tensorneat-mend/tensorneat/test/test_flatten.ipynb

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   "source": [
    "from algorithm.neat.genome import DefaultGenome\n",
    "from utils.tools import flatten_conns, unflatten_conns\n",
    "import jax, jax.numpy as jnp\n",
    "from jax import vmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "((10, 5), (10, 4))"
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "genome = DefaultGenome(num_inputs=3, num_outputs=2, max_nodes=10, max_conns=10)\n",
    "state = genome.setup()\n",
    "key = jax.random.PRNGKey(0)\n",
    "nodes, conns = genome.initialize(state, key)\n",
    "nodes.shape, conns.shape"
   ],
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    "collapsed": false,
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     "end_time": "2024-05-30T11:40:59.021858400Z",
     "start_time": "2024-05-30T11:40:55.592593400Z"
    }
   },
   "id": "89fb5cd0e77a028d"
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  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "(2, 10, 10)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unflatten = unflatten_conns(nodes, conns)\n",
    "unflatten.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-30T11:40:59.472701700Z",
     "start_time": "2024-05-30T11:40:59.021858400Z"
    }
   },
   "id": "aaa88227bbf29936"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(Array([[ 0.        ,  5.        ,  1.        , -0.41923347],\n        [ 1.        ,  5.        ,  1.        , -3.1815007 ],\n        [ 2.        ,  5.        ,  1.        ,  0.5184341 ],\n        [ 5.        ,  3.        ,  1.        , -1.9784615 ],\n        [ 5.        ,  4.        ,  1.        ,  0.7132204 ],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan]],      dtype=float32, weak_type=True),\n Array([[ 0.        ,  5.        ,  1.        , -0.41923347],\n        [ 1.        ,  5.        ,  1.        , -3.1815007 ],\n        [ 2.        ,  5.        ,  1.        ,  0.5184341 ],\n        [ 5.        ,  3.        ,  1.        , -1.9784615 ],\n        [ 5.        ,  4.        ,  1.        ,  0.7132204 ],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan],\n        [        nan,         nan,         nan,         nan]],      dtype=float32))"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# single flatten\n",
    "flatten = flatten_conns(nodes, unflatten, C=10)\n",
    "conns, flatten"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-30T11:41:00.308954100Z",
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  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "((3, 10, 5), (3, 10, 4))"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# batch_flatten\n",
    "key = jax.random.PRNGKey(0)\n",
    "keys = jax.random.split(key, 3)\n",
    "pop_nodes, pop_conns = jax.vmap(genome.initialize, in_axes=(None, 0))(state, keys)\n",
    "pop_nodes.shape, pop_conns.shape"
   ],
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pop_unflatten = jax.vmap(unflatten_conns)(pop_nodes, pop_conns)\n",
    "pop_unflatten.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
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  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "(3, 10, 4)"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flatten = jax.vmap(flatten_conns, in_axes=(0, 0, None))(pop_nodes, pop_unflatten, 10)\n",
    "flatten.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-30T11:43:39.983690700Z",
     "start_time": "2024-05-30T11:43:39.208549Z"
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   },
   "id": "8e5cdf6140c81dc0"
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