Esempio n. 1
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]
type = ['(TEST)', '(TRAIN)']
scaled = ['(PLAIN)', '(NORMALIZED)']
col = 8
valueCol = 4
bincount = 500
values = True
features = False

timeData = False

if __name__ == '__main__':
    tempCWD = os.getcwd()

    os.chdir('D:\Workspace\NLTK comments\src\RatingPrediction')
    X, y, wd, td, out = getDataSets(normalize=False, selected=False)

    y = y[:, valueCol]
    print np.min(y)
    print np.max(y)
    print np.mean(y)
    print 'Loaded testing data\n'
    X_scaled = preprocessing.scale(X)
    print "Scaled testing Features\n"

    X_binned = binScaling(X[:, col], 10)
    print 'Binned Features'

    print 'Number of training rows:', len(X)
    os.chdir(tempCWD)
Esempio n. 2
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                break;
            ind += 1
        
        
        
        print 'Test',j," - ", DCG/float(iDCG)
        NDGC.append(DCG/float(iDCG))
        
    return np.mean(NDGC), np.mean(tau)
     
reg = True
value = 1
feature = 1
if __name__ == '__main__':
    
    X0, y0, wordData,topicData,socialData, out = getDataSets(normalize=True, selected=False)
    y0 = y0[:,value]
    print out
        
    #test(X,y)
        
    if feature == 0:
        X0 = X0
    elif feature == 1:
        X0 = wordData
    elif feature == 2:
        X0 = topicData
    elif feature == 3:
        X0 = np.hstack((X0,wordData))
    elif feature == 4:
        X0 = np.hstack((X0,topicData))
Esempio n. 3
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]
type = ["(TEST)", "(TRAIN)"]
scaled = ["(PLAIN)", "(NORMALIZED)"]
col = 8
valueCol = 4
bincount = 500
values = True
features = False

timeData = False

if __name__ == "__main__":
    tempCWD = os.getcwd()

    os.chdir("D:\Workspace\NLTK comments\src\RatingPrediction")
    X, y, wd, td, out = getDataSets(normalize=False, selected=False)

    y = y[:, valueCol]
    print np.min(y)
    print np.max(y)
    print np.mean(y)
    print "Loaded testing data\n"
    X_scaled = preprocessing.scale(X)
    print "Scaled testing Features\n"

    X_binned = binScaling(X[:, col], 10)
    print "Binned Features"

    print "Number of training rows:", len(X)
    os.chdir(tempCWD)