View svm.py
# Mathieu Blondel, September 2010
# License: BSD 3 clause
import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers
def linear_kernel(x1, x2):
return np.dot(x1, x2)
View lda_gibbs.py
"""
(C) Mathieu Blondel - 2010
License: BSD 3 clause
Implementation of the collapsed Gibbs sampler for
Latent Dirichlet Allocation, as described in
Finding scientifc topics (Griffiths and Steyvers)
"""
View kmeans.py
# Copyright Mathieu Blondel December 2011
# License: BSD 3 clause
import numpy as np
import pylab as pl
from sklearn.base import BaseEstimator
from sklearn.utils import check_random_state
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster import KMeans as KMeansGood
View kernel_kmeans.py
"""Kernel K-means"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import numpy as np
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state
View second_order_ode.py
#!/usr/bin/env python
"""
Find the solution for the second order differential equation
u'' = -u
with u(0) = 10 and u'(0) = -5
using the Euler and the Runge-Kutta methods.
View perceptron.py
# Mathieu Blondel, October 2010
# License: BSD 3 clause
import numpy as np
from numpy import linalg
def linear_kernel(x1, x2):
return np.dot(x1, x2)
def polynomial_kernel(x, y, p=3):
View letor_metrics.py
# (C) Mathieu Blondel, November 2013
# License: BSD 3 clause
import numpy as np
def ranking_precision_score(y_true, y_score, k=10):
"""Precision at rank k
Parameters
View out_of_scope.py
def test():
print(i)
i = 1
test()
View einsum.py
import numpy as np
rng = np.random.RandomState(0)
print "Trace"
A = rng.rand(3, 3)
print np.trace(A)
print np.einsum("ii", A)
print
View kernel_sgd.py
# Mathieu Blondel, May 2012
# License: BSD 3 clause
import numpy as np
def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False):
XX = np.sum(X * X, axis=1)[:, np.newaxis]
YY = np.sum(Y ** 2, axis=1)[np.newaxis, :]
distances = np.dot(X, Y.T)
distances *= -2