amueller

import numpy as np


def km_segmentation(image, n_segments=100, ratio=50, max_iter=100):                       
    # initialize on grid:                                                                 
    height, width = image.shape[:2]                                                       
    # approximate grid size for desired n_segments                                        
    step = np.sqrt(height * width / n_segments)                                           
    grid_y, grid_x = np.mgrid[:height, :width]                                            
    means_y = grid_y[::step, ::step]

amueller

                                                                                       
def check_grad(f, fprime, x0):                                                            
    eps = 1e-5                                                                            
    approx = np.zeros(len(x0))                                                            
    for i in xrange(len(x0)):                                                             
        x0_ = x0.copy()                                                                   
        x0_[i] += eps                                                                     
        approx[i] = (f(x0_) - f(x0)) / eps                                                
    return np.linalg.norm(approx.ravel() - fprime(x0).ravel())                            
   

amueller

##########################################################################
# Maximum Response filterbank from
# http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html
# based on several edge and bar filters.
# Adapted to Python by Andreas Mueller [email protected]
# Share and enjoy
#

import numpy as np
import matplotlib.pyplot as plt

amueller

def make_node(p, name=1):
    if p == 0:
        return [name]
    return [name, make_node(p - 1, 10 * name + 0),
                  make_node(p - 1, 10 * name + 1)]


def depth_first(p):
    print(p[0])
    if len(p) == 3:

amueller

======================================================================
FAIL: test_texture.TestLBP.test_default
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest
    self.test(*self.arg)
  File "/home/andy/checkout/scikits.image/skimage/feature/tests/test_texture.py", line 163, in test_default
    np.testing.assert_array_equal(lbp, ref)
  File "/usr/lib/python2.7/dist-packages/numpy/testing/utils.py", line 707, in assert_array_equal
    verbose=verbose, header='Arrays are not equal')

amueller

import numpy as np
from scipy import sparse
from sklearn import svm
from nose.tools import assert_raises

X = np.array([[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]])
X_sp = sparse.lil_matrix(X)
Y = [1, 1, 1, 2, 2, 2]

sp = svm.sparse.SVC(C=1, kernel=lambda x, y: x * y.T, probability=True)

amueller

import numpy as np
from sklearn.feature_selection import chi2
data = np.load("values.npy")
labels = np.load("labels.npy")
print(chi2(data, labels))

amueller

blub

amueller

# (c) 2012 Andreas Mueller [email protected]
# License: BSD 2-Clause
#
# See my blog for details: http://peekaboo-vision.blogspot.com

import numpy as np

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

amueller

from sklearn.utils.testing import all_estimators
import numpy as np

all_objects = []
for C in all_estimators():
    try:
        all_objects.append(C[1]())
    except:
        pass
params = [c.get_params().keys() for c in all_objects]