repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
Tong-Chen/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 2 | 7107 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
... | bsd-3-clause |
appapantula/scikit-learn | examples/applications/svm_gui.py | 287 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
JohanComparat/nbody-npt-functions | bin/bin_SMHMr/MD10_add_eBOSS_COSMO.py | 1 | 7842 | from astropy.cosmology import FlatLambdaCDM
import astropy.units as u
cosmoMD = FlatLambdaCDM(H0=67.77*u.km/u.s/u.Mpc, Om0=0.307115, Ob0=0.048206)
import glob
import astropy.io.fits as fits
import os
import time
import numpy as n
import sys
# specific functions
from scipy.stats import norm
from scipy.integrate import... | cc0-1.0 |
brev/nupic | src/nupic/research/monitor_mixin/plot.py | 19 | 5187 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2014-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This p... | agpl-3.0 |
mprelee/data-incubator-capstone | src/k_neighbors.py | 1 | 2302 | # Build Bag-of-Words model using a CountVectorizer
# We will build a CountVectorizer, a one-hot vectorizer, and a tfidf vectorizer
# Matt Prelee
import pickle
import re
import pandas as pd
import numpy as np
import time
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, Tfi... | gpl-2.0 |
CforED/Machine-Learning | examples/classification/plot_classifier_comparison.py | 36 | 5123 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=====================
Classifier comparison
=====================
A comparison of a several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.
This should be taken with ... | bsd-3-clause |
vermouthmjl/scikit-learn | sklearn/covariance/robust_covariance.py | 105 | 29653 | """
Robust location and covariance estimators.
Here are implemented estimators that are resistant to outliers.
"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
from scipy import linalg
from scipy.stats import chi2
from . import empir... | bsd-3-clause |
Titan-C/scikit-learn | sklearn/externals/joblib/__init__.py | 54 | 5087 | """Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular, joblib offers:
1. transparent disk-caching of the output values and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
3. logging and tracing of the execution
Joblib is optimized to be **fast** and **r... | bsd-3-clause |
DSLituiev/scikit-learn | sklearn/model_selection/_split.py | 21 | 57608 | """
The :mod:`sklearn.model_selection._split` module includes classes and
functions to split the data based on a preset strategy.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Girsel <olivier.grisel@ensta.org>
# Ragha... | bsd-3-clause |
TechplexEngineer/DB-Benchmarking-Tools | plotting/genstats.py | 1 | 2006 | #!/usr/bin/env python
###
# Code to calculate statictics from dump file
# B.Bourque 7/8/2015
###
import matplotlib.pyplot as plt
import matplotlib.dates as md
import numpy as np
from datetime import datetime, timedelta
# This function expects the data in [filename] to be tab separated.
# Col1 is the unix timestamp a... | mit |
arnavd96/Cinemiezer | myvenv/lib/python3.4/site-packages/numpy/doc/creation.py | 118 | 5507 | """
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from... | mit |
pearsonlab/nipype | nipype/utils/config.py | 9 | 5668 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
'''
Created on 20 Apr 2010
logging options : INFO, DEBUG
hash_method : content, timestamp
@author: Chris Filo Gorgolewski
'''
from future import standard_library
standard_library.install_aliases()
from bu... | bsd-3-clause |
ningchi/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
coders-creed/botathon | src/data_helpers.py | 1 | 1045 | # -*- coding: utf-8 -*-
# @Author: karthik
# @Date: 2016-12-10 23:04:24
# @Last Modified by: chandan
# @Last Modified time: 2016-12-11 12:19:29
from models.company import Company
import pandas as pd
FILENAME = "resources/cnx500.csv"
# arg parser
def parse_args(args):
arg_string = ' '.join(args)
return set(filt... | mit |
spnow/grr | gui/plugins/flow_management.py | 1 | 29906 | #!/usr/bin/env python
# Copyright 2011 Google Inc. All Rights Reserved.
"""GUI elements allowing launching and management of flows."""
