batching-proof.ipynb

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   "source": [
    "import numpy as np\n",
    "\n",
    "from audio_batch import get_valid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------"
   ]
  },
  {
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     "text": [
      "..................................................\n",
      "valid_in is 0.013269% empty in the worst case\n"
     ]
    }
   ],
   "source": [
    "sin_pre = np.load('./data/lowpass/pre/sin.npz')['data']\n",
    "sqr_pre = np.load('./data/lowpass/pre/square.npz')['data']\n",
    "saw_pre = np.load('./data/lowpass/pre/plysaw.npz')['data']\n",
    "\n",
    "sin_post = np.load('./data/lowpass/post/sin.npz')['data']\n",
    "sqr_post = np.load('./data/lowpass/post/square.npz')['data']\n",
    "saw_post = np.load('./data/lowpass/post/plysaw.npz')['data']\n",
    "\n",
    "set_in = np.concatenate([sin_pre[0], sin_pre[1], sqr_pre[0], sqr_pre[1], saw_pre[0], saw_pre[1]])\n",
    "set_out = np.concatenate([sin_post[0], sin_post[1], sqr_post[0], sqr_post[1], saw_post[0], saw_post[1]])\n",
    "\n",
    "\n",
    "sets = []\n",
    "for j in range(50):\n",
    "    train_in, train_out, valid_in, valid_out = get_valid(set_in, set_out, 1, .25)\n",
    "\n",
    "    blank = []\n",
    "    for i in range(len(valid_in)):\n",
    "        if valid_in[i] == 0.0:\n",
    "            blank.append(i)\n",
    "\n",
    "    sets.append(len(blank) / len(valid_in))\n",
    "    print('.', end=\"\", flush=True)\n",
    "    \n",
    "print()\n",
    "print('valid_in is %f%% empty in the worst case' % (np.asarray(sets).max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "<style>.container { width:100% !important; }</style>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  }
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