From ed12464230f9542aeb72509f9dc053a63de9624c Mon Sep 17 00:00:00 2001 From: Taro Yabuki Date: Wed, 13 Mar 2024 13:52:49 +0900 Subject: [PATCH] P --- README.md | 2 +- code/python/python.Rmd | 24 ++---------------------- 2 files changed, 3 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index f15cac1..35db5b5 100644 --- a/README.md +++ b/README.md @@ -61,7 +61,7 @@ Wolfram|Alpha，Python，R，Mathematicaをフル活用して，大学教養レ 環境（言語）|サンプルコード|備考 --|--|-- [Wolfram\|Alpha](https://www.wolframalpha.com/)|[クエリと結果](code/wolframalpha)|無料 -Google Colab (Python)|[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/taroyabuki/comath/blob/master/code/python.ipynb)|無料（要登録） +Google Colab (Python)|[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/taroyabuki/comath/blob/master/code/python/python.ipynb)|無料（要登録） Google Colab (R)|[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/taroyabuki/comath/blob/master/code/r/r.ipynb)|無料（要登録） [Wolfram Cloud](https://www.wolframcloud.com)|動的生成[^1]|無料（要登録） Mathematica|[mathematica.nb](code/mathematica/mathematica.nb) あるいは動的生成[^2]|[ラズベリーパイ版](https://www.wolfram.com/raspberry-pi/)は無料 diff --git a/code/python/python.Rmd b/code/python/python.Rmd index 07d1edc..41fbe65 100644 --- a/code/python/python.Rmd +++ b/code/python/python.Rmd @@ -10,7 +10,7 @@ output: [矢吹太朗『コンピュータでとく数学』（オーム社,\ 2024）](https://github.com/taroyabuki/comath) ```{r setup, include=FALSE} -system("pip install matplotlib pandas seaborn see scikit-learn statsmodels sympy") +system("pip install matplotlib IPython pandas seaborn see scikit-learn statsmodels sympy") ``` ```{python} @@ -19,9 +19,8 @@ from IPython.display import display # 1 実行環境 - ```{python} -!python -m pip install see +#!python -m pip install see ``` ```{python} @@ -66,7 +65,6 @@ see(model) # 2 数と変数 - ```{python} 2 * (-3) ``` @@ -269,7 +267,6 @@ refine(sqrt((x - 1)**2), Q.nonnegative(x - 1)) # 3 データ構造 - ```{python} v = [2, 3, 5]; len(v) ``` @@ -404,7 +401,6 @@ english, math = m.T; english, math # 4 可視化と方程式 - ```{python} var('x') plot(x**2 + 2 * x - 4, (x, -5, 3)); @@ -483,10 +479,8 @@ solve(Lt(x**2 + 2 * x - 4, 0), x) # 5 論理式 - # 6 1次元のデータ - ```{python} a = pd.Series([36, 43, 53, 55, 56, 56, 57, 60, 61, 73]) b = pd.Series([34, 39, 39, 49, 50, 52, 52, 55, 83, 97]) @@ -550,7 +544,6 @@ a.std(ddof=1) * z + a.mean() # 7 2次元のデータ - ```{python} x = pd.Series([35, 45, 55, 65, 75]) y = pd.Series([114, 124, 143, 158, 166]) @@ -618,7 +611,6 @@ sns.regplot(x=data.x, y=data.y, ci=None); # 8 確率変数と確率分布 - ```{python} X = DiscreteUniform('X', range(1, 7)) # X = Die('X', 6)でもよい． density(X)(2) @@ -999,7 +991,6 @@ quad(lambda x: (x - rv.mean())**2 * rv.pdf(x), -1, 1) # 9 多次元の確率分布 - ```{python} X1 = DiscreteUniform('X1', range(1, 7)) X2 = DiscreteUniform('X2', range(1, 7)) @@ -1120,7 +1111,6 @@ plt.contourf(Y1, Y2, z, cmap='viridis'); plt.xlabel('Y1'); plt.ylabel('Y2'); # 10 推測統計 - ```{python} mu = 2; sigma = 3; rv = stats.norm(mu, sigma) data1 = [rv.rvs(5).mean() for _ in range(10000)] @@ -1235,7 +1225,6 @@ stats.f.ppf((alpha / 2, 1 - alpha / 2), m - 1, n - 1) # 11 線形回帰分析 - ```{python} data = pd.DataFrame({ 'x1': [1, 1, 2, 3], 'x2': [2, 3, 5, 7], 'y': [3, 6, 3, 6]}) @@ -1364,7 +1353,6 @@ sns.regplot(x=data.x1, y=data.y, ci=95); # 12 関数の極限と連続性 - ```{python} f = lambda x: 2 * x - 3; var('x') limit(f(x), x, 1) @@ -1393,7 +1381,6 @@ limit(abs(x) / x, x, 0, dir='+'), limit(abs(x) / x, x, 0, dir='-') # 13 \kintou{3zw - ```{python} f = lambda x: x**3; var('h'); a = 1 limit((f(a + h) - f(a)) / h, h, 0) @@ -1452,7 +1439,6 @@ series(f(x), x0=sol[0], n=3) # 14 \kintou{3zw - ```{python} var('x') integrate(-x**2 + 4 * x + 1, (x, 1, 4)) @@ -1524,7 +1510,6 @@ var('x'); integrate(exp(-x**2), (x, -oo, oo)) # 15 多変数関数の微分積分 - ```{python} f = lambda x, y: x**2 * y / (x**4 + y**2); var('x y r theta') (limit(limit(f(x, y), x, 0), y, 0), # ① @@ -1601,7 +1586,6 @@ simplify(J.det()) # 16 ベクトル - ```{python} a = Matrix([2, 3, 5]); len(a) ``` @@ -1673,7 +1657,6 @@ np.arccos(a @ b / (linalg.norm(a) * linalg.norm(b))) # 17 \kintou{3zw - ```{python} A = Matrix([[1, 2, 0], [0, 3, 4]]); A ``` @@ -1837,7 +1820,6 @@ np.linalg.matrix_rank(A) # 18 ベクトル空間 - ```{python} a1 = Matrix([3, 1]); a2 = Matrix([2, 2]); var('c1 c2') solve(c1 * a1 + c2 * a2, (c1, c2)) @@ -1948,7 +1930,6 @@ Q, np.allclose(Q.T @ Q, np.eye(3)) # 19 固有値と固有ベクトル - ```{python} A = Matrix([[5, 6, 3], [0, 9, 2], [0, 6, 8]]) eigs = A.eigenvects() @@ -2104,7 +2085,6 @@ print(np.cumsum(s2) / sum(s2)) # 累積寄与率（後述） # 20 特異値分解と擬似逆行列 - ```{python} A = Matrix([[1, 0], [1, 1], [0, 1]]) Ur, Sr, Vr = A.singular_value_decomposition()