fabianp

import numpy as np
from scipy import linalg
from datetime import datetime

import gc

mu_sec = 1e-6 # number of seconds in one microseconds

solve_time =[]
solve_triangular_time =[]

fabianp

"""
Just some experiments with pls
"""

import numpy as np

m, n = 3, 3
X = np.random.randn(m, n)
Y = np.random.randn(m, n)

fabianp

import pylab as pl
import numpy as np
import scipy.linalg

nrm2, = scipy.linalg.get_blas_funcs(('nrm2',), np.empty(0, dtype=np.float32))

x_min, x_max = -4., 0
y_min, y_max = 0, 4.
h = 134567e-7

fabianp

import numpy as np


def lse(A, b, B, d):
    """
    Equality-contrained least squares.

    The following algorithm minimizes ||Ax - b|| subject to the
    constrain Bx = d.

fabianp

int covariance(int n, int m, double data[], int strides[2], char mode, double matrix[])
/* This algorithm is described in:
 * B.P. Welford:
 * "Note on a method for calculating corrected sums of squares and products."
 * Technometrics 4(3): 419-420 (1962).
 * Also see:
 * Peter M. Neely:
 * "Comparison of several algorithms for computation of means, standard
 * deviations and correlation coefficients."
 * Communications of the ACM 9(7): 496-499 (1966).

fabianp

import numpy as np

from scikits.learn import svm, grid_search, datasets
iris = datasets.load_iris()
parameters = {'C': [np.array(1)]}
svr = svm.SVC()
clf = grid_search.GridSearchCV(svr, parameters)
clf.fit(iris.data, iris.target)
print clf.grid_points_scores_

fabianp

# -*- coding: utf-8 -*-
"""
===================================
Swiss Roll reduction with LLE
===================================

An illustration of Swiss Roll reduction
with locally linear embedding

"""

fabianp

"""
===============================================
Handwritten digits and Locally Linear Embedding 
===============================================

An illustration of locally linear embedding on the digits dataset.
"""

# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# License: BSD, (C) INRIA 2011

fabianp

"""
Hilbert matrix using numpy. Contains:

  - hilb(n, m) : returns the Hilbert matrix of size (n, m)

  - invhilb(n) : returns the inverse of the Hilbert matrix of size (n, n)
"""
import numpy as np
from math import factorial

fabianp

import numpy as np
from scipy import linalg

def cgs(A):
    """Classical Gram-Schmidt (CGS) algorithm"""
    m, n = A.shape
    R = np.zeros((n, n))
    Q = np.empty((m, n))
    R[0, 0] = linalg.norm(A[:, 0])
    Q[:, 0] = A[:, 0] / R[0, 0]