#testing test1 = testSet[:,0:4] test2 = testSet[:,5:9] testing = concatenate((test1, test2), axis=1) #training target tTarget = trainSet[:,4] tTarget = np.asmatrix(tTarget) #test target testTarget = testSet[:,4] #concatenate #training = concatenate((training, -ones((shape(training)[0],1))), axis=1) beta = linreg2.linreg2(training, tTarget) testing = concatenate((testing, -ones((shape(testing)[0],1))), axis=1) testout = dot(testing, beta) error = 0 error = [(testout[i,0]-testTarget[i])**2 for i in range(30)] print sum(error) """print testing.shape print testout.shape print tTarget.shape print testTarget.shape print testout[0] print testTarget[0]""" p = pcn_logic_eg.pcn(training, tTarget)
data.close()
trainingIn = trainSet[0]
target = trainSet[1]
reduceTraining = trainingIn[0:2000,:]
reduceTarget = target[0:2000,]
reduceTarget = np.asmatrix(reduceTarget)
testInputs = testSet[0]
testTarget = testSet[1]
reducetInput = testInputs[0:2000,:]
reducetTarget = testTarget[0:2000,]
reducetTarget = np.asmatrix(reducetTarget)
beta = linreg2.linreg2(reduceTraining, reduceTarget)
reducetInput = concatenate((reducetInput, -ones((shape(reducetInput)[0],1))),axis=1)
reducetOutput = dot(reducetInput, beta)
error=sum((reducetOutput - reducetTarget)**2)
print ("Training Inputs")
print reduceTraining
print ("Test Inputs")
print reducetInput
print ("Error")
print error
