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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 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 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 statistical_tests.py
# Mathieu Blondel, February 2012
# License: BSD 3 clause
# Port to Python of examples in chapter 5 of
# "Introductory Statistics with R" by Peter Dalgaard
import numpy as np
from scipy.stats import ttest_1samp, wilcoxon, ttest_ind, mannwhitneyu
# daily intake of energy in kJ for 11 women
View nmf_cd.py
"""
NMF by coordinate descent, designed for sparse data (without missing values)
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
import numba
View gaussian_process.py
"""Gaussian processes"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import numpy as np
from scipy.linalg import cholesky, solve_triangular
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.metrics.pairwise import pairwise_kernels
View multiclass_svm.py
"""
Multiclass SVMs (Crammer-Singer formulation).
A pure Python re-implementation of:
Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex.
Mathieu Blondel, Akinori Fujino, and Naonori Ueda.
ICPR 2014.
http://www.mblondel.org/publications/mblondel-icpr2014.pdf
"""
View sparse_multiclass_numba.py
"""
(C) August 2013, Mathieu Blondel
# License: BSD 3 clause
This is a Numba-based reimplementation of the block coordinate descent solver
(without line search) described in the paper:
Block Coordinate Descent Algorithms for Large-scale Sparse Multiclass
Classification. Mathieu Blondel, Kazuhiro Seki, and Kuniaki Uehara.
Machine Learning, May 2013.
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):