sYFZAUi7YjhUBi/z/BZ5ACq9r5hGjoHCPiNty0oZkV5HrWtfXT0j8RqgdMtjdGwo7a1D7/PKM3LJOD38fwbrbxtQdG41sWJjgGOtfVzSWUhpflZDI2M0tE/zG8PnKK+Y4A18wv50MaF+OJWTo2tiNpe08bKisiBZ6X5p3csxxsZDXOkuY+i3Awqi7LHjbdzjs6BEYpyMs66Mkve4uA2sweBa4AyoBn4unPuB+f6NwpuEZHzcz7BnbAR45y75c2XJCIib5VZf5IpEZF0o+AWEUkzCm4RkTSj4BYRSTMKbhGRNKPgFhFJMwpuEZE0k9SRk+f9omatwIWerKQMuLATUsxcGpOJNCYTaUwmSqcxWeKcK09mw5QE95thZtXJHj00W2hMJtKYTKQxmWimjolaJSIiaUbBLSKSZrwY3PdMdwEepDGZSGMykcZkohk5Jp7rcYuIyLl5ccYtIiLn4JngNrOtZva6mdWY2R3TXc9UMrN7zazFzPbHPVZiZk+a2ZHo9znRx83Mvh0dp71mtnH6Kk8NM1tkZs+a2SEzO2Bmn48+PmvHBMDMss3sVTN7LToufxt9fJmZvRIdl/8ys8zo41nR+zXR55dOZ/2pZGZ+M9ttZo9F78/oMfFEcJuZH7gbuAFYC9xiZmunt6op9SNg6xmP3QE87Zy7CHg6eh8iY3RR9Os24LtTVONUCgF/6ZxbA1wBfC76+zCbxwQgCGxxzq0H3gFsNbMrgH8E/iU6Lp3ArdHtbwU6nXMrgX+JbjdTfR44FHd/Zo+Jc27av4BNwG/i7n8Z+PJ01zXFY7AU2B93/3VgfvT2fOD16O3vAbdMtt1M/QIeBd6jMRk3JrnALuByIgeYBKKPx/5fAn4DbIreDkS3s+muPQVjsZDIH/ItwGOAzfQx8cSMG1gAnIi73xB9bDab65xrAoh+H7sC6awaq+hH2Q3AK2hMxloCe4AW4EmgFuhyzo1dsjz+Z4+NS/T5bqB0aiueEncBXwTC0fulzPAx8UpwT3YFUS13mdysGSszywceAr7gnOs516aTPDYjx8Q5N+qceweRWeZlwJrJNot+n/HjYmY3Ai3OuZ3xD0+y6YwaE68EdwOwKO7+QqBxmmrximYzmw8Q/d4SfXxWjJWZZRAJ7fudcw9HH57VYxLPOdcFPEdkH0CxmY1dPzb+Z4++Agv0AAABNElEQVSNS/T5IqBjaitNuSuBD5hZHfATIu2Su5jhY+KV4N4BXBTdE5wJ3Az8cpprmm6/BD4Rvf0JIn3escc/Hl1JcQXQPdY+mCnMzIAfAIecc/8c99SsHRMAMys3s+Lo7RzgeiI75J4FPhTd7MxxGRuvDwHPuGhzd6Zwzn3ZObfQObeUSG4845z7CDN9TKa7yR63g+F9wBtEenZ/Pd31TPHP/iDQBIwQmRHcSqTv9jRwJPq9JLqtEVmBUwvsA6qmu/4UjMdVRD6+7gX2RL/eN5vHJPpzvh3YHR2X/cDXoo8vB14FaoCfAVnRx7Oj92uizy+f7p8hxeNzDfDYbBgTHTkpIpJmvNIqERGRJCm4RUTSjIJbRCTNKLhFRNKMgltEJM0ouEVE0oyCW0QkzSi4RUTSzP8HIHB8vCYSqL8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test_rmses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:44.305868Z",
     "start_time": "2019-03-07T12:36:44.302480Z"
    }
   },
   "outputs": [],
   "source": [
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:36:45.170598Z",
     "start_time": "2019-03-07T12:36:45.030317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1a7927a518>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BZB8pE3fG2OlQxf1Hdttwzh/hnM/jnM+rrq5O1akHHDExh8vBMLaqEIAaUTscDG6nAzcvmYLlN54GwN6WEQOoPVKrX8F1T6/Dd5/6SH9v13JAfnCoee7G44QlW6YnEJK2BZZvOYrlW47G/awEQWQHKRF3xthMAI8CuIBz3pyKY2YTbi2nvTjPhQUTqwAYZ0VijCFfewDE6+fe5QvFXA/Ye+5yJg6XK1T1ZZE8912N3YbjXfWXBlz1l4a45yYIIjvod547Y2w0gOcAfIVzvr3/l5R9FGlNwr6+cBy+sXA85o4px8njKg3biAk9/HFSIbsC8cXdruVAMCpyt86WUTiPivIJgsgt4oo7Y+wpAIsAVDHGDgD4KQA3AHDOHwJwO4BKAH/UBu5CnPN56brgwUiBx4XdvzoXjKlR+plTa6O2EQ+Ap9Z8ArfNFHxAdNqjcR0HY8w+cjelQtr1sTG3JpBf+4Jh5PVz/leCIAaeuOLOOb8szvqrAVydsivKUuzaDQi8Lic8Tge2HunErc9v7NM5QgqH28lMnnskApdtGYVzPeVRiLc+h6piEnfpeEfafajXxg0IgsheqEI1gxTZ9HhPlGDYmAsPGAdoZVtGnkNVbC5nyxgKnqTXh9p7+3WNBEEMDkjcM4jdBB6J8v/WHsAXHn7PkOcesvXcERW5hyXPXR7wDSsceVp/nMNtvn5dI0EQgwMS9wwicuD7yof7WrF6T4uhbbBxQNW6iMmc3x5WeFRnyVJtWsDWnkC/rpEgiMEBiXsGKfD0b6CyW8uBb+8N6svkCNwcuYdN4s4lkTd0kFSip+IjCCK7IXEfxJjHaLv9apqkLO52qZCcR6LzsMmeUWIMqJozbAiCyE5I3DNIsrrpNc3Nahm5h+1tmUjkbjx/WLJsAOOAqrm9MEEQ2QmJewZJdgYk8yQgvYHYkXvAPKAqDaDK/WMUbvLcDTnvSV0iQRCDFBL3DJJsgy6vqdtktz86cpcLl8x57iKqNxctKQqPmuQjYuGQuhNELkDinkH6b8uokXtbjxy5Ww+och7x0sNK9ETaZrGniTwIIrcgcc8gE2qKktreHLn3aJF7h4Xn7mAmz12JVKhykw2jKKaJtSWxJ1uGIHIDEvcM8ovPzcCjlyfedsfc40V46laeu8vpiE6FtMlrV0wDqqFw5D3ZMgSRG5C4Z5BCrwuLp9WiMMF8d3PkLrDy3D1R4s51oVY99cj+YYUjHLbrQ5PYZyEIYnBD4j4AlGjVoPGw6x7Z1hupIhUC7nIygy0jz6Eq93YHNOE3i72UWUMQRPZD4j4AxOsxM6I0DwAMsyXJtPdEe+4uB4tKhRRvw5L/Hllnjty145EtQxA5AYn7AFCSZ4zczfbL8LJ8AECrJOIyHdJsTWGFw8HUCblDZqtFisaFtjOm7hMy5bYLUSdbhiByAxL3AcBsy5gj+eoiL4DEmniFFHUCD5fDYRG5R9oNCKF3Oxxq+wE5z12JiH+mKlTf2XnM0ACNIIjUQuI+AJjFXETybqfaTEY0GOvyJzafqoMBDgcQDFl77mFpwNSlTfYhp0LKA7GZ6C2zfn8bvvToatz96ta0n4sghiok7gOA2ZYRYu/RBlDLCz340kmj8ccvHh/3WHLkbjeHqtqKQF3ucrCoIibzfummRftGsqOxK+3nIoihSr8nyCaSpyTfeNu9Wj672+UAAmF0+UK4+6KZ2N/SE/dYobCie+7+UFhfrkgVqgZbxumAL6jYTvKRbP+bvuDQZuxOth0DQRCJQ5H7AFCsRe73XTwL88dV4PjR5QAikXunXx1IjTcvK6BGv8Ewh8vBDLaMnLsuR/HClpG99YDJfweA3kDkQZFqnIx6xxNEuiFxHwCELTNleDGevuZk1JWr2TEeLWumU8uGSUDbAQjfncXoLcP1eVRdDkdUy1/zzE6vbjqMqbe/ik0H2/v2AeMgPlcmviUQxFCFxH0AGFaqZsMIkf/09GEYUZqHh748FxfNrcMdF8wAELEvZPLd1tWtahFTLM9d2DIMnMOQNmmwZTjH8i2NAJA2cWd65J6WwxMEAfLcB4RFk2qw7HunYlRFAQCgutiLd285EwBw78Wz9O2sxL28wI3e9mjLxOlgUZN1iIhczo5xadZPIGzvuYttE7GF+oI4LHnuBJE+KHIfABwOhukjSuNvZ6Gt5YUey21dDnPkDvQG1YeAastEKlkBo6CbbRnhxzstHi6pQDw0KHIniPRB4j6IsYrcK2zE3cGYwUfnnOuDolxKhRT9amRBD0RVtmrHTNNvh/hUNKBKEOmDxH0QY2WL2Im7y2ncVlGkyN2ULQMYI3c5yyYUlmyZNEXuma6GJYihCIn7IMbKlhmh9Z0x45TC7JI8FxQpcpf7ubu17YJhBU4Li0auZnWmyXM3T9xNEETqIXEfxFhFzpUxPHdAbUJWnOdGIKwWKgmBFs0eReTuDyl6Xr3Bq1fkhmOp+RxmuH58UneCSBck7oMYWdyFeNv1eBfbVhV54XIy7DiqlvYXedWEqKDe9z0i6CKv3h8y9pYRWh9KU4vIcJofHgRBkLgPauQBTdFMzM4qEeJfUejBpSeMxubDHQAi4i76vrt1K4brDwpzfryIrEPh9KhvZBIRUneCSBck7oMYOXIv1EXaOpp2anZLZZEHF84ZqS8XDwWxn7BlAiFF7yNvznMXdkkgTZG70HSqUCWI9EHiPoiRxV2IdCCsYPHUGtttKwu9KCuIdJ0s1G0ZYxHTxoPtui0TlGZzUouY1H3TZsso5LkTRLohcR/EyA5MgUcVaX9QwYNfPB7PfftThm17tSn55owuQ57UoqDQa4zc3dJB3VIUD6i9beQiplT1dq+/eRkefGOH/l6IOmk7QaQPEvdBDJMi93wpcs9zO3H86HLsves8ff2NZ0/G7y+bgy+dNNpwDH1ANWyM3AGpoCksiTtPjy1z73+2668VypYhiLRDvWWyhEIh7jZT000dXoKpw0ui9xNevZYt45aKncSAqfDc3U41che2SSoGVK0GTcUXgjCJO0GkjbiRO2PsMcZYI2Nsk836KYyx9xhjfsbYD1J/iQQAnDVtGADg7OnDktovErlrA6pSCo5Pm9xDrPNo4h7Sxb3/kbvVoGlkbtd+H54gCBsSsWWeALAkxvoWAN8DcG8qLogwskQT80m1Rdh713mYO6Y8qf3LtMm4ewPGbBlA9e8Bo+euKJFukoEURO5W0XnEc6fInSDSRVxbhnO+ijFWH2N9I4BGxth5dtsQfef+S2fjXx8dxJzRyYm6oLRArWjt0QZc5SIo0XtGiLjH6UC3P6RP15euyD3dFbAEQWR4QJUxdg1jrIEx1tDU1JTJU2cteW4nLj1xdJ/7vJRraZE+Tcjl4/QEQmofeC1Sd7vUKfhExWoqsmWsbRn1Jw2oEkT6yKi4c84f4ZzP45zPq66uzuSphxw/PncqAOg57z1aEzEh8oCaQeOUpucTA6q6XZOCyN3KV6dsGYJIP5QKmaN8feE47L3rPOS7VedNiHuP3ziLk8MhpUI6HQgpPKW2TMhC3RXqCkkQaYdSIXMcr1t9fov2v92a9y5wsogtIwZU/SlMhbQeUBU/Sd0JIl3EFXfG2FMAFgGoYowdAPBTAG4A4Jw/xBgbBqABQAkAhTF2PYBpnPOOtF01ocNY7ErPPJeaH9+j2TEl+W7DeoeDGQZUQ1IqZLpsGb0rJIXuBJE2EsmWuSzO+iMA6lJ2RURSrPvJ2ZbWh0BE7tuPdAIArjp1LE6sr8D1/1gHQEysHYncDe1/UxC5W10bp2wZgkg7ZMtkOaUF7pjrRZ+ZbUc7sXBSNcZVFWJ8dREcDoZxVYW44rE1elRv7hUf66GRKJaROzUOI4i0QwOqOY5o6wsAZ02t0fvVfHbWCMwYWQqHKXKXCYY5Vm1vwu0vWBYnJ4SV507iThDph8Q9x5E7RBbnRUf5TsZ0jz1a3BVc/tgaPPnevj7742FLW0b9OZhsmVue24D6m5cN9GUQRMogcc9x5Mi9OC/ahTvS4dNfeyRbpqrIa/DczVk2iWI1Jhs2tR/4++pP8PCbu/p0/FTx1Jr9A3p+gkg1JO45juyjW0XuMnLkXlXk0eddBYAOnyruvYFwUj1hYrUfEOtufX4j7nxla8LHJAgiPiTuQwiryF1Gjtyri72G6fc6fUE0dvow9fZX8fg7exM+p6W4UxETQaQdEvchRDxxFxOCAEC+22mwZTp6Q9hyWE2nfH3z0YTPGauICQDae4MJHysT0LyuRK5A4j6EiGXLvHXT6Xrv90KPE26nIypyP6r58zUl3oTPaRbLQEgxLNtzrDvhY2WCYJrmjSWITEN57kMIId4y31o0HiV5boyqKIBD6xhZVeyF28n0qfkAoMMXRKMQ9+K+ifuGA2347IPvYPaoMn3ZrsYuw/b1Ny/DlaeMxe2fmZbwOVJJquaNJYiBhiL3IYRV2+AfLZmCby0aDyDihc+sK4MrKnIPYX9LLwDj3K7xkMX9vV3NAIB1+9v0Zc3d/qh9HntnT8LHTzWpaJZGEIMBEndCZ6vWomBWXSnK8t043B5Jk+zoDeKTlh4AxrbB8ZALlawsj25/4sfKBMEUtFwgiMEAiTuhc/a0WgDAOccNx/CyfMO6Tl8IrT0BAJHp+RJBtjmsJvfukfLnB8NgZipaLhDEYIA89yHA9BElaO0OxN3u9Ck12HuXOlvi8NI8w7oOXwhdflWIxcTaiSBXtvotIvcuKXLv6WOhVCpJRbM0ghgMkLgPAZZ9b0HS+5jF/VBbb0Tck7Bl5Gi82x8t3rKg21k024924uE3d+PXF83s83SDiZKKNscEMRggW4awZHhpxJb59PRafLivVc9J91vYK3bItkxrd3ROuyz4XRbiDwDf+duHePbDA9hpyqxJBxS5E7kCiTthSbWU7njOjOHo9If0hl99HVAVnr1MdwK2jDhGmoN2AJTnTuQOJO6EJU4HwxfmjcJvLpmFmXWlhnW+JAZUZVumtccicg/Ej9zF8yGZFMy+ElI4WrsDqL95GZZvSbwSlyAGG+S5E7bcfdFMANFeeTK2jCzuLRY57fKxzZN3CzLZ9z0UVrD+gJqH/8S7e3Hm1NqMnZsgUglF7kRcCqXKVq/LAb+FLfOHFTste87I4t7RGx2Zy7aMXVthcYhMpCkGw1zvgFkSp4smQQxmKHInkqKqyGvpud/z2jYA0FMpOed48I2d6JW27bXYLxFbRkTumRjsDCmKPnBsnkycILIJEnciKaqKPDjQ2osNB9owvrrIENXLHOsK4L7Xt8c9Xk9AitzjeO7pHOx0MPUbQijM0aGLO/15ENkL2TJEUlQWedHhC+KzD76Drz6+xna7eD65S0t9kW2bLhvPXWyTzqZeIn8+EFbQ4dPEnWwZIoshcScSQiSqVBZ69P4rH+xtBWDdNsCuRYHoTCnP7SroGUBbRmTiqJG7eh3pLpgiiHRC4k4kxMw6tU1vlandb2OnD9c9/VHU9nYtCko1H1sWTrdTfd0dsMuWUX+mc0DVKcRdiUTug6HXDUH0FTIViYR4/KsnYP3+tqgq0RufWY+3dhyL2t6u0KmswI2Dbb2QU9YLPC609wZjeO7pj9zFsyYoee4k7kQ2Q5E7kRAVhR6cPqUGXrfxV0a0ATZjV+gkInfZki/UpvezE3dhy6R3QFXYMgqJO5ETkLgTSWGehelga6/hPeccPYEQVm1vsty/rEAVd0XhujUjMm7sUyHVn6keUG3s8OHGZ9bDFwzr3ySCCtdTNjNZPEXkHh8fajd0Rc00JO5EUtRXFRremwU3EFZw/dPr8OCKnZb7i8jdH1bgcaq/fgWauK/e06JvxznHC+sOwh8Kpy1yv2PZFjz74QH8Z/NRfYrBUFjR+87TlHvZz4FW62+W6WbNnhac98DbePzdvQNyfoDEnUiS+srCmOt7/GFLD15Qmu8BoAq1x6X++hV5ozNnVu04huueXof7/rNdj35S7bkL