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mblondel / check_convex.py
Last active Jan 20, 2021
A small script to get numerical evidence that a function is convex
View check_convex.py
# Authors: Mathieu Blondel, Vlad Niculae
# License: BSD 3 clause
import numpy as np
def _gen_pairs(gen, max_iter, max_inner, random_state, verbose):
rng = np.random.RandomState(random_state)
# if tuple, interpret as randn
@mblondel
mblondel / kmeans.py
Last active Jan 19, 2021
Fuzzy K-means and K-medians
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
@mblondel
mblondel / statistical_tests.py
Last active Jan 19, 2021
t-test and wilcoxon-test examples in Python
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
@mblondel
mblondel / letor_metrics.py
Last active Jan 18, 2021
Learning to rank metrics.
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
@mblondel
mblondel / projection_simplex.py
Created Nov 2, 2014
Projection onto the simplex
View projection_simplex.py
"""
Implements three algorithms for projecting a vector onto the simplex: sort, pivot and bisection.
For details and references, see the following paper:
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
@mblondel
mblondel / hmm.tex
Created Jul 12, 2010
Good-looking HMM and Lattice diagrams using TikZ
View hmm.tex
% (C) Mathieu Blondel, July 2010
\documentclass[a4paper,10pt]{article}
\usepackage[english]{babel}
\usepackage[T1]{fontenc}
\usepackage[ansinew]{inputenc}
\usepackage{lmodern}
\usepackage{amsmath}
@mblondel
mblondel / lda_gibbs.py
Last active Dec 27, 2020
Latent Dirichlet Allocation with Gibbs sampler
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 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
@mblondel
mblondel / svm.py
Last active Nov 2, 2020
Support Vector Machines
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)
@mblondel
mblondel / perceptron.py
Last active Oct 27, 2020
Kernel Perceptron
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):