import os
import StringIO
import urllib
import matplotlib.pyplot as plt
from grr.gui import renderers
from grr.gui.plugins import crash_view
from grr.gui.plugins import fileview
fr... | apache-2.0 |
snurk/meta-strains | final_algo/trash/temp_ig.py | 1 | 1049 | import pandas as pd
import csv
import networkx as nx
from graph_functions import *
from read_files import read_graph
def read_answers_1(G, dataset_name="example"):
df_ref = pd.read_csv("data/{}/refs_edges_0.txt".format(dataset_name), header=None, names=["e"])
df_ref = df_ref["e"].str.split('\t', 1, expand=T... | mit |
AntonSax/plantcv | setup.py | 1 | 4569 | """A setuptools based setup module.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject
"""
import sys
import setuptools
from setuptools.command.test import test as TestCommand
from codecs import open
from os import path
class PyTest(TestCommand):
def initialize_o... | mit |
Johanu/MDAnalysis_scripts | coil_COMdistance.py | 1 | 4687 | from __future__ import division
import matplotlib.pyplot as plt
import MDAnalysis as md
import numpy as np
def calculate_dists(gro_file, xtc_file):
u = md.Universe(gro_file, xtc_file)
select_group1 = u.selectAtoms("backbone and (resnum 50 or resnum 51)")
select_group2 = u.selectAtoms("backbone and (resnum ... | mit |
ilo10/scikit-learn | sklearn/feature_extraction/tests/test_feature_hasher.py | 258 | 2861 | from __future__ import unicode_literals
import numpy as np
from sklearn.feature_extraction import FeatureHasher
from nose.tools import assert_raises, assert_true
from numpy.testing import assert_array_equal, assert_equal
def test_feature_hasher_dicts():
h = FeatureHasher(n_features=16)
assert_equal("dict",... | bsd-3-clause |
matnel/hs-comments-visu | app/main.py | 1 | 2374 | from flask import *
import collect_hs
import collections
import nltk
import numpy
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
app = Flask(__name__)
## common constructs
stem = nltk.stem.snowball.SnowballStemmer('finnish')
stopwords = map... | mit |
numenta/htmresearch | projects/sequence_prediction/mackey_glass/visualize_results.py | 13 | 1819 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | agpl-3.0 |
scubamut/trading-with-python | lib/bats.py | 78 | 3458 | #-------------------------------------------------------------------------------
# Name: BATS
# Purpose: get data from BATS exchange
#
# Author: jev
#
# Created: 17/08/2013
# Copyright: (c) Jev Kuznetsov 2013
# Licence: BSD
#------------------------------------------------------------... | bsd-3-clause |
donkirkby/live-py-plugin | docs/lessons/lesson09_tick_background.py | 1 | 2343 | """
The tick labels are now hardly visible because of the blue and
red lines. We can make them bigger and we can also adjust their
properties such that they'll be rendered on a semi-transparent
white background. This will allow us to see both the data and
the labels. We can also get rid of one of the zero labels.
:less... | mit |
jbrambleDC/sklearn_pydata2015 | notebooks/fig_code/svm_gui.py | 47 | 11549 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
saebrahimi/Emotion-Recognition-RNN | Audio/conf_mat_plot.py | 2 | 1458 | from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import random
def main():
true_labels = [random.randint(1, 10) for i in range(100)]
predicted_labels = [random.randint(1, 10) for i in range(100)]
plot = getConfusionMatrixPlot(true_labels, predicted_labels)
plot.show()
def g... | mit |
ryfeus/lambda-packs | LightGBM_sklearn_scipy_numpy/source/numpy/linalg/linalg.py | 3 | 80437 | """Lite version of scipy.linalg.
