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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 準備\n",
"* 個人PCの持ち込みを必須とします. \n",
"* Numpy, Scipy, Matplotlib, Jupyterノートブックはインストール済みであるとします. \n",
"* 個人的にはAnacondaがオススメです. \n",
"* 数式を確認するため, 紙と鉛筆があればベターです(必須ではありません). \n",
"* Shift + Enterでセル内のコードが実行されます. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 初期設定"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 図をインラインで表示するように指定します. \n",
"%matplotlib inline \n",
"\n",
"import numpy as np # Numpyモジュールをインポートします. as以降でnpという別名を与えます.\n",
"import matplotlib.pyplot as plt # Matplotlibモジュールをインポートします. pltという別名を与えます. \n",
"import seaborn as sns # 図を綺麗に表示するモジュールです. このモジュールは省略できます. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpyの基礎"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1次元配列 (ベクトル)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 3 4]\n",
"int64\n",
"<class 'numpy.ndarray'>\n",
"(4,)\n",
"1\n",
"['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_interface__', '__array_prepare__', '__array_priority__', '__array_struct__', '__array_wrap__', '__bool__', '__class__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tobytes', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']\n"
]
}
],
"source": [
"a = np.array([1, 2, 3, 4])\n",
"print(a) # 中身を表示します. \n",
"print(a.dtype) # 中に入っている数値型\n",
"print(type(a)) # 配列自身の型\n",
"print(a.shape) # 配列の形 (タプルなので次元が1の場合は最後にカンマが付きます)\n",
"print(a.ndim) # 配列の次元\n",
"print(dir(a)) # 1次元配列オブジェクトが持つ属性(メソッドも含まれます)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 数値型の変換"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 2. 3. 4.]\n",
"float32\n",
"[1 2 3 4]\n",
"[ 100. 2. 3. 4.]\n"
]
}
],
"source": [
"a = np.array([1, 2, 3, 4])\n",
"b = a.astype('float32')\n",
"print(b)\n",
"print(b.dtype)\n",
"\n",
"b[0] = 100\n",
"print(a)\n",
"print(b) # bはaのディープコピーです. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 転置"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 2 3 4]\n",
"(4,)\n",
"[100 2 3 4]\n",
"[100 2 3 4]\n"
]
}
],
"source": [
"# 1次元配列に対する転置は何もしません. \n",
"a = np.array([1, 2, 3, 4])\n",
"b = a.T\n",
"print(b)\n",
"print(b.shape)\n",
"\n",
"b[0] = 100\n",
"print(a) # bはaのビュー(シャローコピー)です. \n",
"print(b) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 統計量"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n",
"2.5\n",
"1.11803398875\n",
"1\n",
"4\n"
]
}
],
"source": [
"a = np.array([1, 2, 3, 4])\n",
"print(a.sum()) # 和\n",
"print(a.mean()) # 平均\n",
"print(a.std()) # 標準偏差\n",
"print(a.min()) # 最小値\n",
"print(a.max()) # 最大値"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### インデックスの取得"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"3\n"
]
}
],
"source": [
"a = np.array([1, 2, 3, 4])\n",
"print(a.argmin()) # 最小値のインデックス\n",
"print(a.argmax()) # 最大値のインデックス\n",
"assert(a[a.argmin()] == a.min())\n",
"assert(a[a.argmax()] == a.max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 加減乗除"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 6 8 10]\n",
"[-4 -4 -4]\n",
"[ 5 12 21]\n",
"[ 0.2 0.33333333 0.42857143]\n"
]
}
],
"source": [
"# 加減乗除はベクトルの成分ごとに適用されます. \n",
"a = np.array([1, 2, 3])\n",
"b = np.array([5, 6, 7])\n",
"print(a + b)\n",
"print(a - b)\n",
"print(a * b)\n",
"print(a / b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 内積"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"38\n",
"38\n"
]
}
],
"source": [
"a = np.array([1, 2, 3])\n",
"b = np.array([5, 6, 7])\n",
"print(a.dot(b))\n",
