{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  7,  8,  9, 10]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "state = np.array([[1, 2, 3], [1, 2, 3]])\n",
    "aaa = np.array([[123]])\n",
    "\n",
    "state[:, -1]\n",
    "\n",
    "np.append([[1, 2, 3]], [[7, 8, 9, 10]], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "aa = tf.constant([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])\n",
    "bb = tf.constant([1, 2, 3, 4])\n",
    "\n",
    "print(tf.expand_dims(bb, axis=1))\n",
    "\n",
    "cc = tf.math.multiply(aa, tf.expand_dims(bb, axis=1))\n",
    "\n",
    "print(cc)\n",
    "print(tf.shape(aa))\n",
    "\n",
    "print(aa[:, 2:3])\n",
    "\n",
    "aa = tf.constant([1.0, 2.0, 3.0, np.nan])\n",
    "print(np.any(tf.math.is_nan(aa)))\n",
    "if np.any(tf.math.is_nan(aa)):\n",
    "    print(\"true\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "prob = tf.constant([0.3, 0.3, 0.0])\n",
    "\n",
    "entropy = tf.reduce_mean(\n",
    "    tf.math.negative(tf.math.multiply(prob, tf.math.log(prob)))\n",
    ")\n",
    "\n",
    "print(entropy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int 23\n",
      "str twenty three\n",
      "bool True\n",
      "error\n"
     ]
    }
   ],
   "source": [
    "from functools import singledispatchmethod\n",
    "\n",
    "\n",
    "class person:\n",
    "    @singledispatchmethod\n",
    "    def age(self, arg):\n",
    "        print(\"error\")\n",
    "\n",
    "    @age.register(int)\n",
    "    def _(self, arg: int):\n",
    "        print(\"int\", arg)\n",
    "\n",
    "    @age.register(str)\n",
    "    def _(self, arg: str):\n",
    "        print(\"str\", arg)\n",
    "\n",
    "    @age.register(bool)\n",
    "    def _(self, arg: bool):\n",
    "        print(\"bool\", arg)\n",
    "\n",
    "\n",
    "p = person()\n",
    "p.age(23)  # int\n",
    "p.age(\"twenty three\")  # str\n",
    "p.age(True)  # bool\n",
    "p.age([\"23\"])  # list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras.layers as layers\n",
    "\n",
    "\n",
    "def conv_bn_relu(inputs, chs, reps):\n",
    "    x = inputs\n",
    "    for i in range(reps):\n",
    "        x = layers.Conv2D(chs, 3, padding=\"same\")(x)\n",
    "        x = layers.BatchNormalization()(x)\n",
    "        x = layers.ReLU()(x)\n",
    "    return x\n",
    "\n",
    "\n",
    "def create_model():\n",
    "    inputs = layers.Input((32, 32, 3))\n",
    "    x = conv_bn_relu(inputs, 64, 3)\n",
    "    x = layers.AveragePooling2D(2)(x)\n",
    "    x = conv_bn_relu(x, 128, 3)\n",
    "    x = layers.AveragePooling2D(2)(x)\n",
    "    x = conv_bn_relu(x, 256, 3)\n",
    "    x = layers.GlobalAveragePooling2D()(x)\n",
    "    x = layers.Dense(10, activation=\"softmax\")(x)\n",
    "    return tf.keras.models.Model(inputs, x)\n",
    "\n",
    "\n",
    "def perprocess(img, label):\n",
    "    img = tf.cast(img, tf.float32) / 255.0\n",
    "    label = tf.cast(label, tf.float32)\n",
    "    return img, label\n",
    "\n",
    "\n",
    "def train():\n",
    "    (X_train, y_train), (\n",
    "        X_test,\n",
    "        y_test,\n",
    "    ) = tf.keras.datasets.cifar10.load_data()\n",
    "    trainset = (\n",
    "        tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
    "        .map(perprocess)\n",
    "        .shuffle(4096)\n",
    "        .batch(128)\n",
    "        .repeat()\n",
    "        .prefetch(50)\n",
    "    )\n",
    "\n",
    "    model = create_model()\n",
    "    model.compile(\"adam\", \"sparse_categorical_crossentropy\", [\"acc\"])\n",
    "\n",
    "    model.fit(trainset, steps_per_epoch=50000 // 128, epochs=1)\n",
    "    # 'Adam/conv2d/kernel/m:0' shape=(3, 3, 3, 64)\n",
    "    print(model.optimizer.weights[1][0, 0, 0, :10])\n",
    "    # <tf.Variable 'conv2d/kernel:0' shape=(3, 3, 3, 64)\n",
    "    print(model.weights[0][0, 0, 0, :10])\n",
    "\n",
    "    model.save_weights(\"model_tf.ckpt\", save_format=\"tf\")  # デフォルト\n",
    "    model.save_weights(\"model_h5.h5\", save_format=\"h5\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "390/390 [==============================] - 28s 59ms/step - loss: 1.2548 - acc: 0.5441\n",
