{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy (2): 行列・テンソル\n",
    "\n",
    "このノートブックでは`numpy`モジュールのエイリアス（短縮表記）として`np`を用いる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行列のオブジェクト\n",
    "\n",
    "行列も[np.array](https://numpy.org/doc/stable/reference/generated/numpy.array.html)で作成できる。以下のコードは$3 \\times 4$の行列\n",
    "\n",
    "$$\n",
    "\\pmb{X} = \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 & 4 \\\\\n",
    "2 & 3 & 4 & 5 \\\\\n",
    "3 & 4 & 5 & 6\n",
    "\\end{array}\\right)\n",
    "$$\n",
    "\n",
    "を定義している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [1, 2, 3, 4],\n",
    "    [2, 3, 4, 5],\n",
    "    [3, 4, 5, 6]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [2, 3, 4, 5],\n",
       "       [3, 4, 5, 6]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作成されたオブジェクトの型は[numpy.ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の次元数（軸の数）は2（`x`は2次元の行列）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各次元の要素数を表すタプル。`x`の1次元目には$3$個、2次元目には$4$個の要素が格納されている。すなわち、$3 \\times 4$の行列であることを表している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "オブジェクトに含まれる要素数。`x`は$3 \\times 4$の行列なので、全要素数は$12$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`x`の各要素は整数である。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行列の要素\n",
    "\n",
    "$$\n",
    "\\pmb{X} = \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 & 4 \\\\\n",
    "2 & 3 & 4 & 5 \\\\\n",
    "3 & 4 & 5 & 6\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [1, 2, 3, 4],\n",
    "    [2, 3, 4, 5],\n",
    "    [3, 4, 5, 6]\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列$\\pmb{X}$の$2$行$1$列目の要素$X_{2,1}$（インデックスが$0$から始まることに注意）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[2][1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要素$X_{2,1}$に以下のようにアクセスすることも可能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[2,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列$\\pmb{X}$の1次元目（行）で1番目の行$\\pmb{X}_{1,:}$を取り出す（1行目の行ベクトル）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列$\\pmb{X}$の2次元目（列）で1番目の列$\\pmb{X}_{:,1}$を取り出す（1列目の列ベクトル）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列$\\pmb{X}$の1行目および1列目まで$\\pmb{X}_{0:2,0:2}$を取り出す（つまり、左上から$2 \\times 2$の部分を取り出す）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:2,:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "スライスで取り出す要素をリストで指定してもよい。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[[0,1,2],[0,1,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列$\\pmb{X}$の要素$X_{0,0} \\leftarrow 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2, 3, 4],\n",
       "       [2, 3, 4, 5],\n",
       "       [3, 4, 5, 6]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0][0] = 0\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列$\\pmb{X}$の0行目の行ベクトル$\\pmb{X}_{0,:} \\leftarrow \\boldsymbol{0}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [2, 3, 4, 5],\n",
       "       [3, 4, 5, 6]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0] = 0\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列$\\pmb{X}$の0列の列ベクトル$\\pmb{X}_{:,0} \\leftarrow \\boldsymbol{0}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [0, 3, 4, 5],\n",
       "       [0, 4, 5, 6]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,0] = 0\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\pmb{X}_{0,:} \\leftarrow \\left(\\begin{array}{c}\n",
    "0 & 1 & 2 & 3\n",
    "\\end{array}\\right)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [0, 3, 4, 5],\n",
       "       [0, 4, 5, 6]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0] = np.arange(4)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行列の算術演算\n",
    "\n",
    "$$\n",
    "\\pmb{X} = \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right),\n",
    "\\pmb{Y} = \\left(\\begin{array}{c}\n",
    "1 & 3 & 5 \\\\\n",
    "2 & 4 & 6 \\\\\n",
    "\\end{array}\\right)\n",
    "$$\n",
    "\n",
    "に対して、様々な演算を見ていく。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [1, 2, 3],\n",
    "    [2, 3, 4],\n",
    "], dtype='float')\n",
    "y = np.array([\n",
    "    [1, 3, 5],\n",
    "    [2, 4, 6],\n",
    "], dtype='float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の和\n",
    "\n",
    "$$\n",
    "\\pmb{X} + \\pmb{Y} = \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right) + \\left(\\begin{array}{c}\n",
    "1 & 3 & 5 \\\\\n",
    "2 & 4 & 6 \\\\\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "1+1 & 2+3 & 3+5 \\\\\n",
    "2+2 & 3+4 & 4+6 \\\\\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "2 & 5 & 8 \\\\\n",
    "4 & 7 & 10 \\\\\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.,  5.,  8.],\n",
       "       [ 4.,  7., 10.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の差\n",
    "\n",
    "$$\n",
    "\\pmb{X} - \\pmb{Y} = \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right) - \\left(\\begin{array}{c}\n",
    "1 & 3 & 5 \\\\\n",
