import numpy as np import matplotlib.pyplot as plt from sklearn import svm import gradio as gr from PIL import Image def calculate_score(clf): xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) X_test = np.c_[xx.ravel(), yy.ravel()] Y_test = np.logical_xor(xx.ravel() > 0, yy.ravel() > 0) return clf.score(X_test, Y_test) def getColorMap(kernel, gamma): # prepare the training dataset np.random.seed(0) X = np.random.randn(300, 2) Y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0) # fit the model clf = svm.NuSVC(kernel=kernel, gamma=gamma) clf.fit(X, Y) #create a grid for the plotting the decision function xx, yy = np.meshgrid(np.linspace(-3, 3, 500), np.linspace(-3, 3, 500)) # plot the decision function for each datapoint on the grid Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.figure(figsize=(10, 4)) plt.imshow( Z, interpolation="nearest", extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect="auto", origin="lower", cmap=plt.cm.PuOr_r, ) contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2, linestyles="dashed") plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired, edgecolors='k') plt.title(f"Decision function for Non-Linear SVC with the {kernel} kernel and '{gamma}' gamma ", fontsize='14') #title plt.xlabel("X",fontsize='13') #adds a label in the x axis plt.ylabel("Y",fontsize='13') #adds a label in the y axis return plt, calculate_score(clf) #XOR_TABLE markdown text XOR_TABLE = """
A | B | A XOR B |
---|---|---|
0 | 0 | 0 |
0 | 1 | 1 |
1 | 0 | 1 |
1 | 1 | 0 |