      "tf.Tensor(\n",
      "[ 0.00190057 -0.00765918  0.00163367  0.00782851  0.02600338  0.00516749\n",
      " -0.00424899  0.01562062 -0.0022073  -0.00355565], shape=(10,), dtype=float32)\n",
      "tf.Tensor(\n",
      "[ 0.07978954 -0.04595745 -0.03745254 -0.03701654  0.03296526 -0.11328737\n",
      " -0.10719797  0.00874998  0.0226855   0.02288487], shape=(10,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "train()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[ 0.07978954 -0.04595745 -0.03745254 -0.03701654  0.03296526 -0.11328737\n",
      " -0.10719797  0.00874998  0.0226855   0.02288487], shape=(10,), dtype=float32)\n",
      "tf.Tensor(\n",
      "[ 0.00190057 -0.00765918  0.00163367  0.00782851  0.02600338  0.00516749\n",
      " -0.00424899  0.01562062 -0.0022073  -0.00355565], shape=(10,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "def load_tf_w_zero_grad():\n",
    "    model = create_model()\n",
    "    model.compile(\"adam\", \"sparse_categorical_crossentropy\", [\"acc\"])\n",
    "\n",
    "    zero_grad = [tf.zeros_like(x) for x in model.weights]\n",
    "    model.optimizer.apply_gradients(zip(zero_grad, model.weights))\n",
    "\n",
    "    model.load_weights(\"model_tf.ckpt\")\n",
    "    # これでようやくオプティマイザーの値も同一になる\n",
    "    print(model.weights[0][0, 0, 0, :10])\n",
    "    print(model.optimizer.weights[1][0, 0, 0, :10])\n",
    "\n",
    "\n",
    "load_tf_w_zero_grad()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([10, 20, 30, 0])\n",
    "\n",
    "np.asarray([[0.]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "asd = [1,2,3,np.array([0.5]),np.array([0.5])]\n",
    "\n",
    "asd[3:]\n",
    "len(asd)\n",
    "\n",
    "np.mean([1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import pyautogui as pag\n",
    "\n",
    "from pynput.mouse import Button, Controller\n",
    "\n",
    "w = pag.size().width\n",
    "h = pag.size().height\n",
    "mouse = Controller()\n",
    "\n",
    "nowt = time.time()\n",
    "\n",
    "middletime = time.time() - nowt\n",
    "print(middletime)\n",
    "# print(nowPos-(w/2))\n",
    "\n",
    "print(time.time() - middletime - nowt)\n",
    "while True:\n",
    "    x,_ = mouse.position\n",
    "    #print(mouse.press)\n",
    "    #print(mouse.position)\n",
    "    \n",
    "    mouse.position = (w / 2, h / 2)\n",
    "    time.sleep(1/60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import pyautogui as pag\n",
    "\n",
    "import mouse\n",
    "\n",
    "w = pag.size().width\n",
    "h = pag.size().height\n",
    "\n",
    "nowt = time.time()\n",
    "\n",
    "middletime = time.time() - nowt\n",
    "print(middletime)\n",
    "# print(nowPos-(w/2))\n",
    "\n",
    "print(time.time() - middletime - nowt)\n",
    "while True:\n",
    "    x = mouse.get_position()\n",
    "    print(x)\n",
    "    #print(mouse.position)\n",
    "    \n",
    "    mouse.move(w / 2, h / 2)\n",
    "    time.sleep(1/60)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "deque([[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [1, 1, 1, 1, 1]], maxlen=3)\n",
      "3\n",
      "deque([[0.0, 0.0, 0.0, 0.0, 0.0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2]], maxlen=3)\n",
      "3\n",
      "deque([[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]], maxlen=3)\n",
      "3\n",
      "deque([[2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4]], maxlen=3)\n",
      "3\n",
      "deque([[3, 3, 3, 3, 3], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5]], maxlen=3)\n",
      "3\n",
      "deque([[4, 4, 4, 4, 4], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6]], maxlen=3)\n",
      "3\n",
      "deque([[5, 5, 5, 5, 5], [6, 6, 6, 6, 6], [7, 7, 7, 7, 7]], maxlen=3)\n",
      "3\n",
      "deque([[6, 6, 6, 6, 6], [7, 7, 7, 7, 7], [8, 8, 8, 8, 8]], maxlen=3)\n",
      "3\n",
      "deque([[7, 7, 7, 7, 7], [8, 8, 8, 8, 8], [9, 9, 9, 9, 9]], maxlen=3)\n"
     ]
    }
   ],
   "source": [
    "from collections import deque\n",
    "import numpy as np\n",
    "\n",
