Notebooks/GhosalHTMExperiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2cad2ebf-71ba-4b47-bd02-ebd81d60f925",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Work around for relative imports\n",
    "if \"..\" not in sys.path:\n",
    "    sys.path.insert(0,\"..\")\n",
    "\n",
    "from Scripts.htm_model_v4 import network, G, cell, segment, synapse, sparse_encoder_decoder\n",
    "import Scripts\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = [15, 7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1af9677-f2eb-41e5-8373-c5c443e61f95",
   "metadata": {},
   "source": [
    "# Investigating Dipak Ghosals code: Reconnection and Higher Order Behavior\n",
    "\n",
    "[Turns out that Numenta starts off with 0 segments per cell.](https://discourse.numenta.org/t/htm-learning-algorithm-temporal-memory/8884/14?u=djpasseyjr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16780ad4-cd7b-40e0-b749-d2430be00f71",
   "metadata": {},
   "outputs": [],
   "source": [
    "class G:\n",
    "    # WORKING_DIRECTORY = \"/Users/dghosal/Documents/HTM-Work\"\n",
    "    # WORKING_DIRECTORY = os.getcwd()\n",
    "\n",
    "    # Network paramaters\n",
    "    number_of_columns = 100\n",
    "    cells_per_column = 10\n",
    "    \n",
    "    \n",
    "    list_of_column_ids = list(range(1,number_of_columns+1))\n",
    "    list_of_ids_in_a_column = list(range(1,cells_per_column+1))\n",
    "    \n",
    "    # cell_id = (colum_id, cell_in_column_id)\n",
    "    \n",
    "    \n",
    "    # Cell parameters\n",
    "    number_of_segments_min = 64\n",
    "    number_of_segments_max = 64\n",
    "    fixed_number_of_segments = 10\n",
    "    \n",
    "    \n",
    "    # segment parameters\n",
    "    max_num_synapses = 30\n",
    "    min_num_synapses = 20\n",
    "    fixed_num_synapses = 25\n",
    "    \n",
    "    theta = 5\n",
    "    \n",
    "     \n",
    "    # synapse parameter\n",
    "    \n",
    "    max_p_value = 1\n",
    "    min_p_value = 0\n",
    "\n",
    "    alpha = 0.3 \n",
    "    \n",
    "    \n",
    "    # synaptic strength update parameters \n",
    "    \n",
    "    small_decrement = -0.05\n",
    "    very_small_decrement = -0.01\n",
    "    large_increment = 0.36\n",
    "    \n",
    "    \n",
    "    # encoding parameters  \n",
    "    encoding_length = 10  # number of columns activated for every input. \n",
    "    encoding_range = number_of_columns # \n",
    "    \n",
    "    \n",
    "    # This file will contain the encodings\n",
    "    encodings_filename = \"encodings_file.txt\"\n",
    "    \n",
    "    \n",
    "    \n",
    "    # epoch length\n",
    "    \n",
    "    epoch_length = 12\n",
    "    \n",
    "    # training file\n",
    "    # pre_training_filename = \"tkamb_training.txt\"\n",
    "    #training_filename = \"tkamb_training.txt\"\n",
    "    #training_filename_long = \"tkamb_training_long.txt\"\n",
    "    #training_filename = \"tkamb.txt\"\n",
    "    \n",
    "    #training_filename = \"training_file-two-inputs.txt\"\n",
    "\n",
    "    # training_filename = \"mapped_data_22093.txt\"\n",
    "    \n",
    "    # testing file\n",
    "    # testing_filename = \"testing_file.txt\"\n",
    "    \n",
    "    # output file\n",
    "    # output_filename = \"output_file_v4_sm00_22093.txt\"\n",
    "    \n",
    "    # pickle file to store the network\n",
    "    # pickle_filename = \"my_network_v4_sm00.p\"\n",
    "    \n",
    "    \n",
    "# Monkey typing to avoid editing the file\n",
    "Scripts.htm_model_v4.G = G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb2382d0-3b41-4dee-bb1c-327f61804ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TempMem(network):\n",
    "                \n",
    "    def train_network(self, sdr, input_sequence):\n",
    "        epoch_number = 1\n",
    "\n",
    "        for char in input_sequence:\n",
    "            input_char = char.lower()\n",
    "            # Encode character as a feed forward input\n",
    "            self.feed_forward_input = sdr.sparse_encode(input_char)\n",
    "            \n",
    "            # compute_cell_actovity_state\n",
    "            self.compute_cell_activity_state(self.feed_forward_input)\n",
    "                        \n",
    "            # Accuracy\n",
    "            predicted_active_overlapp = set(self.list_of_cells_predicted).intersection(set(self.active_cells_current))\n",
    "            list_of_columns_predicted = []\n",
    "            for cell_predicted in self.list_of_cells_predicted:\n",
    "                list_of_columns_predicted.append(cell_predicted[0])   \n",
    "            self.prediction_accuracy.append(len(predicted_active_overlapp)/len(self.feed_forward_input))\n",
    "\n",
    "            # update the synpatic strengths\n",
    "            self.update_segment_and_synapse(self.feed_forward_input)\n",
    "\n",
    "            # update_cell_predictive_state\n",
    "            self.compute_cell_predictive_state(self.feed_forward_input)\n",
    "            \n",
    "class TempMemNoReconnect(TempMem):\n",
    "            \n",
    "    def update_segment_and_synapse(self, feed_forward_input):\n",
    "        \"\"\"We comment out synapse reconnection from this function.\n",
    "        \"\"\"\n",
    "        \n",
    "        \n",
    "        # Consider the three cases here all with respect to the set of cell in the columns \n",