2z8otVMISuI+kFEX0X9WbGvEqXevwKubjmT83Hu1id83H+rI+LkFJO5EUsgpjFZ9vL76xAeWlagCYctwDl3c893R4/ptWgfJg229ui2Tag9cXP+N/1yPNq2pWTDMEdAeIuS5ZzcbD7QDAL7517W47z/bMnpu8a3uDiOfAAAgAElEQVTPOYAKS+JO9JkiT7Qor5cmv7aiTCrpF7ZMntuB28+fZthO7wDJpQHVFKdCWmWxh8IcAS2Nk2yZ7EYeMvn9G6pNuH5/G7786Gr4k5hNrC+EuRD3gZNYEnciaVbfeibeuul020ZfsRCRO6A2IQMAt9OBz8waYdhOCC8H1/9IZVvmQGsP3tvVnPT5Deew+OoRUhR9NiYaUM1urP7/fvTsBry981jaJ34Jab9DrgEshKM8dyJpakvUKfj6EtjKzbg8urgzFOfZ/ypyRA+oLrpnJUIK1xuVxaKtJ4ADrb2YMdLYl97KVgqGue65ky2T3Qzk/57omTSQVc4UuRNpR+SxA0CZNqAKRMTd5XREtSMQRVCcRx4iTV1+/Pb17QiGlaQsk4seeg/n//7tqOXMwphRs3PU1yTuWU6Mb17p/lLWqbWNlr89/N/7+3Cwrddul5QTV9wZY48xxhoZY5ts1jPG2AOMsZ2MsQ2MseNTf5lENvDSd0+1XC4LsWzLCEF3W0Q3IvLhkuf+8Ju78bvlO7Bsw+Gkrkt8Beemv2iryF2eMITEPbuJ9b+XzIQzfaHLrw7Qi9/j9p4gfvKvTfjKo6vTel6ZRCL3JwAsibH+HAATtX/XAPhT/y+LyAae+NoJ+NlnIgOhU4eXWG4XkOwU2X6pLlILotwWKQWioEn23AU7Gjv118mkK5qjfasvzF3SOAKJe3YTKzq3mnCmf+fiBh9fRO69mriL8al9NhXd6YCZoxnLjRirB/AS53yGxbqHAazknD+lvd8GYBHnPGZ4NW/ePN7Q0NCXa8aiRYv6tB+RHvbO/yEAoP79e/TXdox5/17sm/8DAEDx4bXoHD4XJQdXo2L/KsO+JQdXo2PkSchv3YXe8vHqQiUEOFwoaN6Knsop6vFW/waMx/5DFccdveZ+OJTIPK7Hxn0aXTUzDdvK5ys8thnVO5dZHlNxuAHO4eDJDyoTmaF11AK0j5yvv69//x4cPO4KBAtrULP1WRS07U7qeB3Djoen6yjyug7anmvE+sfh6T2Go5MvRG/5BOS37kLttucQyK/EoVlX6texcuXKPn8uxthazvm8eNulwnMfCWC/9P6Atszqoq5hjDUwxhqamqzL04nsw93dqL8eue5R1H34kO22TPqy7AxqkQ6L/jXkTtWbV5wRjx4ONeoP5ZVHtnNEF0DZkci2YXekSItbxvYqn5x4PQ7N+pphWU9pPfbO/yGC0vURA0es/z/uSC6XRHG40VJ/Jo7M+GLUOn9hrf4QCRTWqNs7vdp5tHYb8u9xhkhF5L4MwJ2c87e198sB3MQ5XxvrmP2J3InBRZc/hNbuAEZVFOjL6m82RrzXnTkRy7cexUvfXaCv+/XSmbjp2Q1Yenwd7rtklmGfz80egX+tO4TJtcXYdrTTcKyqIg+OdalFTmtvW4zKImO/GzPiuKtvPVPP9AGAm5/dgKc/2G/Y1ulguh1zzoxh+NOX58Y8ppyt8/1n1uG5Dw/inotm4uJ5o2JeE5F+7nhpM/789h79/d67zsOS+1dh65FO3HfxLCydWxdz/3X72zCptggFHhdW727GFx55Xz+OzJVPfICPPmlFa08Q3zhtHG45Z6p+nll1pXjh2lPx1o4mfOXPayz3T5ZMRu4HAMi/yXUADqXguESWUOR1GYTdihvOmoSXvrvAsEwMrrZq1ai/vDASOwjP/XB7dHZBa0/EWgkm0ZIgYBpEsxpQlX32sMJxuL0Xtz6/Ma0dKYnU89aOJoOwm7Gbb0DQ2OnD5/7wDm57Xs0j+UgrzhNzDwBAc5cfrd0BNHf5MbOuDFOHl2D7ETUQEZ1FxYCq3PE0U20tUiHuLwK4XMuamQ+gPZ7fTuQ+F8+tw4VzLN05nYpC9atqS7cq7l86aQx+vVT1wMWAVIcv2tOWBdgs2LEIRAm0Ud3HVBofUArnuPW5jfj76k/w9k77fjmRo6nHo2HYgUP0BhJRsh12LakFe5q03jCH1d4wQrSDYa5Xt879xX8x547X0doTRHmBGxNrirCzqQvBsIIjHT4AEXHvlH6PW7RgJt0kkgr5FID3AExmjB1gjF3FGPsmY+yb2iYvA9gNYCeA/wXw7bRdLZE13HPxLPz2C7P1afVkGANOHFuBMVoTsgUTq/R1ouhDbv/rdjJUFlp7lofae/FOAsILxI/cJ9YUG96HFB4p1JIU+6NPWhM6H5F6mjr9uPbvH+q9h8x85vdvY9Jtr8Q9jt1kMoLdWuMv8e3yaKdPX3eozWfYtq0ngLICD2qKvWjuCuBwmw8KVyuwRZ8lOXI/1uWPe32pIO6oAuf8sjjrOYDvpOyKiJzivVvOiBLVPXdGPMcPfrxYj+ABwOWMFvfJw4rVxl7d0ce/VPNB99x5rmU7AZl/rz+E659eh5evWwCng0WlylWbetWHpZ7zAHD7C5vw5Hv7DNtwzvXzskjPBCJNPLXmE7y04TDGVhXixrMnR63feqTTYq8I4v988+EO+IJhy7l8AWCXltbocanrj3b49