MechCoder

commit e5ca40b8972b0b7cd2210c1108ac696e94f675ce
Author: Manoj-Kumar-S <manojkumarsivaraj334@gmail.com>
Date:   Sat Sep 7 23:53:55 2013 +0530

    Changed n to int(n)

diff --git a/sympy/solvers/ode.py b/sympy/solvers/ode.py
index a464f8a..9620b04 100644
--- a/sympy/solvers/ode.py
+++ b/sympy/solvers/ode.py

MechCoder

from sklearn.datasets.samples_generator import make_regression
from scipy import sparse
X, y = make_regression(n_samples=500, n_features=20000, random_state=0)
X[X < 2.5] = 0
mat = sparse.coo_matrix(X)
clf = ElasticNetCV(max_iter=2000)
%timeit clf.fit(mat,y)
1 loops, best of 3: 376 s per loop
clf = ElasticNetCV(max_iter=2000)
%timeit clf.fit(X, y)

MechCoder

Dense data

0 alpha 0.0855941772461
1 alpha 0.0869829654694
2 alpha 0.0868599414825
3 alpha 0.0869789123535
4 alpha 0.0870821475983
5 alpha 0.0872299671173
6 alpha 0.0973160266876
7 alpha 0.0973291397095

MechCoder

# -*- coding: utf-8 -*-
"""
Strong rules for coordinate descent

Author: Fabian Pedregosa <fabian@fseoane.net>
"""

import numpy as np
from scipy import linalg

MechCoder

# Accessing rows and columns.

for ptr in xrange(len(csc.indptr) - 1):
    strptr = csc.indptr[ptr]
    endptr = csc.indptr[ptr + 1]
    temp = xrange(strptr, endptr)
    if temp:
        for row in temp:
            print ptr, csc.data[row], csc.indices[row]

MechCoder

from numpy.distutils.system_info import get_info
import os
from os.path import join

import numpy


def configuration():
    from numpy.distutils.misc_util import Configuration

MechCoder

from libc.math cimport fabs, sqrt
cimport cython

cdef extern from "cblas.h":

    void daxpy "cblas_daxpy"(int N, double alpha, double *X, int incX,
                             double *Y, int incY)

def b(int x):
    print x

MechCoder

import pylab as pl
import numpy as np
from sklearn.linear_model import *
from sklearn.datasets import make_regression
import time


def plot():
    pl.figure("Benchmark with nogil")

MechCoder

from sklearn.linear_model import ElasticNetCV
from sklearn.datasets import make_regression
import numpy as np
from scipy.sparse import csr_matrix


X, y = make_regression(n_samples=2000, n_features=2000)
clf = ElasticNetCV(n_jobs=4, cv=3, n_alphas=100, fit_intercept=False, l1_ratio=np.linspace(0.1, 0.9, 5))
clf.fit(X, y)

MechCoder

from sklearn.linear_model import LogisticRegressionCV
from sklearn.datasets import make_classification
from time import time
import numpy as np

rng = np.random.RandomState(0)
X, y = make_classification(n_samples=2000, n_features=2000, random_state=rng)
clf = LogisticRegressionCV(n_jobs=4, Cs=[1, 10, 100, 1000], cv=10)
t = time()
clf.fit(X, y)