    "        # correspoinding to the feedforward input\n",
    "        \n",
    "        # Case 1: cell was in predictive state and got activated\n",
    "        #         These cell ids are in self.active_cells_predicted = []\n",
    "        \n",
    "        # Consider each cell at a time\n",
    "        #print(\"List of active cells predicted {0}\".format(self.active_cells_predicted))\n",
    "        for cell_id in self.active_cells_predicted:\n",
    "            \n",
    "            # get all the segments that caused the cell to be in the predictive state in the previous\n",
    "            #  timestep\n",
    "\n",
    "\n",
    "            # To do this consider the previous active list and find all the active segments \n",
    "            # for which the intersection with tildeD is greater than theta\n",
    "            self.cell_objects[cell_id].determine_segment_id_causing_predictive_state(self.active_cells_previous)\n",
    "            \n",
    "            \n",
    "            # Find the man\n",
    "            \n",
    "            for segment_id in self.cell_objects[cell_id].segment_ids_causing_predictive_state.keys():\n",
    "                \n",
    "                # For each segment we update the synaptic strengths as follows \n",
    "                # self.cell_objects[cell_id].cell_segments[segment_id].print_segment_synapse_connection_strenght()\n",
    "                # We decrement the p_value of all the synapses in dotdij\n",
    "                self.cell_objects[cell_id].cell_segments[segment_id].update_all_synapses_in_dotdij(G.small_decrement)\n",
    "                \n",
    "                # next we find the overlapp of a(t-1) with dotdij and increment the  corresponding\n",
    "                # synapses by a large value\n",
    "                self.cell_objects[cell_id].cell_segments[segment_id].update_all_synapses_with_active_presynaptic_cell(self.active_cells_previous, G.large_increment)\n",
    "                \n",
    "        \n",
    "        # Case 2: No cell in these winning columns were in a predictive state\n",
    "        # Hence all the cell in the columns became active\n",
    "        \n",
    "        # This is a set of column_ids \n",
    "\n",
    "        # for each column in the set \n",
    "        #print(self.active_cell_notpredicted_column_ids)\n",
    "        #print(\"List of cells not-predicted column ids {0}\".format(self.active_cell_notpredicted_column_ids))\n",
    "        for i in self.active_cell_notpredicted_column_ids:\n",
    "            max_cell_id = ()\n",
    "            max_segment_id = -1\n",
    "            max_segment_overlapp = set()\n",
    "            for j in G.list_of_ids_in_a_column:\n",
    "                cell_id = (i,j)\n",
    "                # get a list of segment id and their overlapps with the previous active cells\n",
    "                self.cell_objects[cell_id].determine_segment_id_strengths(self.active_cells_previous)\n",
    "                for segment_id in self.cell_objects[cell_id].segment_id_strengths.keys():\n",
    "                    if len(self.cell_objects[cell_id].segment_id_strengths[segment_id]) > len(max_segment_overlapp):\n",
    "                        max_cell_id = cell_id\n",
    "                        max_segment_id = segment_id\n",
    "                        max_segment_overlapp = self.cell_objects[cell_id].segment_id_strengths[segment_id]\n",
    "                \n",
    "            # At this point we have the following case\n",
    "                        \n",
    "            # Case 1: We have found a segment that has some strength max_strength > 0 \n",
    "            #         In thism case we find the number of additional synapses and update\n",
    "            #         their strengths\n",
    "            \n",
    "            #print(\"Max cell_id: {0}, max_segment_id: {1}, len of overlap: {2}\".format(max_cell_id, max_segment_id, len(max_segment_overlapp)))\n",
    "            \n",
    "#             if (len(max_segment_overlapp) > 0):\n",
    "#                  num_synapses_to_reconnect = G.theta - len(max_segment_overlapp) + 1\n",
    "#                  self.cell_objects[max_cell_id].cell_segments[max_segment_id].update_and_reconnect_synapses(num_synapses_to_reconnect, max_segment_overlapp, self.active_cells_previous)\n",
    "            \n",
    "            # # Case 2: max_strength > 0 \n",
    "            # #     In this case we consider a random segment and consider the lowest G.theta + synapases \n",
    "            # #     and reassign remote id's and update strengths\n",
    "#             elif (len(max_segment_overlapp) == 0):\n",
    "#                 j = np.random.randint(1, G.cells_per_column+1)\n",
    "#                 max_cell_id = (i,j)\n",
    "#                 self.cell_objects[max_cell_id]\n",
    "#                 max_segment_id = self.cell_objects[max_cell_id].num_segments + 1\n",
    "                \n",
    "#                 num_synapses_to_reconnect = G.theta + 1\n",
    "#                 self.cell_objects[max_cell_id].cell_segments[max_segment_id].update_and_reconnect_synapses(num_synapses_to_reconnect, max_segment_overlapp, self.active_cells_previous)  \n",
    "        \n",
    "        # Case 3: cells that did not  become active but had active dendritic segments \n",