Notes
-----
This module is a lite version of the linalg.py module in SciPy which
contains high-level Python interface to the LAPACK library. The lite
version only accesses the following LAPACK functions: dgesv, zgesv,
dgeev, zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetr... | mit |
inspirehep/beard-server | beard_server/modules/predictor/arxiv.py | 2 | 8502 | # -*- coding: utf-8 -*-
#
# This file is part of Inspire.
# Copyright (C) 2016 CERN.
#
# Inspire is free software; you can redistribute it
# and/or modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 2 of the
# License, or (at your option) any later... | gpl-2.0 |
rohanp/scikit-learn | examples/linear_model/plot_ols.py | 220 | 1940 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
Djabbz/scikit-learn | sklearn/cluster/bicluster.py | 211 | 19443 | """Spectral biclustering algorithms.
Authors : Kemal Eren
License: BSD 3 clause
"""
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import dia_matrix
from scipy.sparse import issparse
from . import KMeans, MiniBatchKMeans
from ..base import BaseEstimator, BiclusterMixin
from ..external... | bsd-3-clause |
KristofferC/FeynSimul | examples/benchmark_xorshift_vs_ranlux.py | 1 | 7136 | import numpy as np
import pylab as pl
from matplotlib import rc
from matplotlib.ticker import *
import matplotlib.pyplot as plt
logplot = True # Log the x axis or not
showdouble = False
#xor = np.array([0.000835, 0.021259, 0.077566, 2.113641, 7.748644, 213.113910]) #xorshift single
xor = np.array([0.000835, 0.0212... | gpl-3.0 |
mohamed--abdel-maksoud/chromium.src | chrome/test/nacl_test_injection/buildbot_chrome_nacl_stage.py | 12 | 11594 | #!/usr/bin/python
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Do all the steps required to build and test against nacl."""
import optparse
import os.path
import re
import shutil
import subproc... | bsd-3-clause |
fashandge/deja | src/experiment/utilities.py | 1 | 31043 | import os
import re
import itertools
import time
import datetime
import string
import random
import nltk
import toolz
from nltk.corpus import wordnet as wn
#from nltk.corpus import verbnet as vn
#from nltk.corpus import framenet as fn
from nltk.stem import WordNetLemmatizer
from nltk.tag.stanford import POSTagger
from ... | mit |
ephes/scikit-learn | sklearn/svm/tests/test_bounds.py | 280 | 2541 | import nose
from nose.tools import assert_equal, assert_true
from sklearn.utils.testing import clean_warning_registry
import warnings
import numpy as np
from scipy import sparse as sp
from sklearn.svm.bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model.logistic import LogisticRegression... | bsd-3-clause |
yousrabk/mne-python | mne/decoding/time_gen.py | 1 | 51426 | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Clement Moutard <clement.moutard@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import copy
from ..io.pick import pick_... | bsd-3-clause |
wesm/statsmodels | scikits/statsmodels/tools/datautils.py | 1 | 2104 | import os
import time
import numpy as np
from numpy import genfromtxt, array
class Dataset(dict):
def __init__(self, **kw):
dict.__init__(self,kw)
