[英]scipy optimization error for logistic regression with regularization
i get the following error while i try to minimize the following function using CG: 我在尝试使用CG最小化以下功能时收到以下错误:
def costFunctionReg(theta, X, y, labda):
import numpy as np
import sigmoid as sg
grad=np.zeros((28,1),dtype=float)
#setting predictions
m=len(y)
#calculating predictions using the sigmoid function
predictions=sg.sigmoid(np.dot(X,theta))
predictions=predictions.reshape(np.size(predictions),1)
y=y.reshape(np.size(y),1)
predictionsminusy=predictions-y
#predictionsminusyr=predictionsminusy.reshape(np.size(predictionsminusy),1)
#calculating the cost of using theta as per logistic regression equation
logistic=(-y*np.array(np.log(predictions)))-((1-y)*np.array(np.log(1-predictions)))
endr=np.size(theta)
J=1./m * np.sum(logistic)+(labda/(2*m))*np.sum(theta[0:endr]**2)
#computing partial derivatives w.r.t to parameters
endg=np.size(grad)
endx,end=np.shape(X)
grad[0,:]= 1.0/m * np.sum(np.dot((X[:,0]) ,(predictionsminusy) ));
grad[1:endg,:]=((((1./m )* (np.dot(((predictionsminusy.conj().T)), (X[:,1:end]))))).conj().T)
return [J,grad]
I get the error: 我收到错误:
ValueError: matrices are not aligned
/usr/lib/python2.7/dist-packages/scipy/optimize/optimize.py(1151)_minimize_cg() 1150 while (gnorm > gtol) and (k < maxiter): -> 1151 deltak = numpy.dot(gfk, gfk) 1152 /usr/lib/python2.7/dist-packages/scipy/optimize/optimize.py(1151)_minimize_cg()1150而(gnorm> gtol)和(k <maxiter):-> 1151 deltak = numpy.dot(gfk ,gfk)1152
尝试return J, grad.flatten()
而不是return J, grad
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