"print(a[0] * b[0] + a[1] * b[1] + a[2] * b[2]) # 内積の定義と一致することを確認します. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 配列生成用関数"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3 4 5 6 7 8 9]\n",
"[ 5 7 9 11 13]\n",
"[ 0. 0. 0.]\n",
"[ 1. 1. 1.]\n"
]
}
],
"source": [
"print(np.arange(10)) # 10が含まれないことに注意します. \n",
"print(np.arange(5, 15, 2))\n",
"print(np.zeros(3))\n",
"print(np.ones(3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### スライス"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2 3]\n",
"[4 5 6 7 8 9]\n",
"[6 7 8 9]\n",
"[0 2 4 6 8]\n"
]
}
],
"source": [
"a = np.arange(10)\n",
"print(a[:4]) # 最初の4成分\n",
"print(a[4:]) # 最初の4成分以降\n",
"print(a[-4:]) # 最後の4成分\n",
"print(a[::2]) # 2つずつ飛ばす. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2次元配列 (行列)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1 2 3 4]\n",
" [ 5 6 7 8]\n",
" [ 9 10 11 12]]\n",
"(3, 4)\n",
"2\n"
]
}
],
"source": [
"a = np.array([[1, 2, 3, 4], \n",
" [5, 6, 7, 8], \n",
" [9, 10, 11, 12]]) # リストのリストを引数に与えます. \n",
"print(a)\n",
"print(a.shape) # 1次元の場合と同じくタプルです. \n",
"print(a.ndim)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1次元配列と2次元配列の間の変換"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1 2 3 4 5 6 7 8 9 10 11 12]\n",
"[[ 1 2 3 4]\n",
" [ 5 6 7 8]\n",
" [ 9 10 11 12]]\n",
"[ 1 2 3 4 5 6 7 8 9 10 11 12]\n"
]
}
],
"source": [
"a = np.arange(1, 13) # 1から12までの1次元配列\n",
"b = a.reshape(3, 4) # 1D -> 2D\n",
"c = b.ravel() # 2D -> 1D. 3次元以上の配列も1次元に変換されます. \n",
"print(a)\n",
"print(b)\n",
"print(c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 行列とベクトルの積\n",
"\\begin{eqnarray}\n",
"A = \\left[\n",
" \\begin{array}{ccc}\n",
" 1 & 2 \\\\\n",
" 3 & 4\n",
" \\end{array}\n",
"\\right], \n",
"b = [5, 6]\n",
"\\end{eqnarray}\n",
"\n",
"ndarrayでは行ベクトルと列ベクトルは同じ形を取ります. $bA$を計算する場合bは行ベクトルとして解釈され, $Ab$を計算する場合は列ベクトルとして解釈されます. "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1]\n",
" [2 3]]\n",
"[5 6]\n",
"[ 6 28]\n",
"[12 23]\n"
]
}
],
"source": [
"a = np.arange(4).reshape(2, 2)\n",
"b = np.array([5, 6])\n",
"c = a.dot(b) # 一般にAB!=BAです. \n",
"d = b.dot(a) # 上の行の結果と一致しません.\n",
"print(a)\n",
"print(b)\n",
"print(c)\n",
"print(d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 単位行列"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 5. 6.]\n",
"[ 5. 6.]\n"
]
}
],
"source": [
"a = np.eye(2)\n",
"b = np.array([5, 6])\n",
"print(a.dot(b)) # 単位行列を掛けても値は変化しません. \n",
"print(b.dot(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 行列同士の積"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0 1]\n",
" [2 3]]\n",
"[[0 1 2 3]\n",
" [4 5 6 7]]\n",
"[[ 4 5 6 7]\n",
" [12 17 22 27]]\n"
]
},
{
"ename": "ValueError",
"evalue": "shapes (2,4) and (2,2) not aligned: 4 (dim 1) != 2 (dim 0)",
"output_type": "error",
"traceback": [
"\u001b[1;31m\u001b[0m",
"\u001b[1;31mValueError\u001b[0mTraceback (most recent call last)",
"\u001b[1;32m<ipython-input-47-5ab5bfb7c709>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m# エラー\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 左の行列(b)の列数と右の行列(a)の行数が一致しません.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mValueError\u001b[0m: shapes (2,4) and (2,2) not aligned: 4 (dim 1) != 2 (dim 0)"
]
}
],
"source": [
"a = np.arange(4).reshape(2, 2)\n",
"b = np.arange(8).reshape(2, 4)\n",
"c = a.dot(b)\n",
"print(a)\n",
"print(b)\n",
"print(c)\n",
"\n",
"# エラー\n",