    "2 & 4 & 6 \\\\\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "1-1 & 2-3 & 3-5 \\\\\n",
    "2-2 & 3-4 & 4-6 \\\\\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "0 & -1 & -2 \\\\\n",
    "0 & -1 & -2 \\\\\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0., -1., -2.],\n",
       "       [ 0., -1., -2.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x - y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列と行列のアダマール積\n",
    "\n",
    "$$\n",
    "\\pmb{X} \\odot \\pmb{Y} = \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right) \\odot \\left(\\begin{array}{c}\n",
    "1 & 3 & 5 \\\\\n",
    "2 & 4 & 6 \\\\\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "1 \\times 1 & 2 \\times 3 & 3 \\times 5 \\\\\n",
    "2 \\times 2 & 3 \\times 4 & 4 \\times 6 \\\\\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "1 & 6 & 15 \\\\\n",
    "4 & 12 & 24 \\\\\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  6., 15.],\n",
       "       [ 4., 12., 24.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "スカラーと行列の和\n",
    "\n",
    "$$\n",
    "1 + \\pmb{X} = \\left(\\begin{array}{c}\n",
    "1 & 1 & 1 \\\\\n",
    "1 & 1 & 1\n",
    "\\end{array}\\right) + \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "2 & 3 & 4 \\\\\n",
    "3 & 4 & 5 \\\\\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 3., 4.],\n",
       "       [3., 4., 5.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "スカラーと行列の差\n",
    "\n",
    "$$\n",
    "1 - \\pmb{X} = \\left(\\begin{array}{c}\n",
    "1 & 1 & 1 \\\\\n",
    "1 & 1 & 1\n",
    "\\end{array}\\right) - \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "0 & -1 & -2 \\\\\n",
    "-1 & -2 & -3 \\\\\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0., -1., -2.],\n",
       "       [-1., -2., -3.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列のスカラー倍\n",
    "\n",
    "$$\n",
    "2\\pmb{X} = \\left(\\begin{array}{c}\n",
    "2 & 2 & 2 \\\\\n",
    "2 & 2 & 2\n",
    "\\end{array}\\right) \\odot \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "2 \\times 1 & 2 \\times 2 & 2 \\times 3 \\\\\n",
    "2 \\times 2 & 2 \\times 3 & 2 \\times 4\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "2 & 4 & 6 \\\\\n",
    "4 & 6 & 8 \\\\\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 4., 6.],\n",
       "       [4., 6., 8.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 * x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "転置行列\n",
    "\n",
    "$$\n",
    "Z = Y^\\top = \\left(\\begin{array}{c}\n",
    "1 & 3 & 5 \\\\\n",
    "2 & 4 & 6 \\\\\n",
    "\\end{array}\\right)^\\top\n",
    "= \\left(\\begin{array}{c}\n",
    "1 & 2 \\\\\n",
    "3 & 4 \\\\\n",
    "5 & 6\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2.],\n",
       "       [3., 4.],\n",
       "       [5., 6.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = y.T\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列積\n",
    "\n",
    "\\begin{align*}\n",
    "XZ &= \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right) \\left(\\begin{array}{c}\n",
    "1 & 2 \\\\\n",
    "3 & 4 \\\\\n",
    "5 & 6\n",
    "\\end{array}\\right) \\\\\n",
    "&= \\left(\\begin{array}{c}\n",
    "1 \\times 1 + 2 \\times 3 + 3 \\times 5 & 1 \\times 2 + 2 \\times 4 + 3 \\times 6 \\\\\n",
    "2 \\times 1 + 3 \\times 3 + 4 \\times 5 & 2 \\times 2 + 3 \\times 4 + 4 \\times 6 \\\\\n",
    "\\end{array}\\right) \\\\\n",
    "&= \\left(\\begin{array}{c}\n",
    "22 & 28 \\\\\n",
    "31 & 40 \\\\\n",
    "\\end{array}\\right)\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22., 28.],\n",
       "       [31., 40.]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x, z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列積$XZ$は`@`演算子で書くこともできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22., 28.],\n",
       "       [31., 40.]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x @ z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の累乗\n",
    "\n",
    "$$\n",
    "X^2 = \\left(\\begin{array}{c}\n",
    "1 & 2 & 3 \\\\\n",
    "2 & 3 & 4\n",
    "\\end{array}\\right)^2 = \\left(\\begin{array}{c}\n",
    "1^2 & 2^2 & 3^2 \\\\\n",
    "2^2 & 3^2 & 4^2\n",
    "\\end{array}\\right) = \\left(\\begin{array}{c}\n",
    "1 & 4 & 9 \\\\\n",
    "4 & 9 & 16\n",
    "\\end{array}\\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  4.,  9.],\n",
       "       [ 4.,  9., 16.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素数が合わないなどで、演算が実行できないときはエラーとなる。例えば行列積$XY$を計算するには、$X$の列の数と$Y$の行の数が一致する必要がある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-e6c6f87ceb97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m@\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)"
     ]
    }
   ],
   "source": [
    "x @ y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行列の様々な作成方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "零行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$X$と同じ形状の零行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [0, 1],\n",