bGePce6UC99y+vwhVCa74bX7UBPIKyPEU0eVozt2mt5CsnW7oitmE6oQpVIK8V57pgDntXFXoOAurRGS13S19gT6iv0Hh12EXy8r9kA8MeVu7DtaKf+4AiZbBr52A5mrC7k4FHCDiBmB0witYTCij72sd8iX9z8/2nmmYb9uvAu23DYcu5fwa4mVdzFN4TGDh9On1wDj9OB93e36F1EBeUFbv33RzTPm1xbDF9QQVjhhmCldbDYMgSRSUTk3in9MSyeWguXVuhUZzNwm4zIigIWc5GSXGBV4HEhFI4k09kllbX3Rv7Ixba50HDs6r98gGdMmUQDzZV/adC7O244qLbz/ev7+/D65qMAgP2tsUv7b/p/GwzvX/v4qP56f0sP2iXBPtKhWifNXQH0BsLo8IVQX1WI48eU4Z2dx9DYabRWygo8qCxUg5h1+9vgcjC9qK+1J2AIVkjciSGJiNADIQWMASPL8g2Re115vuV+PUl0qBSDXEGTuMvfIPLcTiiS5WJHR2/0eaMHbrOPt3YcM8yM1V8efnMXVu/uXxdPeerGT5p7oCgct/1rE77+pJpSvasPnR455wiFFSz49Qpc9NC7+nLhi7d0B9Co+e01xV7MHVOOLYc7orK4ygrcqChSI/e1+1oxuqJAT7tt6vSj2x/Sbb/WbhJ3YgjikloRXDh7JN65+Qx4XA69RUFJnttyv3gDZDKtPQG8u+sYwqb+8LK453schsjerlFZh0+K3LXdk8ngSTWH23tRf/MyrOmHMIfCCvwhxXbQMlkaO32485Wt+OrjH/T5GOZ6nJDCo0r59/dhSr2jHX58sFcdIN/R2IWeQAiKwtHSHYDLwdAbDGNvs3rc2pI8DCvNh8KBLYeN3r4auavi3hsMY1x1Iao0sT/W5UeXP4TKQg8KPU5DKm86IXEnBhVyD7EvnzxGfy3smkKPE5Nqi6L26w1EBJVzHrMvzI3PrMcX/3c1djcZR2hlcS9wuxBWIraMnWA3dwVw+WNr0LA3IqbpbkoViwZNqB6LkeMdjx7tQdnWmxoRemOLWsE8rDQvzpb2tEjRbolmn8nfBDjnevphMhzp8GHl9kiF9fu7m9HaE0BY4Zg8TM2e+viQagHVluShtlhYL8aMqbJ849jSuOoiPVI/1uVHW28QJXlulBV4KHInhiZV2h/I986YgONHR8r4hS1T4HXh4rnR1Z+yLXPF4x9g/K0v45KH3kOjxR+8SHOTB7kAwMFkW8ahz6YD2E/OveNoJ1Ztb8J3n/pIT5scSHEv0Normz+bFY+s2oUX10fXG4pmV331hjnnhkKd9zQRHt4Pcf9Ym4v010tn4okrTwSgCrGgvTeIo+0+/fMnylf+vBoPv7lbF+J9zT169fOsUWUAgA/3qQOktSVe1GhWy+ubj2J0RQGWTB+Gy04cjVEVBYbW1uOqClGlHfORVXuw8UA7RlcWoKLQM3jy3Akik0wdXoKG2xbj+6Y0N/HVua48H1cvGIv7vzDbsF4eUBXe7Jq9LbGrFE1Wjtlzl/XcLnIXItrlC+kPgHRP4RYLkTXUmYC4/+rlrfjeU9EZIyInu72P9sF1T6/Dkt+t0h+4oltip0VB2optjXhpQ+yCdn8ojGv+T/XVTx5fiTHaoLo8EfuhNh8Ot/swRYu2LztxdELXKq5p9qgy5LkdONjaq/vtc7XgomFfCzwuB0rz3agtUQVb4WoW10NfmYs7P38cnA4GxhhuWDwJTgfD3DHlKNZqPLYc7kBvMIyxVYUoK3CTLUMMXaosUie/vWg8Tp1QhaXH14ExhpPGVRjW9wasBfXN7fYN6syDoU4pci/wOBFWlIiPbhO5N2tfsbsCIf0BMJCee7f+sElcQFbvbsYHkq0kBpzbeoNRXnc8OOd4cf0hbD/ahb++vw+KwnX7q9Pimr72+Ae49u/GB0x7TxCXP7ZGfygcbvPBF1TwzdPGY1SFGv26HAzN3QH9gXykoxdHO3wYUZaPTT//NH7xuag2WAbEQ0AwpqIAdeUF+KSlBz96Vs2qOa6uFC4HQ1tPEDXFXjDGDL+bx48pizrudYsnYvsvzsHE2uKowfhxVYWoKPSgnSJ3gohw05Ip+OvVJ+l/zB5TD3i7VEizry5jJdjzxqjRmhq5x29zILxgziPbDKQtIyL2RGwZwRceeR8XP/Se/l48IMIKj/oGEFY4Lnn4PazY2ggr5BTBd3c141B7L3qDYTBmHblbsXJ7I1Ztb8KNz6wDAH32otMmVQNQ5769YPZIDC/Nw8NaY7dPmntwpMOHYSV5KPK64HQwfPSTswzHrSvPx65fnYu9d52HF681TixTW5KHkWX5eHvnMRzQUipHlOXr2Vki80Wee+DcGcMtr1/+BnjKhEr9dX1VIe5eOhNv3LgoofvQX2gOVSIrcbuM4t7Y6ceq7U04cawa0VcWenDSuAq8vPFIwsdUOMfjXzsB+5p78Miq3ZqHrqVmxonc5W0GR+Te9z7zPdKDsq07aMhQ2tfcjTV7WvDxwXZ8/D/Rc/iIXiyTaovQsLcVW7WskjmjyrDxYLuhoteOTVoO+8eHOtDtD+niPrIskgZ73yWzAKjfFIq8Lqzb3wZfUDEM2pabCt7y3M5IcGD6/Sn0ulBXno83t6uf/YHL5qDI68KYykLsbe7RJ5YBgG8sHIdhpXlRx7fib1fPx8YD7