コード例 #1
0
ファイル: pcatest.py プロジェクト: PhilippeNguyen/COMP598Ass1
from OLSgradientDescent import gradientDescent
import pandas as pd
from sklearn.decomposition import PCA
from sklearn import linear_model


nInstances = 500
nParams = 100
noiseLevel = 500
trueWeights = np.asarray(range(nParams))
#    trueWeights[5:] = 0
#    trueWeights[:4] = 0
X = np.random.rand(nInstances,nParams)
Y = np.dot(X,trueWeights) + noiseLevel*np.random.rand(np.size(nInstances))

test = gradientDescent(Y, X, 0, 1000, 0.01)


input = pd.read_csv('OnlineNewsPopularity/OnlineNewsPopularity.csv')

inp = input.as_matrix()

X = inp[:,1:60]
Y = inp[:,60]
Y = Y.reshape(Y.shape[0],1)
X, Y = shuffle(X,Y)
pca = PCA(n_components = 3)

pca.fit(X)
print(pca.explained_variance_ratio_) 
X_new = pca.transform(X)
コード例 #2
0
ファイル: CSVTest.py プロジェクト: PhilippeNguyen/COMP598Ass1
            clf = linear_model.Lasso(alpha=lambArray[j], max_iter = 10000)
            clf.fit(Xtrain,Ytrain)


            Ypred = clf.predict(Xvalid)
            error  = mse(Ypred,Yvalid)
            lassoErrorArray[i,j] = error

        #Estimation and prediction using closed form solution 
            wEst = OLSClosed(Xtrain,Ytrain,L2 = lambArray[j])
            Ypred = np.dot(Xvalid,wEst)
            error = mse(Ypred,Yvalid)            
            closedErrorArray[i,j]= error

        #Estimation and prediction using gradient descent
            wEst = gradientDescent(Ytrain,Xtrain,0.0001, 10000, 0.01, 'ridge', lambArray[j])
            Ypred = np.dot(Xvalid,wEst['Weights'])
            error = mse(Ypred,Yvalid)
            gradientErrorArray[i,j]= error
            
        #Estimation and prediction using closed form solution AND PCA
            wEst = OLSClosed(np.hstack((np.ones((np.size(Xtrain,axis=0) ,1)),pca.transform(Xtrain[:,1:]))),Ytrain,L2 = lambArray[j])
            Ypred = np.dot(np.hstack((np.ones((np.size(Xvalid,axis=0) ,1)),pca.transform(Xvalid[:,1:]))),wEst)
            error = mse(Ypred,Yvalid)            
            pcaErrorArray[i,j]= error    

    aveLassoError = np.average(lassoErrorArray,axis = 0)
    aveClosedError = np.average(closedErrorArray,axis = 0)
    aveGradientError = np.average(gradientErrorArray,axis = 0)
    avePCAError = np.average(pcaErrorArray,axis = 0)