{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class cvg:\n",
    "    def __init__(self, attributes, ground_truth, n_test=2, order_dim=0):\n",
    "        self._order        = order_dim\n",
    "        self._ntests       = n_test\n",
    "        self._actual_ntest = 0\n",
    "        self._size         = attributes.shape[order_dim]\n",
    "        self._att          = attributes\n",
    "        self._gt           = ground_truth\n",
    "        \n",
    "        if attributes.shape[0] != ground_truth.shape[0] or \\\n",
    "           attributes.shape[1] != ground_truth.shape[1] :\n",
    "                raise ValueError('attributes and ground_truth must have the same 2D shape')\n",
    "        \n",
    "    def __iter__(self):\n",
    "        return self\n",
    "    \n",
    "    def __next__(self):\n",
    "        if self._actual_ntest == self._ntests:\n",
    "            raise StopIteration\n",
    "        \n",
    "        step = self._size / self._ntests\n",
    "        train_filter = (np.arange(self._size) - step * self._actual_ntest) % self._size < step\n",
    "        \n",
    "        if self._order == 0:\n",
    "            Xtrain = self._att[train_filter].reshape(-1, self._att.shape[2])\n",
    "            Xtest  = self._att[train_filter == False].reshape(-1, self._att.shape[2])\n",
    "            Ytrain = self._gt[train_filter].reshape(-1, 1)\n",
    "            Ytest  = self._gt[train_filter == False].reshape(-1, 1)\n",
    "        else:\n",
    "            Xtrain = self._att[:,train_filter].reshape(-1, self._att.shape[2])\n",
    "            Xtest  = self._att[:,train_filter == False].reshape(-1, self._att.shape[2])\n",
    "            Ytrain = self._gt[:,train_filter].reshape(-1, 1)\n",
    "            Ytest  = self._gt[:,train_filter == False].reshape(-1, 1)\n",
    "\n",
    "        \n",
    "        self._actual_ntest += 1\n",
    "        \n",
    "        return (Xtrain, Xtest, Ytrain, Ytest, train_filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.arange(100*200*10).reshape(100, 200, 10)\n",
    "Y = np.arange(100 * 200).reshape(100, 200)\n",
    "\n",
    "for xn, xt, yn, yt, t in cvg(X, Y, 10, 1):\n",
    "        disp = np.zeros(Y.shape)\n",
    "        disp[:,t] = 1.\n",
    "        plt.imshow(disp)\n",
    "        plt.show()\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "from CrossValidationGenerator import APsCVG\n",
    "sys.path.append('../triskele/python')\n",
    "import triskele"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt = triskele.read('../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif')\n",
    "att = np.array((gt, gt))\n",
    "att = np.moveaxis(att, 0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for xt, xv, yt, yv, ti in APsCVG(gt, att, 1):\n",
    "    print(xt.shape, yt.shape, xv.shape, yv.shape)\n",
    "    plt.imshow(ti * 1.)\n",
    "    plt.show()"
   ]
  }
 ],
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