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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)) |
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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() |
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