    "    [1, 2],\n",
    "    [2, 3],\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 0],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros_like(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要素がすべて$1$の行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$X$と同じ形状で要素がすべて$1$の行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [1, 1],\n",
       "       [1, 1]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones_like(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "任意の値で初期化した行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2., -2., -2., -2.],\n",
       "       [-2., -2., -2., -2.],\n",
       "       [-2., -2., -2., -2.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3, 4), -2.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$X$と同じ形状で任意の値で初期化した行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2, -2],\n",
       "       [-2, -2],\n",
       "       [-2, -2]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full_like(x, -2.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "単位行列（必ず正方行列となる）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "対角要素が$1$、それ以外の要素が$0$である行列（正方行列でなくても作成できる）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[np.eye](https://numpy.org/doc/stable/reference/generated/numpy.eye.html)でも単位行列を作成できる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ベクトルから形状を変更して行列にする例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(12).reshape(3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[reshape](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.reshape.html#numpy.ndarray.reshape)メソッドにおいて、いずれかの次元の要素数を-1とすると、他の次元の要素数に基づいてその次元の要素数が推定される。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(12).reshape(2, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$[0, 1)$の範囲の一様分布からランダムに要素をサンプルして作った行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.41138273, 0.56472476, 0.74019377, 0.21415386],\n",
       "       [0.72671417, 0.00608295, 0.73117914, 0.97736548],\n",
       "       [0.27531559, 0.69205105, 0.1033887 , 0.94401989]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "標準正規分布（平均$0$、分散$1$の正規分布）からランダムに要素を抽出して作った行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.10454056, -0.14676112, -0.12848028,  0.62593397],\n",
       "       [-0.82940938, -0.45294596, -0.3850291 , -1.75512478],\n",
       "       [ 0.28350437,  0.54926243, -0.66035861, -0.50153151]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正規分布（以下の例では平均$0.5$、分散$2$の正規分布）からランダムに要素を抽出して作った行列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.05489919,  0.4101351 ,  2.20940252, -1.12757369],\n",
       "       [ 0.18863236,  0.20804204, -0.58426747,  1.76459683],\n",
       "       [-2.4958973 ,  4.89609212, -3.17816385,  1.27884234]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(0.5, 2, (3, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数学関数\n",
    "\n",
    "ベクトルのときに紹介した数学関数は行列でも使える。ただし、必要に応じて演算を行う単位を軸（axis）として指定する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [  1, 12,  9,  4],\n",
    "    [  8,  2,  3,  7],\n",
    "    [ 11,  5,  6, 10],\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の和。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）の和。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 19, 18, 21])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）の和。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26, 20, 32])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の積。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "479001600"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.prod(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）の積。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 88, 120, 162, 280])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.prod(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）の積。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 432,  336, 3300])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.prod(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の累積和。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 22, 26, 34, 36, 39, 46, 57, 62, 68, 78])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）の累積和。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, 12,  9,  4],\n",
       "       [ 9, 14, 12, 11],\n",
       "       [20, 19, 18, 21]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）の累積和。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1, 13, 22, 26],\n",
       "       [ 8, 10, 13, 20],\n",
       "       [11, 16, 22, 32]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の累積積。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([        1,        12,       108,       432,      3456,      6912,\n",
       "           20736,    145152,   1596672,   7983360,  47900160, 479001600])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumprod(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）の累積積。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,  12,   9,   4],\n",
