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#! /usr/bin/env python
# Last Change: Mon Jul 09 05:00 PM 2007 J
# Various utilities for examples
import numpy as N
from numpy.testing import set_package_path, restore_path
from scikits.learn.datasets import oldfaithful, pendigits, iris
def get_faithful():
"""Return faithful data as a nx2 array, first column being duration, second
being waiting time."""
# Load faithful data, convert waiting into integer, remove L, M and S data
data = oldfaithful.load()
tmp1 = []
tmp2 = []
for i in data:
if not (i[0] == 'L' or i[0] == 'M' or i[0] == 'S'):
tmp1.append(i[0])
tmp2.append(i[1])
waiting = N.array([int(i) for i in tmp1], dtype = N.float)
duration = N.array([i for i in tmp2], dtype = N.float)
waiting = waiting[:, N.newaxis]
duration = duration[:, N.newaxis]
return N.concatenate((waiting, duration), 1)
def get_pendigits():
"""Return faithful data as a nx2 array, first column being duration, second
being waiting time."""
# Load faithful data, convert waiting into integer, remove L, M and S data
data = pendigits.load()
return data['training']['x'], data['training']['y']
def scale(data, mode = 'sym'):
"""Linearly scale data in place such as each col is in the range [0..1].
Returns the translation factor t and scaling factor s. You can retrieve
the original values with data = s * scaled + t."""
n = N.min(data, 0)
m = N.max(data, 0)
if mode == 'sym':
t = n + 0.5 * (m - n)
s = 0.5 * (m - n)
elif mode == 'right':
t = n
s = m - n
else:
raise ValueError("Mode %s not recognized" % mode)
data -= t
data /= s
return t, s
if __name__ == '__main__':
a = N.random.randn(10, 2)
b = a.copy()
scale(a)
print a
scale(b, 'right')
print b
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