self.__dict__ = self
# Some datasets have string variables. If you want a raw_data attribute you
# must create this in the dataset's load function.
... | bsd-3-clause |
DSLituiev/scikit-learn | sklearn/utils/tests/test_random.py | 85 | 7349 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from scipy.misc import comb as combinations
from numpy.testing import assert_array_almost_equal
from sklearn.utils.random import sample_without_replacement
from sklearn.utils.random import random_choice_csc
from sklearn.utils.testing import ... | bsd-3-clause |
chrissly31415/amimanera | competition_scripts/higgs.py | 1 | 45715 | #!/usr/bin/python
# coding: utf-8
import numpy as np
import pandas as pd
import sklearn as sl
import random
import math
from qsprLib import *
import inspect
import pickle
from pandas.tools.plotting import scatter_matrix
from xgboost_sklearn import *
def createFeatures(X_all,keepAll=True,createNAFeats='all'):
"... | lgpl-3.0 |
terrycojones/dark-matter | dark/proteins.py | 1 | 43815 | from __future__ import division, print_function
import os
from collections import defaultdict, Counter
import numpy as np
from os.path import dirname, exists, join
from operator import itemgetter
import re
from six.moves.urllib.parse import quote
from textwrap import fill
from dark.dimension import dimensionalIterato... | mit |
lbishal/scikit-learn | sklearn/tree/tree.py | 23 | 40423 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
aolindahl/polarization-monitor | offline_viewer.py | 2 | 9811 | # -*- coding: utf-8 -*-
"""
Created on Wed Apr 22 15:32:02 2015
@author: Anton O Lindahl
"""
import h5py
import numpy as np
import matplotlib.pyplot as plt
import lmfit
import sys
import os.path
sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
'/aolPyModules')
import cookie_box
from Burnin... | gpl-2.0 |
dipanjanS/text-analytics-with-python | New-Second-Edition/Ch05 - Text Classification/model_evaluation_utils.py | 2 | 9263 | # -*- coding: utf-8 -*-
"""
Created on Mon Jul 31 20:05:23 2017
@author: DIP
@Copyright: Dipanjan Sarkar
"""
from sklearn import metrics
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.base import clone
from sklearn.preprocessing impor... | apache-2.0 |
0todd0000/spm1d | spm1d/examples/stats0d/ex_ci_pairedsample.py | 1 | 1479 |
import numpy as np
from matplotlib import pyplot
import spm1d
#(0) Load dataset:
dataset = spm1d.data.uv0d.cipaired.FraminghamSystolicBloodPressure()
yA,yB = dataset.get_data()
print( dataset )
# yB += 1.9
#(1) Compute confidence intervals:
alpha = 0.05
mu = 0
ci0 = spm1d.stats.ci_pairedsample(yA, y... | gpl-3.0 |
zehpunktbarron/iOSMAnalyzer | scripts/c3_highway_actuality.py | 1 | 6584 | # -*- coding: utf-8 -*-
#!/usr/bin/python2.7
#description :This file creates a plot: Calculates the actuality of the total OSM highway. Additionally plots the first version for comparison purposes
#author :Christopher Barron @ http://giscience.uni-hd.de/
#date :19.01.2013
#version :0.1
... | gpl-3.0 |
PanDAWMS/panda-jedi | pandajedi/jeditest/addTestTask.py | 1 | 3246 | import sys
import uuid
from pandajedi.jedicore.JediTaskBufferInterface import JediTaskBufferInterface
from pandajedi.jedicore.JediTaskSpec import JediTaskSpec
from pandajedi.jedicore.JediDatasetSpec import JediDatasetSpec
tbIF = JediTaskBufferInterface()
tbIF.setupInterface()
task = JediTaskSpec()
task.jediTaskID =... | apache-2.0 |
torebutlin/cued_datalogger | setup.py | 1 | 7651 | from setuptools import setup
import sys
import subprocess
from os.path import isfile
import urllib.request
import traceback
import pip
def version():
"""Get version number"""
with open('cued_datalogger/VERSION') as f:
return f.read()
def readme():
"""Get text from the README.rst"""
with open('... | bsd-3-clause |
stkubr/zipline | zipline/utils/munge.py | 29 | 2299 | #
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
tapomayukh/projects_in_python | classification/Classification_with_HMM/Multiple_Contact_Classification/continuous_hmm_prediction_two_objects_force_motion_10_states.py | 1 | 10741 |
# Hidden Markov Model Implementation
import pylab as pyl
import numpy as np
import matplotlib.pyplot as pp
#from enthought.mayavi import mlab
import scipy as scp
import scipy.ndimage as ni
import roslib; roslib.load_manifest('sandbox_tapo_darpa_m3')
import rospy
#import hrl_lib.mayavi2_util as mu
import hrl_lib.viz... | mit |
mtat76/atm-py | build/lib/atmPy/for_removal/LAS/LAS.py | 6 | 8188 | # -*- coding: utf-8 -*-
"""
Created on Mon Nov 10 11:43:10 2014
@author: htelg
"""
import datetime
import warnings
import numpy as np
import pandas as pd
import pylab as plt
from StringIO import StringIO as io
from scipy.interpolate import UnivariateSpline
from atmPy.aerosols.size_distr import sizedistribution
de... | mit |
DonBeo/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
a... | bsd-3-clause |
drakipovic/deep-learning | 1. labos/fcann.py | 1 | 1825 | import numpy as np
from sklearn.preprocessing import OneHotEncoder
import matplotlib.pyplot as plt
from data import sample_gmm_2d, eval_perf_binary, graph_data, graph_surface
def forward(X, w_1, w_2, b_1, b_2):
s_1 = np.dot(X, w_1) + b_1
h_1 = np.maximum(s_1, np.zeros(s_1.shape))
s_2 = np.dot(h_1, w... | mit |
MartinThoma/write-math | tools/evaluate_preprocessing_algorithms.py | 1 | 8689 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Find outliers in the dataset."""