       "       [  8,  24,  27,  28],\n",
       "       [ 88, 120, 162, 280]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumprod(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）の累積積。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   1,   12,  108,  432],\n",
       "       [   8,   16,   48,  336],\n",
       "       [  11,   55,  330, 3300]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumprod(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の平均。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.5"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）の平均。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.66666667, 6.33333333, 6.        , 7.        ])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）の平均。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.5, 5. , 8. ])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の分散。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.916666666666666"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）の分散。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17.55555556, 17.55555556,  6.        ,  6.        ])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）の分散。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.25,  6.5 ,  6.5 ])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の標準偏差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.452052529534663"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）の標準偏差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.18993503, 4.18993503, 2.44948974, 2.44948974])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）の標準偏差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.27200187, 2.54950976, 2.54950976])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の最小値。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）を取り出し、各要素の最小値を取り出したベクトル。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）を取り出し、各要素の最小値を取り出したベクトル。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 5])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の要素の最大値。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最初の次元のベクトル（行ベクトル）を取り出し、各要素の最大値を取り出したベクトル。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12,  9, 10])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の2番目の次元のベクトル（列ベクトル）を取り出し、各要素の最大値を取り出したベクトル。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  8, 11])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最小値に対応するインデックス番号。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 0)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unravel_index(x.argmin(), x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の最大値に対応するインデックス番号。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unravel_index(x.argmax(), x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ユニバーサル関数\n",
    "\n",
    "ベクトルの場合と同様に、行列に対して[ユニバーサル関数](https://numpy.org/doc/stable/reference/ufuncs.html)を適用すると、要素ごとに数学的な計算を行う。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [ 0, 1],\n",
    "    [-1, 2],\n",
    "])\n",
    "theta = np.array([\n",
    "    [  0,  90],\n",
    "    [-90, 180],\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "累乗（`**`と同じ）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 1, 16]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.power(x, 4)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "平方根。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [1., 4.]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$e$に対する指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 2.71828183],\n",
       "       [0.36787944, 7.3890561 ]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.exp(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自然対数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.],\n",
       "       [-1.,  2.]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三角関数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.0000000e+00,  1.0000000e+00],\n",
       "       [-1.0000000e+00,  1.2246468e-16]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(theta / 360 * 2 * np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.000000e+00,  6.123234e-17],\n",
       "       [ 6.123234e-17, -1.000000e+00]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(theta / 360 * 2 * np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  1.63312394e+16],\n",
       "       [-1.63312394e+16, -1.22464680e-16]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tan(theta / 360 * 2 * np.pi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "その他、要素の切り捨て、切り上げ、四捨五入などもベクトルの場合と同様なので省略する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行列の連結"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [2, 3],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([\n",
    "    [1, 2],\n",
    "    [2, 3],\n",
    "    [3, 4],\n",
    "])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [1.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.ones((3, 1))\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.array([0, 1])\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列・ベクトルを横に（水平に）連結。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 1.],\n",
