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- 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()
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