ShivendraAgrawal

SOURCE http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/

# Correlation
import numpy as np
from scipy.stats import pearsonr
np.random.seed(0)
size = 300
x = np.random.normal(0, 1, size)
print "Lower noise", pearsonr(x, x + np.random.normal(0, 1, size))
print "Higher noise", pearsonr(x, x + np.random.normal(0, 10, size))

ShivendraAgrawal

import numpy as np
from sklearn import datasets

# sigmoid function
def activation(x,derivative=False):
    if(derivative==True):
        return x*(1-x)
    return 1/(1+np.exp(-x))

iris = datasets.load_iris()