       "       [2., 3., 1.],\n",
       "       [3., 4., 1.]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列・ベクトルを縦に（垂直に）連結。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [1, 2],\n",
       "       [2, 3],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((z, x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ビュー"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [1, 2, 3, 4],\n",
    "    [2, 3, 4, 5],\n",
    "    [3, 4, 5, 6]\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "元の行列・ベクトル（np.ndarray）からスライスで抽出した部分はビュー、すなわち元のオブジェクトへの参照となり、元のオブジェクトとメモリ領域を共有している。以下の例では行列`x`から行ベクトル`y`をビューとして抽出している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x[2]\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "抽出されたビュー`y`の要素を変更してみる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 5, 6])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0] = 0\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "元の行列`x`の該当する箇所`x[2,0]`も変更されている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [2, 3, 4, 5],\n",
       "       [0, 4, 5, 6]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ビュー`y`の全ての要素を変更する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:] = 0\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "元の行列`x`の行ベクトル`x[2]`も変更されている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [2, 3, 4, 5],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "なお、ビュー`y`の全体を変更しようとして以下のコードを実行すると、`y`の要素が変更されるのではなく、`y`に`1`が代入されるだけなので混同しないこと。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = 1\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当然、元の行列`x`の要素にも変化がない。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [2, 3, 4, 5],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`x`の部分行列をスライスで取り出した場合も挙動は同じ。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x[:2,:2]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:] = 0\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`x`全体のビューは[view](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.view.html)メソッドで作成することもできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 3, 4],\n",
       "       [0, 0, 4, 5],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1],\n",
       "       [1, 1, 1, 1],\n",
       "       [1, 1, 1, 1]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:] = 1\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1],\n",
       "       [1, 1, 1, 1],\n",
       "       [1, 1, 1, 1]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## テンソル"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "スカラー（次元数$0$）やベクトル（次元数$1$）、行列（次元数$2$）を一般化したものをテンソルと呼ぶ。以下のコードは3次元のテンソル$\\mathsf{X}$を定義している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([\n",
    "    [\n",
    "        [1, 2, 3],\n",
    "        [2, 3, 4]\n",
    "    ],\n",
    "    [\n",
    "        [3, 4, 5],\n",
    "        [5, 6, 7]\n",
    "    ],\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列の次元数（軸の数）は3（$\\mathsf{X}$は3次元のテンソル）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "各次元の要素数を表すタプル。$\\mathsf{X}$の1次元目には$2$個、2次元目には$2$個、3次元目には$3$個の要素が格納されている。すなわち、$2 \\times 2 \\times 3$の行列であることを表している。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 3)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\mathsf{X}$の1次元目で1番目の要素（＝行列）を取り出したスライス$\\mathsf{X}_{1,:,:}$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "これは以下のように書くこともできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1,...]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\mathsf{X}$の3次元目で1番目の要素を取り出したスライス$\\mathsf{X}_{:,:,1}$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [4, 6]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "これは以下のように書くこともできる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [4, 6]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[...,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "行列からテンソルへの変換。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([\n",
    "    [1, 2],\n",
    "    [3, 4],\n",
    "])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2],\n",
       "        [3, 4]]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.expand_dims(x, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2]],\n",
       "\n",
       "       [[3, 4]]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.expand_dims(x, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "---\n",
    "\n",
    "[Python早見帳](https://chokkan.github.io/python/) © Copyright 2020-2022 by [岡崎 直観 (Naoaki Okazaki)](https://www.chokkan.org/). この作品は<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\">クリエイティブ・コモンズ 表示 - 非営利 - 改変禁止 4.0 国際 ライセンス</a>の下に提供されています。<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-nd/4.0/\"><img alt=\"クリエイティブ・コモンズ・ライセンス\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-nd/4.0/80x15.png\" /></a>"
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