"d = b.dot(a) # 左の行列(b)の列数と右の行列(a)の行数が一致しません. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 逆行列"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1. 1. 2.]\n",
" [ 3. 5. 5.]\n",
" [ 6. 7. 9.]]\n",
"[[-2. -1. 1. ]\n",
" [-0.6 0.6 -0.2]\n",
" [ 1.8 0.2 -0.4]]\n",
"[[ 1.00000000e+00 -2.22044605e-16 0.00000000e+00]\n",
" [ 0.00000000e+00 1.00000000e+00 0.00000000e+00]\n",
" [ 0.00000000e+00 -4.44089210e-16 1.00000000e+00]]\n",
"[[ 1.00000000e+00 0.00000000e+00 0.00000000e+00]\n",
" [ 0.00000000e+00 1.00000000e+00 0.00000000e+00]\n",
" [ 0.00000000e+00 -4.44089210e-16 1.00000000e+00]]\n"
]
}
],
"source": [
"a = np.arange(9).reshape(3, 3) + np.eye(3)\n",
"b = np.linalg.inv(a)\n",
"c = a.dot(b)\n",
"d = b.dot(a)\n",
"print(a)\n",
"print(b)\n",
"print(c)\n",
"print(d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## プロットの基礎"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 散布図とグラフ"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ATB/XV3n71SmuWXYVh65c6+njIgRHYhtWQdiRZQ2tJG1HZuajosNxCteeU/aU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\nIH8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f15b781c5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(-4, 4, 0.1)\n",
"y = x**2 # y=x^2\n",
"\n",
"# Figureオブジェクトを準備します. \n",
"fig = plt.figure(figsize=(8, 4)) # 横幅10, 縦幅5\n",
"\n",
"# 散布図\n",
"ax1 = fig.add_subplot(121) # Figureオブジェクトの中にAxesオブジェクトを準備します. \n",
"ax1.scatter(x, y, marker='.', s=10) # sはマーカーのサイズ\n",
"\n",
"# グラフ\n",
"ax2 = fig.add_subplot(122) # Axesの数が1行2列, 末尾の数字はその2番目を意味します. \n",
"ax2.plot(x, y);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 等高線図\n",
"メッシュを生成し, その座標値で関数を計算してプロットします. "
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(160,)\n",
"(160,)\n",
"(160, 160)\n",
"(160, 160)\n",
"[[-4. -3.95 -3.9 -3.85 -3.8 ]\n",
" [-4. -3.95 -3.9 -3.85 -3.8 ]\n",
" [-4. -3.95 -3.9 -3.85 -3.8 ]\n",
" [-4. -3.95 -3.9 -3.85 -3.8 ]\n",
" [-4. -3.95 -3.9 -3.85 -3.8 ]]\n",
"[[-4. -4. -4. -4. -4. ]\n",
" [-3.95 -3.95 -3.95 -3.95 -3.95]\n",
" [-3.9 -3.9 -3.9 -3.9 -3.9 ]\n",
" [-3.85 -3.85 -3.85 -3.85 -3.85]\n",
" [-3.8 -3.8 -3.8 -3.8 -3.8 ]]\n"
]
}
],
"source": [
"# メッシュの作成\n",
"x_ = np.arange(-4, 4, 0.05)\n",
"y_ = np.arange(-4, 4, 0.05)\n",
"x, y = np.meshgrid(x_, y_)\n",
"print(x_.shape)\n",
"print(y_.shape)\n",
"print(x.shape)\n",
"print(y.shape)\n",
"print(x[:5, :5])\n",
"print(y[:5, :5])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(160, 160)\n",
"4.03896783473e-28 32.0\n"
]
}
],
"source": [
"# 表示する関数の値の計算\n",
"z = x**2 + y**2\n",
"print(z.shape)\n",
"print(z.min(), z.max())"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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UEDqRQx8gtVsBc0YsSnDKqTjmfkrq0GMI3/s0OJprsX655OI9rDUTRs2FVbkxMoSGsrTW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9bApNVGmgaFdo1JGXrXkKb5oGkvSJlnjpMF0BpjB8ImPrWIhMjF119/jZEjR6JTp07YsWNHQhVp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\nYooLExMTExMTk6zy/3nWLZahC0bUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f15aefc1908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(9, 4))\n",
"\n",
"# 等高線図\n",
"plt.jet() # jetカラーマップ\n",
"ax1 = fig.add_subplot(121)\n",
"ax1.contour(x, y, z, 10) # 最後の数値は等高線の数です. \n",
"\n",
"plt.hot() # hotカラーマップ\n",
"ax2 = fig.add_subplot(122)\n",
"h = ax2.pcolor(x, y, z)\n",
"plt.colorbar(h);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
},
"widgets": {
"state": {},
"version": "1.1.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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