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123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
from sklearn import svm
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import random
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import numpy as np
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import math
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from pylab import scatter,figure, clf, plot, xlabel, ylabel, xlim, ylim, title, grid, axes, show,semilogx, semilogy
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import matplotlib.pyplot as plt
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from matplotlib.ticker import MaxNLocator
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from matplotlib.colors import BoundaryNorm,ListedColormap
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# Generation of data to train the SVM classifier
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# 100 vectors are generated. Vector have dimension 2 so can be represented as points
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NBVECS = 100
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VECDIM = 2
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# A cluster of point is generated around the origin.
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ballRadius = 0.5
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x = ballRadius * np.random.randn(NBVECS,2)
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# An annulus of point is generated around the central cluster.
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angle = 2.0*math.pi * np.random.randn(1,NBVECS)
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radius = 3.0+0.1*np.random.randn(1,NBVECS)
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xa = np.zeros((NBVECS,2))
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xa[:,0]=radius*np.cos(angle)
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xa[:,1]=radius*np.sin(angle)
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# All points are concatenated
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X_train=np.concatenate((x,xa))
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# First points (central cluster) are corresponding to class 0
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# OTher points (annulus) are corresponding to class 1
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Y_train=np.concatenate((np.zeros(NBVECS),np.ones(NBVECS)))
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# Some bounds are computed for the graphical representation
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x_min = X_train[:, 0].min()
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x_max = X_train[:, 0].max()
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y_min = X_train[:, 1].min()
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y_max = X_train[:, 1].max()
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# Training is done with a polynomial SVM
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clf = svm.SVC(kernel='poly',gamma='auto', coef0=1.1)
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clf.fit(X_train, Y_train)
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# The classifier is tested with a first point inside first class
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test1=np.array([0.4,0.1])
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test1=test1.reshape(1,-1)
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predicted1 = clf.predict(test1)
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# Predicted class should be 0
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print(predicted1)
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# Second test is made with a point inside the second class (in the annulus)
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test2=np.array([x_max,0]).reshape(1,-1)
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predicted2 = clf.predict(test2)
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# Predicted class should be 1
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print(predicted2)
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# The parameters of the trained classifier are printed to be used
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# in CMSIS-DSP
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supportShape = clf.support_vectors_.shape
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nbSupportVectors=supportShape[0]
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vectorDimensions=supportShape[1]
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print("nbSupportVectors = %d" % nbSupportVectors)
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print("vectorDimensions = %d" % vectorDimensions)
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print("degree = %d" % clf.degree)
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print("coef0 = %f" % clf.coef0)
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print("gamma = %f" % clf._gamma)
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print("intercept = %f" % clf.intercept_)
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dualCoefs=clf.dual_coef_
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dualCoefs=dualCoefs.reshape(nbSupportVectors)
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supportVectors=clf.support_vectors_
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supportVectors = supportVectors.reshape(nbSupportVectors*VECDIM)
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print("Dual Coefs")
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print(dualCoefs)
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print("Support Vectors")
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print(supportVectors)
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# Graphical representation to display the cluster of points
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# and the SVM boundary
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r=plt.figure()
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plt.axis('off')
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XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
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Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(XX.shape)
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levels = MaxNLocator(nbins=15).tick_values(Z.min(), Z.max())
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#cmap = plt.get_cmap('gray')
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newcolors = ['#FFFFFF','#FFFFFF']
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cmap = ListedColormap(newcolors)
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norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
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plt.pcolormesh(XX, YY, Z > 0, cmap=cmap,norm=norm)
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plt.contour(XX, YY, Z, colors=['k', 'k', 'k'],
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linestyles=['--', '-', '--'], levels=[-.5, 0, .5])
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scatter(x[:,0],x[:,1],s=1.0,color='#FF6B00')
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scatter(xa[:,0],xa[:,1],s=1.0,color='#95D600')
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# The test points are displayed in red.
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scatter(test1[:,0],test1[:,1],s=6.0,color='Red')
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scatter(test2[:,0],test2[:,1],s=6.0,color='Red')
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#r.savefig('fig1.jpeg')
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#plt.close(r)
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show()
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#r=plt.figure()
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#plt.axis('off')
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#scatter(x[:,0],x[:,1],s=1.0,color='#FF6B00')
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#scatter(xa[:,0],xa[:,1],s=1.0,color='#95D600')
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#r.savefig('fig2.jpeg')
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#plt.close(r) |