def getErrorRate(logP, yActual): predictedRes = getPredictions(logP) yActual = yActual.flatten().astype(int) return np.mean(predictedRes != yActual) def getPredictions(logP): predictedClass = np.argmax(logP, axis=1) return predictedClass # %% # Load data from spamData.mat xtrain, ytrain, xtest, ytest = du.loadData('spamData.mat') xtrain = du.binarization(xtrain) xtest = du.binarization(xtest) # Create an array of alpha values, from 0 to 100 with step size 0.5 alphaStart = 0 alphaEnd = 100 alphaStepSize = 0.5 alphaArr = np.arange(alphaStart, alphaEnd + alphaStepSize, alphaStepSize) trainErr = np.zeros(len(alphaArr)) testErr = np.zeros(len(alphaArr)) # %% for j in range(len(alphaArr)): alpha = alphaArr[j]