    "        #         For these segments we decay the synaptic strenghts\n",
    "        for cell_id in self.cells_not_activated:\n",
    "            \n",
    "            # We find the strengths of the segments \n",
    "            self.cell_objects[cell_id].determine_segment_id_strengths(self.active_cells_previous)\n",
    "            for segment_id in self.cell_objects[cell_id].segment_strengths.keys(): \n",
    "                if self.cell_objects[cell_id].segment_strengths[segment_id] > G.theta:\n",
    "                    # For the active segments decrement p_value and dotdij\n",
    "                    for synapse_id in self.cell_objects[cell_id].segment_strengths[segment_id].segment_synapses.keys():\n",
    "                        self.cell_objects[cell_id].segment_strengths[segment_id].update_synapse(synapse_id, G.very_small_decrement, ())\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d299c230-6317-4ec9-9b6c-9e69555e72c2",
   "metadata": {},
   "source": [
    "### Normal algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc4dfe74-ce55-4d30-9ea3-b2998a0e9594",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = sparse_encoder_decoder(32)\n",
    "on_off = \"oa\"\n",
    "for char in on_off:\n",
    "    enc.sparse_encode(char)\n",
    "    \n",
    "tm = TempMem(G.number_of_columns, G.cells_per_column)\n",
    "tm.initialize_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2db4473-a81f-44a9-a269-0ba1e1052ab1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n",
      "Error reconnecting synapses: len of remote  cell ids 0 < number of synapses to reconnect 6\n"
     ]
    }
   ],
   "source": [
    "predictions = []\n",
    "\n",
    "for i in range(100):\n",
    "    tm.train_network(enc, on_off)\n",
    "    predictions.append(tm.list_of_cells_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1163a79d-db59-4aa5-b5da-0abcd53a4515",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize_predictions(active_cell_idxs, ncols, nrows):\n",
    "    A = np.zeros((ncols, nrows))\n",
    "    for i, j in active_cell_idxs:\n",
    "        A[i-1, j-1] = 1.0\n",
    "    return np.ravel(A)\n",
    "\n",
    "def raster_matrix(predictions, ncols, nrows):\n",
    "    R = np.vstack([vectorize_predictions(p, ncols, nrows) for p in predictions])\n",
    "    return R.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e5d10f02-08cc-426a-9c1f-f3fb521dc0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x126abf490>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "R = raster_matrix(predictions, G.number_of_columns, G.cells_per_column)\n",
    "plt.imshow(R, aspect=\"auto\", interpolation=\"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3fa5392a-0d93-44c7-8202-08dea81205e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 1.0,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.9375,\n",
       " 0.09375,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.9375,\n",
       " 0.03125,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.9375,\n",
       " 0.90625,\n",
       " 0.5,\n",
       " 1.0,\n",
       " 0.65625,\n",
       " 1.0,\n",
       " 0.53125,\n",
       " 1.0,\n",
       " 0.6875,\n",
       " 1.0,\n",
       " 0.0625,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.0625,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.9375,\n",
       " 0.96875,\n",
       " 0.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.96875,\n",
       " 0.34375,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.78125,\n",
       " 1.0,\n",
       " 0.21875,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.21875,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.125,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.90625,\n",
       " 0.28125,\n",
       " 1.0,\n",
       " 0.9375,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.875,\n",
       " 0.96875,\n",
       " 0.09375,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.90625,\n",
       " 0.96875,\n",
       " 0.09375,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.90625,\n",
       " 0.96875,\n",
       " 0.9375,\n",
       " 0.15625,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.875,\n",
       " 1.0,\n",
       " 0.09375,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.6875,\n",
       " 1.0,\n",
       " 0.625,\n",
       " 1.0,\n",
       " 0.15625,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.90625,\n",
       " 0.96875,\n",
       " 0.9375,\n",
       " 0.28125,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.90625,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.21875,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.90625,\n",
       " 0.96875,\n",
       " 0.96875,\n",
       " 0.96875,\n",
       " 0.25,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.90625,\n",
       " 0.96875,\n",
       " 0.25,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 0.90625,\n",
       " 0.96875,\n",
       " 0.96875,\n",
       " 0.71875,\n",
       " 1.0,\n",
       " 0.65625,\n",
       " 1.0,\n",
       " 0.5,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.9375,\n",
       " 0.375,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.9375,\n",
       " 0.9375,\n",
       " 0.9375,\n",
       " 1.0,\n",
       " 0.46875,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.1875,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.78125,\n",
