tensorneat-mend/tensorneat/examples/interpret_visualize/genome_sympy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import jax, jax.numpy as jnp\n",
    "\n",
    "from algorithm.neat import *\n",
    "from algorithm.neat.genome.advance import AdvanceInitialize\n",
    "from algorithm.neat.gene.node.default_without_response import NodeGeneWithoutResponse\n",
    "from utils.graph import topological_sort_python\n",
    "from utils import Act, Agg"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T11:35:46.886073700Z",
     "start_time": "2024-06-12T11:35:46.042288800Z"
    }
   },
   "id": "9531a569d9ecf774"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "genome = AdvanceInitialize(\n",
    "    num_inputs=3,\n",
    "    num_outputs=1,\n",
    "    hidden_cnt=1,\n",
    "    max_nodes=50,\n",
    "    max_conns=500,\n",
    "    node_gene=NodeGeneWithoutResponse(\n",
    "        # activation_default=Act.tanh,\n",
    "        aggregation_default=Agg.sum,\n",
    "        # activation_options=(Act.tanh,),\n",
    "        aggregation_options=(Agg.sum,),\n",
    "    )\n",
    ")\n",
    "\n",
    "state = genome.setup()\n",
    "\n",
    "randkey = jax.random.PRNGKey(42)\n",
    "nodes, conns = genome.initialize(state, randkey)\n",
    "\n",
    "network = genome.network_dict(state, nodes, conns)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T11:35:52.274062400Z",
     "start_time": "2024-06-12T11:35:46.892042200Z"
    }
   },
   "id": "4013c9f9d5472eb7"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "[-0.535*sigmoid(0.346*i0 + 0.044*i1 - 0.482*i2 + 0.875) - 0.264]"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sympy as sp\n",
    "\n",
    "symbols, input_symbols, nodes_exprs, output_exprs, forward_func = genome.sympy_func(state, network, precision=3, )\n",
    "output_exprs"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T11:35:52.325161800Z",
     "start_time": "2024-06-12T11:35:52.282008300Z"
    }
   },
   "id": "addea793fc002900"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "- 0.535 \\mathrm{sigmoid}\\left(0.346 i_{0} + 0.044 i_{1} - 0.482 i_{2} + 0.875\\right) - 0.264\n"
     ]
    }
   ],
   "source": [
    "print(sp.latex(output_exprs[0]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T11:35:52.341639700Z",
     "start_time": "2024-06-12T11:35:52.323163700Z"
    }
   },
   "id": "967cb87e24373f77"
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "88eee4db9eb857cd"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "[-0.7940936986556304]"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "random_inputs = np.random.randn(3)\n",
    "res = forward_func(random_inputs)\n",
    "res "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T11:35:52.342638Z",
     "start_time": "2024-06-12T11:35:52.330160600Z"
    }
   },
   "id": "c5581201d990ba1c"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "Array([-0.7934886], dtype=float32, weak_type=True)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed = genome.transform(state, nodes, conns)\n",
    "res = genome.forward(state, transformed, random_inputs)\n",
    "res"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T11:35:53.273851900Z",
     "start_time": "2024-06-12T11:35:52.384588600Z"
    }
   },
   "id": "fe3449a5bc688bc3"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-06-12T11:35:53.274854100Z",
     "start_time": "2024-06-12T11:35:53.265856700Z"
    }
   },
   "id": "174c7dc3d9499f95"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}