fjTmzsxoabIcmKadEGRO5GVmCP3u17ZissfW4NtWvn5Dz49GSfUV1jtaovC1YraGSNL4XQwhKRUSfvIPRKp+tMcub+88TDW7otOcVy+5SgefUudeEJE7N2BsGUVpyBWl0t5qsG2XqOFsEOzSuwmfha55tcsHI8ufwhXP9kABwPmj6vUmm5FziuPTUTmzOVYo42vhBSOlduacKitF4wBtaXRdh1jDGOrCrFim2q/iX5FVuS5reXu4rl1WDp3JEZKNRTThqu2zWdmjQBgHFy+5dyp+NopY23PY+a4ulL88UtzMyrsAIk7kaWYxV0gMiiqirwozbfOibdDzo5xMAZFiVSb2olhS1fkj17YDn2dwq29J4j/bj5qu/7bf/sQS//0XtTyq/7SgF8s2wLAaMe8YWOdALErersDcjWl0SffoZXv24l7U5cfHqcDS48fictOVLOaKgq9GK5F3XJdQLN070Q5/wvrDmH9/jbccs4UVBZ68IN/rsfrm4+iusgLr8s6E2ZsVaFeKTy2yljBfKL0gM+z2f+286fB63Kirjyyr/DWL5wzEl88aTRuOGuS5b6DGRJ3IitxOJieHikjqiqrijxJi7s8eOjSJu0IxrFa5IZQB7RIWbZwuvwhbDzQrr8/1NZre6xrn/oQVz/ZgMZOX1TGTaLzuXb5QqivLMCYyoKY3TJ9FgPQInqWB6fNhUzbj6qReWtPEA+/uSvqGC1dAVQUesAYw1WnjgMAjCzP13PT5UFsuTui+IbwyqbDGFWRj6sXjMP9l85GbzCMjw91GKJqM2OrItG6eV6Bv1x5ot5G2q5JmLg2Yfu4nUz/3XE6GH514XGYP67Sct/BDIk7kbW4nNHi/tYO9et5TUle8pG7JKAOB0NIidgIVoJsfraIwUe/1MTsW39di888+DZ8wTB8wTA+ddcbuOW5jZbnF9khz3ywH5NvexX7miODwS0JFL74Q2F0+0Mo1LzioxY5/kfafVi3v80ychfRrxy5yw2yOOd4f3czvJpffecrW6OyaZq7A3qXzwk1RfjdpbPx4GVzdN9ezpgxiLt2nk0HOzCrrgxOB8OCidV698UZI0ptP/dZ02r11+boPt/jRKnWttdO3MUYwCjtAVJZ6I07LpANkLgTWYs5mJ1ZV4pgmKM0340RpXl6L+5EkcXd6VAtGT1yt7Bl7B4egbCCP7+9B6ffu1K3iTp8QV08X1gXPcEyEGlO9tIGda6bd3Y26wJtJdTqPpFr7ugNocsfQpHXheoiL5o6/fjDip2497Vt+jZffXwNPveHdyx7iovr6/GH9RTQn774Mf6++hMAqvA2dvrxywuPw9cXjNWuy3ic5u4AKosiA40XzB6JURUFKMlXo2O56nXPsciYQFtPEG09ARxs68WMkREhF+0oZo+KTjsUzBhZikm1RThbEnmZmXXq8S6XKp4B4Olr5uM32sAsoFoxHqcDVcWZn+80HZC4E9mLSdxP1r46j6ksAGMMJTEi93yLKE4OQl0OB1q6A/joE7U6sdsfHemWFUREYHJtJG/aHwrjjpc2Y8+xbn0awI7eiLjb9akRiIfGz/79MU761XL87r879OZVZmSbo8MXjIh7sRdNXX7c89o2PLhip55Fs0erzn1xXXThkEgb7QmEUehx6V0yb31e/abRoA3mLphYhTOmqEK63TS/bUu337It87BSNSo+0q5+juVbjuKe17bq/VfaewN6ps00rZsioOaGA8CsUfaROwC8et1CPPwV6/luh5fmY+9d52GhlkopmD+uEp8/PjKPqsPBUFeRj5rivlfSDiZI3ImsRU57u/SEUTh7uio4QhzlyPr6xRPx5ysicwoL4TptUrX+R6+YBlRlrPLGxcPD7WQG8bGycP710SGc/dtVMT8P155WwqIQx/nDyp167jVgzDJp6oqIfkdvEJ0+1ZapLvYa5pcVbXGFr7zcYrD1m39di8ZOH7r9IeR7nFFjGkc6fPA4Hagp9ur9fXY0dhlaDTR3BVBRGJ3VUlvshYOpYw5v7ziGq59sgNvpwF2fV/3wVTuO6Q+e8TWR3kF3L52J3106GxNMs2SZcWgVov3lnotm4qYl0ZOAZCMk7kTWMkmLlu+5aCbuWjoTc0aV40dLpuBXFx4HwOi/Xr94Es6cWouXv7cAr9+wUO9BMqoiX48U5WwZczKOlbiLh0ee22mwEqxSIR9csdPw3qqLpTi9aCf7qfGV+M0lsxAIKbj9hY/17eQe8nLR0L7mHuxv7cGEmiI9IhY88MYOhMIKDmuRs/yw+OJJow3H2NXUhVHl+Xqlqn6uDj9qSlQ/urLIi2ElefhwXyvG3foybnluI3zBMHoCYYMtI3A5HRhWkoeDbb34R8N+VBZ6sObWxThzag0unDMSyzYcxj2vbYPX5cDwEmOu+gWzY8/Fm0rmjqnAlGEl8TfMAkjciaxl8jA1whNC5XAwfGvReEPGxPfOnIjHv3aC/n7aiBJMrC3WB9dcDocu5LIt43QY/zSsWugKcc93OzFdGvBLpIDojHtX2mbNdPhCGF9diL9/fb5elQlA72vSIYn7U2siE2q8uukIOAdOmVBlmDLwN5fMwu6mbqzY1hQ1kPrctz+F286bqr/f39KDTYfaMXtUueEh9ZN/bcKRdp9eqQmo33qWbTysXccn+rcDu9myRpTl40BLL1Ztb8Jpk