       " 1.0,\n",
       " 0.1875,\n",
       " 1.0,\n",
       " 1.0,\n",
       " 0.96875,\n",
       " 1.0,\n",
       " 0.9375,\n",
       " 0.9375]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.prediction_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09005d43-7a75-4115-8c66-1aed2244c865",
   "metadata": {
    "tags": []
   },
   "source": [
    "### No synapse rewiring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f2398f8-d2d9-4dc0-b227-e5c31a55c38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "enc = sparse_encoder_decoder(32)\n",
    "tm = TempMemNoReconnect(G.number_of_columns, G.cells_per_column)\n",
    "tm.initialize_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b925caa6-f618-4055-9929-b737438977ae",
   "metadata": {},
   "outputs": [
    {
     "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/var/folders/mh/dtmtv51x0dvfvh316ntx8jg00000gn/T/ipykernel_7827/3132114570.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\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[0;36m100\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----> 4\u001b[0;31m     \u001b[0mtm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_network\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mon_off\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      5\u001b[0m     \u001b[0mpredictions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlist_of_cells_predicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/mh/dtmtv51x0dvfvh316ntx8jg00000gn/T/ipykernel_7827/2821497149.py\u001b[0m in \u001b[0;36mtrain_network\u001b[0;34m(self, sdr, input_sequence)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m             \u001b[0;31m# update the synpatic strengths\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_segment_and_synapse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeed_forward_input\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     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m             \u001b[0;31m# update_cell_predictive_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/mh/dtmtv51x0dvfvh316ntx8jg00000gn/T/ipykernel_7827/2821497149.py\u001b[0m in \u001b[0;36mupdate_segment_and_synapse\u001b[0;34m(self, feed_forward_input)\u001b[0m\n\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m             \u001b[0;31m# We find the strengths of the segments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell_objects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcell_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetermine_segment_id_strengths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactive_cells_previous\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    119\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0msegment_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell_objects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcell_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msegment_strengths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\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    120\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell_objects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcell_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msegment_strengths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msegment_id\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Projects/HTM/Scripts/htm_model_v4.py\u001b[0m in \u001b[0;36mdetermine_segment_id_strengths\u001b[0;34m(self, at)\u001b[0m\n\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0msegment_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell_segments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\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--> 511\u001b[0;31m             \u001b[0moverlapp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcell_segments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msegment_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdotdij_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mat\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    512\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msegment_id_strengths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0msegment_id\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0moverlapp\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    513\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "predictions = []\n",
    "\n",
    "for i in range(100):\n",
    "    tm.train_network(enc, on_off)\n",
    "    predictions.append(tm.list_of_cells_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9af3a2b6-d7a8-455c-b6c1-e1bffb1e23c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b769e688-0fb1-4ee4-8fdf-3c7dc4df2898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11ffd22d0>"
      ]
     },
     "execution_count": 15,
     "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": [
    "R = raster_matrix(predictions, G.number_of_columns, G.cells_per_column)\n",
    "plt.imshow(R, aspect=\"auto\", interpolation=\"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e5f78b71-8e8c-422e-897b-160532147f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "506"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.prediction_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05ca04bd-d3b3-400a-94bc-0354f5379323",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}