def example(nfeature,m,case,nclass=2): X = 4*np.random.rand(nfeature,m)-2 if case == "linear": Y = X[0,:] + X[1,:] - 0.25 elif case == "quadratic": Y = X[1,:] - np.square(X[0,:]) + 1.5 elif case == "cubic": Y = X[1,:] - np.power(X[0,:],3) - 2*np.power(X[0,:],2)+ 1.5 elif case == "disk": Y = np.square(X[0,:])+np.square(X[1,:])-1 elif case == "ring": Y = 1.25*np.sqrt(np.square(X[0,:])+np.square(X[1,:])) Y = np.fmod(Y,nclass) elif case == "band": Y = X[0,:] + X[1,:] Y = np.fmod(Y,nclass) Y = np.maximum(Y,0.0) Y = np.round(Y) Y = np.minimum(Y,nclass-1) Y = np.expand_dims(Y,axis=0) return X,Y if __name__ == "__main__": nfeature = 2 m = 2000 case = "ring" nclass = 2 X,Y = example(nfeature,m,case,nclass) plot_results.plot_results_data((X,Y),nclass) plt.show()
def example(nfeature, m, case, nclass=2):
X = 4 * np.random.rand(nfeature, m) - 2
if case == "linear":
Y = X[0, :] + X[1, :] - 0.25
elif case == "quadratic":
Y = X[1, :] - np.square(X[0, :]) + 1.5
elif case == "cubic":
Y = X[1, :] - np.power(X[0, :], 3) - 2 * np.power(X[0, :], 2) + 1.5
elif case == "disk":
Y = np.square(X[0, :]) + np.square(X[1, :]) - 1
elif case == "ring":
Y = 1.25 * np.sqrt(np.square(X[0, :]) + np.square(X[1, :]))
Y = np.fmod(Y, nclass)
elif case == "band":
Y = X[0, :] + X[1, :]
Y = np.fmod(Y, nclass)
Y = np.maximum(Y, 0.0)
Y = np.round(Y)
Y = np.minimum(Y, nclass - 1)
Y = np.expand_dims(Y, axis=0)
return X, Y
if __name__ == "__main__":
nfeature = 2
m = 2000
case = "ring"
nclass = 2
X, Y = example(nfeature, m, case, nclass)
plot_results.plot_results_data(X, Y, nclass)
plt.show()
if noise:
Y = Y + 0.3 * np.random.randn(X.shape[1])
Y = np.maximum(Y, 0.0)
Y = np.round(Y)
Y = np.minimum(Y, nclass - 1)
Y = np.expand_dims(Y, axis=0)
# train and validation/test sets
if validpercent == 0:
return X, Y
else:
return X[:, :m], Y[:, 0:m], X[:, m:], Y[:, m:]
if __name__ == "__main__":
nfeature = 2
m = 1000
case = "quadratic"
nclass = 2
noise = False
validpercent = 0.0
if validpercent > 0:
Xtrain, Ytrain, Xvalid, Yvalid = example(nfeature, m, case, nclass,
noise, validpercent)
plot_results.plot_results_data((Xtrain, Ytrain),
nclass,
validation_data=(Xvalid, Yvalid))
else:
Xtrain, Ytrain = example(nfeature, m, case, nclass, noise,
validpercent)
plot_results.plot_results_data((Xtrain, Ytrain), nclass)
plt.show()