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CMSIS/DSP/Examples/ARM/arm_bayes_example/train.py

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+from sklearn.naive_bayes import GaussianNB
+import random
+import numpy as np
+import math
+
+from pylab import scatter,figure, clf, plot, xlabel, ylabel, xlim, ylim, title, grid, axes, show,semilogx, semilogy
+import matplotlib.pyplot as plt
+from matplotlib.font_manager import FontProperties
+
+# Generation of data to train the  classifier
+# 100 vectors are generated. Vector have dimension 2 so can be represented as points
+NBVECS = 100
+VECDIM = 2
+
+# 3 cluster of points are generated
+ballRadius = 1.0
+x1 = [1.5, 1] +  ballRadius * np.random.randn(NBVECS,VECDIM)
+x2 = [-1.5, 1] + ballRadius * np.random.randn(NBVECS,VECDIM)
+x3 = [0, -3] + ballRadius * np.random.randn(NBVECS,VECDIM)
+
+# All points are concatenated
+X_train=np.concatenate((x1,x2,x3))
+
+# The classes are 0,1 and 2.
+Y_train=np.concatenate((np.zeros(NBVECS),np.ones(NBVECS),2*np.ones(NBVECS)))
+
+gnb = GaussianNB()
+gnb.fit(X_train, Y_train)
+
+print("Testing")
+y_pred = gnb.predict([[1.5,1.0]])
+print(y_pred)
+
+y_pred = gnb.predict([[-1.5,1.0]])
+print(y_pred)
+
+y_pred = gnb.predict([[0,-3.0]])
+print(y_pred)
+
+# Dump of data for CMSIS-DSP
+
+print("Parameters")
+# Gaussian averages
+print("Theta = ",list(np.reshape(gnb.theta_,np.size(gnb.theta_))))
+
+# Gaussian variances
+print("Sigma = ",list(np.reshape(gnb.sigma_,np.size(gnb.sigma_))))
+
+# Class priors
+print("Prior = ",list(np.reshape(gnb.class_prior_,np.size(gnb.class_prior_))))
+
+print("Epsilon = ",gnb.epsilon_)
+
+
+# Some bounds are computed for the graphical representation
+x_min = X_train[:, 0].min()
+x_max = X_train[:, 0].max()
+y_min = X_train[:, 1].min()
+y_max = X_train[:, 1].max()
+
+font = FontProperties()
+font.set_size(20)
+
+r=plt.figure()
+plt.axis('off')
+plt.text(1.5,1.0,"A", verticalalignment='center', horizontalalignment='center',fontproperties=font)
+plt.text(-1.5,1.0,"B",verticalalignment='center', horizontalalignment='center', fontproperties=font)
+plt.text(0,-3,"C", verticalalignment='center', horizontalalignment='center',fontproperties=font)
+scatter(x1[:,0],x1[:,1],s=1.0,color='#FF6B00')
+scatter(x2[:,0],x2[:,1],s=1.0,color='#95D600')
+scatter(x3[:,0],x3[:,1],s=1.0,color='#00C1DE')
+#r.savefig('fig.jpeg')
+#plt.close(r)
+show()