From 7fb7305858c8eb29f12d8bf7cabcb81ff6763d79 Mon Sep 17 00:00:00 2001 From: jason_cv Date: Mon, 4 May 2020 17:49:51 -0500 Subject: [PATCH] =?UTF-8?q?C=C3=B3digo=20de=20regresi=C3=B3n=20logistica?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Logistica.py | 78 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 Logistica.py diff --git a/Logistica.py b/Logistica.py new file mode 100644 index 0000000..bf9cb86 --- /dev/null +++ b/Logistica.py @@ -0,0 +1,78 @@ +from sklearn import datasets +from sklearn.linear_model import LogisticRegression +import numpy as np +import matplotlib.pyplot as plt + +iris = datasets.load_iris() +list(iris.keys()) +print(iris.DESCR) +X = iris["data"][:, 3:] # petal width +y = (iris["target"] == 2).astype(np.int) # 1 if Iris-Virginica, else 0 + + + +log_reg = LogisticRegression(solver="liblinear", random_state=42) +log_reg.fit(X, y) + + + + +X_new = np.linspace(0, 3, 1000).reshape(-1, 1) +y_proba = log_reg.predict_proba(X_new) +decision_boundary = X_new[y_proba[:, 1] >= 0.5][0] + +plt.figure(figsize=(8, 3)) +plt.plot(X[y==0], y[y==0], "bs") +plt.plot(X[y==1], y[y==1], "g^") +plt.plot([decision_boundary, decision_boundary], [-1, 2], "k:", linewidth=2) +plt.plot(X_new, y_proba[:, 1], "g-", linewidth=2, label="Iris-Virginica") +plt.plot(X_new, y_proba[:, 0], "b--", linewidth=2, label="Not Iris-Virginica") +plt.text(decision_boundary+0.02, 0.15, "Decision boundary", fontsize=14, color="k", ha="center") +plt.arrow(decision_boundary, 0.08, -0.3, 0, head_width=0.05, head_length=0.1, fc='b', ec='b') +plt.arrow(decision_boundary, 0.92, 0.3, 0, head_width=0.05, head_length=0.1, fc='g', ec='g') +plt.xlabel("Ancho de petalo (cm)", fontsize=14) +plt.ylabel("Probabilidad", fontsize=14) +plt.legend(loc="center left", fontsize=14) +plt.axis([0, 3, -0.02, 1.02]) + + + +X = iris["data"][:, (2, 3)] # petal length, petal width +y = iris["target"] + +softmax_reg = LogisticRegression(multi_class="multinomial",solver="lbfgs", C=10, random_state=42) +softmax_reg.fit(X, y) + +x0, x1 = np.meshgrid( + np.linspace(0, 8, 500).reshape(-1, 1), + np.linspace(0, 3.5, 200).reshape(-1, 1), + ) +X_new = np.c_[x0.ravel(), x1.ravel()] + + +y_proba = softmax_reg.predict_proba(X_new) +y_predict = softmax_reg.predict(X_new) + +zz1 = y_proba[:, 1].reshape(x0.shape) +zz = y_predict.reshape(x0.shape) + +plt.figure(figsize=(10, 4)) +plt.plot(X[y==2, 0], X[y==2, 1], "g^", label="Iris-Virginica") +plt.plot(X[y==1, 0], X[y==1, 1], "bs", label="Iris-Versicolor") +plt.plot(X[y==0, 0], X[y==0, 1], "yo", label="Iris-Setosa") + +from matplotlib.colors import ListedColormap +custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0']) + +plt.contourf(x0, x1, zz, cmap=custom_cmap) +contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg) +plt.clabel(contour, inline=1, fontsize=12) +plt.xlabel("Largo de petalo", fontsize=14) +plt.ylabel("ancho de petalo", fontsize=14) +plt.legend(loc="center left", fontsize=14) +plt.axis([0, 7, 0, 3.5]) + + + +plt.show() +