To simplify the code examples in these notebooks, we populate the namespace with functions from numpy and matplotlib:
%pylab inline
Populating the interactive namespace from numpy and matplotlib
cat_data = array(['male', 'female', 'male', 'male', 'female', 'male', 'female', 'female'])
def cat_to_num(data):
categories = unique(data)
features = []
for cat in categories:
binary = (data == cat)
features.append(binary.astype("int"))
return features
cat_to_num(cat_data)
[array([0, 1, 0, 0, 1, 0, 1, 1]), array([1, 0, 1, 1, 0, 1, 0, 0])]
cabin_data = array(["C65", "", "E36", "C54", "B57 B59 B63 B66"])
def cabin_features(data):
features = []
for cabin in data:
cabins = cabin.split(" ")
n_cabins = len(cabins)
# First char is the cabin_char
try:
cabin_char = cabins[0][0]
except IndexError:
cabin_char = "X"
n_cabins = 0
# The rest is the cabin number
try:
cabin_num = int(cabins[0][1:])
except:
cabin_num = -1
# Add 3 features for each passanger
features.append( [cabin_char, cabin_num, n_cabins] )
return features
cabin_features(cabin_data)
[['C', 65, 1], ['X', -1, 0], ['E', 36, 1], ['C', 54, 1], ['B', 57, 4]]
num_data = array([1, 10, 0.5, 43, 0.12, 8])
def normalize_feature(data, f_min=-1, f_max=1):
d_min, d_max = min(data), max(data)
factor = (f_max - f_min) / (d_max - d_min)
normalized = f_min + data*factor
return normalized, factor
normalize_feature(num_data)
(array([-0.95335821, -0.53358209, -0.9766791 , 1.00559701, -0.99440299, -0.62686567]), 0.046641791044776115)