    "maxBuffer = 3\n",
    "stateSize = 5\n",
    "\n",
    "aa = deque([[0.0]*stateSize],maxlen=maxBuffer)\n",
    "\n",
    "def ss(s):\n",
    "    aa.append(s)\n",
    "    if len(aa) < maxBuffer:\n",
    "        for i in range(maxBuffer - len(aa)):\n",
    "            aa.appendleft([0.0] * stateSize)\n",
    "\n",
    "for i in range(1,10):\n",
    "    ss([i,i,i,i,i])\n",
    "    print(len(aa))\n",
    "    print(aa)\n",
    "'''\n",
    "3\n",
    "deque([[0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [1, 1, 1, 1, 1]], maxlen=3)\n",
    "3\n",
    "deque([[0.0, 0.0, 0.0, 0.0, 0.0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2]], maxlen=3)\n",
    "3\n",
    "deque([[1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3]], maxlen=3)\n",
    "3\n",
    "deque([[2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4]], maxlen=3)\n",
    "3\n",
    "deque([[3, 3, 3, 3, 3], [4, 4, 4, 4, 4], [5, 5, 5, 5, 5]], maxlen=3)\n",
    "3\n",
    "deque([[4, 4, 4, 4, 4], [5, 5, 5, 5, 5], [6, 6, 6, 6, 6]], maxlen=3)\n",
    "3\n",
    "deque([[5, 5, 5, 5, 5], [6, 6, 6, 6, 6], [7, 7, 7, 7, 7]], maxlen=3)\n",
    "3\n",
    "deque([[6, 6, 6, 6, 6], [7, 7, 7, 7, 7], [8, 8, 8, 8, 8]], maxlen=3)\n",
    "3\n",
    "deque([[7, 7, 7, 7, 7], [8, 8, 8, 8, 8], [9, 9, 9, 9, 9]], maxlen=3)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 0, 1, 2, 0, 1, 2])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import deque\n",
    "import numpy as np\n",
    "\n",
    "aa = np.array([range(0,3)]*5)\n",
    "np.reshape(aa[[0,1,2]],(9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'list'>\n",
      "300\n",
      "256.1\n",
      "[300, 256.1]\n",
      "300\n",
      "256.1\n"
     ]
    }
   ],
   "source": [
    "# 変数を設定\n",
    "ringo_int = 300\n",
    "ringo_float = 256.1\n",
    "ringo_list = [ringo_int, ringo_float]\n",
    "\n",
    "# 型を確認\n",
    "print(type(ringo_int))\n",
    "print(type(ringo_float))\n",
    "print(type(ringo_list))\n",
    "\n",
    "# 値を表示\n",
    "print(ringo_int)\n",
    "print(ringo_float)\n",
    "print(ringo_list)\n",
    "\n",
    "# 配列から要素を取り出す\n",
    "print(ringo_list[0]) # ここでエラーになるという。どぼじで？？？\n",
    "print(ringo_list[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "dirrr = \"GAIL-Expert-Data/1014-1302/pack-24957.npz\"\n",
    "\n",
    "memFile = np.load(dirrr, allow_pickle=True)\n",
    "states = memFile[\"states\"].tolist()\n",
    "actorProbs = memFile[\"actorProbs\"].tolist()\n",
    "actions = memFile[\"actions\"].tolist()\n",
    "rewards = memFile[\"rewards\"].tolist()\n",
    "dones = memFile[\"dones\"].tolist()\n",
    "memNum = len(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\UCUNI\\AppData\\Local\\Temp/ipykernel_39608/3742051961.py:3: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  npact = np.array(actions)\n"
     ]
    }
   ],
   "source": [
    "states = np.reshape(states, (24957, 90))\n",
    "\n",
    "npact = np.array(actions)\n",
    "\n",
    "last = npact[:,3]\n",
    "newlast = []\n",
    "last[2][0]\n",
    "for i in range(len(last)):\n",
    "    newlast.append(last[i][0])\n",
    "\n",
    "#print(newlast)\n",
    "npact[:,3] = newlast\n",
    "\n",
    "statesNP = np.asarray(states)\n",
    "actorProbsNP = np.asarray(actorProbs)\n",
    "actionsNP = np.asarray(npact)\n",
    "rewardsNP = np.asarray(rewards)\n",
    "donesNP = np.asarray(dones)\n",
    "thisSaveDir = \"GAIL-Expert-Data/1014-1302/pack-24957-RE.npz\"\n",
    "\n",
    "np.savez(\n",
    "    thisSaveDir,\n",
    "    states=statesNP,\n",
    "    actorProbs=actorProbsNP,\n",
    "    actions=actionsNP,\n",
    "    rewards=rewardsNP,\n",
    "    dones=donesNP,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(actions)\n",
    "npact = np.array(actions)\n",
    "\n",
    "last = npact[:,3]\n",
    "newlast = []\n",
    "last[2][0]\n",
    "for i in range(len(last)):\n",
    "    newlast.append(last[i][0])\n",
    "\n",
    "#print(newlast)\n",
    "npact[:,3] = newlast\n",
    "print(npact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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