2qQ73GCMYb7Lp6FE+rL0dYTxMjyfDhs0iyJ5CBxJ7KWK08ZiwUTq/CFE6K7RAq+f9YknD65Jmq5iNxdDoZ5Wi70rLrIoF0i9Sal2iBhvseJacNLUOBxojjPZahateNQuw/f/tta3P/f7QDUHuqyE1+sZZdUFnlx2Ymjcd/Fs/Dri9TBP2HbtPcE8e/1h/D5OWpk+5/NR1DkdWFWXak+K9CYygJ9MFJMaiGPN9QUe1HgcWHd7Wqp/sptTfAFlSiP+//e34edTV0YJon7GVON9/W7WhOyibXWFsrI8nys2duC9t4gFk2OPLQcDoZvLBwPQP3mQKQGEncia6ks8uL/rjoJI8rsc6DtyNfE3elkOH1yDT76yVk4dWKVvj6ROVnL8tUI1etyIN/jxGvXL8QPPx3frz1pbAVGluXjv1sacf9/Vbtk/p1voEmyWOQp/+78/HFYOrcOZdo3BbHdrmNq6uSZU9WxBoUDx40shcvpwISaIlx16lj89aqToqo250tN14TQlxV4UFbg1lsAzxkV6WooaOpUbRnB4qm1uMKUgQIA00dY2xr10nUskO41AJw2uRouB8MNiyda7kskD4k7MSQZbSp2MfcJ6U6g8ZbI596rpfSNqiiIWf4u+Mc3Tsa1Z0zQ3z/x7t6o1ESrTJ96rWXszc9txIYDbXqp/7QRJXrqokj7czoYfnL+NIyqKDBUk75+w0KcPD5SkJMv9SAfoWW0jCzLx6iKfLxw7Sn68QSyLeN0MPz8ghm4+lRjKb5dPvmlWsUqY8ZMI0BtyLXjl+fg2jNI3FMFiTsxJFkwUbUF5OpRmXhdFb/6qXpcqNkh8uClbFvE4tITRuGahWoF58uaby1Tkhfd0680340XvnMKirwu/Pj5TdjV1A23k2FUeb4+scn0kdYpg1+ePxrThqvjDXLbWrkkX7TBPXFsBRhjGFddFNUXvbYkOhOmSvL37/z8cbafeXhpPh69fB5e+M4plutzoXBoMEFdIYkhySkTVFtgZp11cYyduJ973DB867QJOE6LaFf+YJEhMpbTM3/+2ek40uHDU2s+MVR6AqqQfXr6MDyyajc+1HLpZezmih1TWYjLTx6DO1/Zio0H2zGxpggupwN/vmIe/vz2HiyeGj2+AAC/+FxEdOVvKfLg5U8/Mx115fk4X2uWZXUdtRYPrwotCj9/5vC4k2Qstik0IlIPiTsxJCnNd2PtbYttq0zlzohelwPTRpTgo0/asGhyjS7sgGqVyMgR96iKfFzxqXr4gmE8/s5enDS2AhfNjRTNyA8Cj9NhqIIttojcBUtmDMOdr2xFfWUBfqmlfZYVeHDj2YnlZ08fUYLJtcX4nwumG5aXFrijZsAyd1K0EneRn2/XzI0YGEjciSFLpcWMTwI5cj9uZCke+spc/Pzfm3HucdYTNAgYY3jyyhPx4vpDmDtGHbi8/fxpOG1SNU6or0ChNEhZI9kZXzl5DDp9QTz/0UEEw1zf14oxlYV484eLMKIsv09tZGuK8/DaDQsT2tacrmkl7uLbzzlx7g2RWUjcCcKC82YOx+bDHbjjgulYMmM4qoq8+P1lcxLad6FU9Qqogr/IIh1TFubx1UX44kmjcfnJ9QiGFcyRJge3IpGB21SwYFI1TplQiXd2NqPI60KRN1oypg4vwdY7ltgOpBIDA0t2jsRUMW/ePN7Q0DAg5yaIeHDO4QsqhmySdLBiayPuX74DD1w6O2OC3RdOuesNeN2OjE0RR9jDGFvLOZ8XbzuK3AnCAsZY2oUdAE6fUoPTp1gPgg4mygvdtoO8xOCExJ0giLj89DPTbWcyIgYnJO4EQcQl2floiYGHcpcIgiByEBJ3giCIHITEnSAIIgchcScIgshBEhJ3xtgSxtg2xthOxtjNFuvHMMaWM8Y2MMZWMsbqrI5DEARBZIa44s4YcwL4A4BzAEwDcBljbJpps3sBPMk5nwngfwDcmeoLJQiCIBInkcj9RAA7Oee7OecBAE8DuMC0zTQAy7XXKyzWEwRBEBkkkTz3kQD2S+8PADjJtM16AEsB/A7AhQCKGWOVnPNmeSPG2DUArtHedjHGtvXpqoEqAMf6uG+uQvckGron0dA9iSbb7kn09FcWJCLuVh30zQ1pfgDgQcbYVwGsAnAQQFRDbM75IwAeSeTCYl4QYw2J9FYYStA9iYbuSTR0T6LJ1XuSiLgfACDPQFwH4JC8Aef8EIDPAwBjrAjAUs659RQ3BEEQRNpJxHP/AMBExthYxpgHwKUAXpQ3YIxVMcbEsW4B8FhqL5MgCIJIhrjizjkPAbgWwGsAtgB4hnP+MWPsfxhjn9U2WwRgG2NsO4BaAL9M0/UK+m3t5CB0T6KhexIN3ZNocvKeDFg/d4IgCCJ9UIUqQRBEDkLiThAEkYNknbjHa4WQqzDGHmOMNTLGNknLKhhjrzPGdmg/y7XljDH2gHaPNjDGjh+4K08fjLFRjLEVjLEtjLGPGWPXacuH7H1hjOUxxtYwxtZr9+Tn2vKxjLHV2j35h5YcAcaYV3u/U1tfP5DXn04YY07G2EeMsZe09zl9T7JK3BNshZCrPAFgiWnZzQCWc84nQq0QFg+7cwBM1P5dA+BPGbrGTBMCcCPnfCqA+QC+o/0+DOX74gdwBud8FoDZAJYwxuYDuBvAb7V70grgKm37qwC0cs4nAPittl2uch3UpBBBbt8TznnW/ANwMoDXpPe3ALhloK8rg5+/HsAm6f02AMO118MBbNNePwzgMqvtcvkfgBcAnEX3Rf98BQA+hFpRfgyAS1uu/x1BzYI7WXvt0rZjA33tabgXdVAf9GcAeAlqcWZO35Ositxh3Qph5ABdy2CglnN+GAC0n2Km5SF3n7SvznMArMYQvy+a/bAOQCOA1wHsAtDG1bRmwPi59XuirW8HUJnZK84I9wO4CYCiva9Ejt+TbBP3RFohEEPsPmlV0c8CuJ5z3hFrU4tlOXdfOOdhzvlsqNHqiQCmWm2m/cz5e8IYOx9AI+d8rbzYYtOcuifZJu5xWyEMMY4yxoYDgPazUVs+ZO4TY8wNVdj/xjl/Tls85O8LAHDO2wCshBB8uRcAAAE8SURBVDoeUcYYE+1G5M+t3xNtfSmAlsxeado5BcBnGWN7oXa1PQNqJJ/T9yTbxD1uK4QhxosArtBeXwHVcxbLL9eyQ+YDaBc2RS7BGGMA/gxgC+f8N9KqIXtfGGPVjLEy7XU+gMVQBxFXALhI28x8T8S9ugjAG1wzm3MFzvktnPM6znk9VM14g3P+JeT6PRlo078PAyPnAtgO1Uf88UBfTwY/91MADgMIQo0sroLqAy4HsEP7WaFty6BmFe0CsBHAvIG+/jTdk1Ohfl3eAGCd9u/coXxfAMwE8JF2TzYBuF1bPg7AGgA7AfwTgFdbnqe936mtHzfQnyHN92cRgJeGwj2h9gMEQRA5SLbZMgRBEEQCkLgTBEHkICTuBEEQOQiJO0EQRA5C4k4QBJGDkLgTBEHkICTuBEEQOcj/B7ePyOwB6WFLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(moving_average(test_rmses, n=1))\n",
    "plt.ylim(0.9,1.4)\n",
    "plt.hlines(1,0,len(test_rmses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:44:38.442003Z",
     "start_time": "2019-03-07T12:44:38.301920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1adedd7e48>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(moving_average(test_rmses, n=50))\n",
    "plt.ylim(0.9,1.4)\n",
    "plt.hlines(1,0,len(test_rmses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:43:46.521932Z",
     "start_time": "2019-03-07T12:43:46.516445Z"
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   "outputs": [
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      "text/plain": [
       "392"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(moving_average(test_rmses, n=50))"
   ]
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   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:37:03.286418Z",
     "start_time": "2019-03-07T12:37:03.282752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "441"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_rmses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:37:09.295711Z",
     "start_time": "2019-03-07T12:37:09.290910Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(moving_average(test_rmses, n=50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-07T12:37:14.462357Z",
     "start_time": "2019-03-07T12:37:14.458339Z"
    }
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   "outputs": [
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     "data": {
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      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(test_rmses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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