import pymysql.cursors
from copy import deepcopy
from hwrt.handwritten_data import HandwrittenData
from hwrt import preprocessing
from hwrt import utils
class HandwrittenDataM(HandwrittenData):
"""A modified version of Handwritte... | mit |
girone/hatmaker | cluster.py | 1 | 2450 | import csv
import sys
import pprint
from collections import defaultdict
from sklearn.cluster import KMeans
import numpy as np
import pandas as pd
from format_data import audit_experience, audit_height, audit_fitness, audit_throwing
def extract_fields(line_dict):
return (line_dict["name"],
audit_experi... | mit |
runt18/nupic | examples/audiostream/audiostream_tp.py | 32 | 9991 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | agpl-3.0 |
zerothi/siesta-es | sids/es/hamiltonian.py | 1 | 3350 | """
Electronic structure code which handles the Hamiltonian
"""
import numpy as _np
import sids.helper.units as _unit
# Import scipy linear algebra routines
import scipy.linalg as _dlin
import scipy.sparse.linalg as _slin
class Hamiltonian(object):
"""
Object for retaining information about the Hamiltonian ma... | gpl-3.0 |
tschaume/pymatgen | pymatgen/phonon/plotter.py | 2 | 23775 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
import logging
from collections import OrderedDict, namedtuple
import numpy as np
import scipy.constants as const
from monty.json import jsanitize
from pymatgen.phonon.bandstructure import PhononBandStructure... | mit |
ioos/system-test | Theme_1_Baseline/Scenario_1B_CoreVariable_Strings/Scenario_1B_CoreVariable_Strings.py | 2 | 7535 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <codecell>
from utilities import css_styles
css_styles()
# <markdowncell>
# # IOOS System Test - Theme 1 - Scenario B - [Description](https://github.com/ioos/system-test/wiki/Development-of-Test-Themes#theme-1-baseline-assessment)
#
# ## Core Variable Strings
# ... | unlicense |
mrbean-bremen/pyfakefs | pyfakefs/patched_packages.py | 2 | 4115 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under t... | apache-2.0 |
YinongLong/scikit-learn | sklearn/gaussian_process/gpc.py | 42 | 31571 | """Gaussian processes classification."""
# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
from operator import itemgetter
import numpy as np
from scipy.linalg import cholesky, cho_solve, solve
from scipy.optimize import fmin_l_bfgs_b
from scipy.special import erf... | bsd-3-clause |
chongguang/scikitLearning | c2-housePrice.py | 1 | 2829 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
boston = load_boston()
print boston.data.shape
print boston.feature_names
print np.max(boston.target), np.min(boston.target), np.mean(boston.target)
#print boston.DESCR
from sklearn.cross_validation import train_test_split
X_... | mit |
SteveNguyen/QM_OptimalControl | plot.py | 1 | 3168 | #!/usr/bin/python
# -*- coding: utf-8 -*-
########################################################################
# File Name : 'plot_v_a.py'
# Author : Steve NGUYEN
# Contact : steve.nguyen@college-de-france.fr
# Created : mercredi, septembre 14 2011
# Revised :
# Version :
# Target MCU :
#
# This c... | gpl-2.0 |
DTMilodowski/LiDAR_canopy | src/BALI_synthesis_contribution/canopy_profiles_oilpalm.py | 1 | 6736 | ###############################################################################################################
# This driver function analyses the sensisitivity of the LiDAR-based metrics to spatial scale and point density
################################################################################################... | gpl-3.0 |
joewledger/Green_Labs_Plotting | package/plotting/implemented_plotters.py | 1 | 15309 | from collections import *
from PyQt5.QtGui import QColor
from package.utils import param_utils
from package.plotting import generic_plotters as gen_plt
from PyQt5.QtCore import *
import pandas as pd
import numpy as np
class Plotter():
subset_functions = OrderedDict([(lambda hdc: hdc, "Entire Study Period"),
... | lgpl-3.0 |
chakpongchung/RIPS_2014_BGI_source_code | scikit_code/classifier_with_multi_components.py | 1 | 3607 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import sys
import csv
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm impor... | mit |
lseman/pylspm | pylspm/blindfolding.py | 1 | 2545 | # CHIN, W. W. How to Write Up and Report PLS Analyses. In: Handbook of
# Partial Least Squares. Berlin, Heidelberg: Springer Berlin Heidelberg,
# 2010. p. 655–690.
import pandas
import numpy as np
from numpy import inf
import pandas as pd
from .pylspm import PyLSpm
from .boot import PyLSboot
def isNaN(... | mit |
LiuVII/Self-driving-RC-car | drive4.py | 1 | 10712 | from __future__ import print_function
import pygame
import os, sys, time, shutil
from datetime import datetime
import select
import argparse
import urllib2
import subprocess
import cv2
import numpy as np, pandas as pd
from PIL import ImageOps
from PIL import Image
from train4 import process_image, model
import logging
... | mit |
jorik041/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 230 | 2649 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
Lucas-Armand/genetic-algorithm | dev/9ºSemana/3DsequenceShip.py | 1 | 5268 | # -*- coding: utf-8 -*-
import os
import math
import csv
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from itertools import product, combinations
import matplotlib.animation as animation
def csv_read(name): #Metodo de leitura, transforma um arquivo CSV em um vetor
... | gpl-3.0 |
jondo/shogun | examples/undocumented/python_static/graphical/util.py | 22 | 1225 | """ Utilities for matplotlib examples """
import pylab
from numpy import ones, array, meshgrid, linspace, concatenate, ravel, min, max
from numpy.random import randn
QUITKEY='q'
NUM_EXAMPLES=200
DISTANCE=2
def quit (event):
if event.key==QUITKEY or event.key==QUITKEY.upper():
pylab.close()
def set_title (title):... | gpl-3.0 |
ondrejch/MSBR-ORNL-4528 | scripts/analyze-lattices/kmax.py | 1 | 3249 | #!/usr/bin/python3
#
# Analysis module for MSBR lattice collection. Finds the highest KEFF(l,sf) for given CR
# Ondrej Chvala, ochvala@utk.edu
# 2016-07-16
# GNU/GPL
#
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.CloughTocher2DInterpolator.html
import numpy as np
import matplotlib.pyplot a... | gpl-2.0 |
wndhydrnt/airflow | airflow/hooks/base_hook.py | 3 | 3216 | # -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
#... | apache-2.0 |
gfetterman/bark | bark/tools/datenvclassify.py | 2 | 5284 | import bark
import numpy as np
import pandas as pd
import os.path
from scipy.signal import filtfilt, butter
from scipy.io import wavfile
def abs_and_smooth(x, sr, lp=100):
abs_x = np.abs(x)
if len(x.shape) > 1:
abs_x = np.sum(abs_x,
axis=-1) # sum over last dimension eg sum ove... | gpl-2.0 |
potash/scikit-learn | sklearn/naive_bayes.py | 26 | 30641 | # -*- coding: utf-8 -*-
"""
The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
are supervised learning methods based on applying Bayes' theorem with strong
(naive) feature independence assumptions.
"""
# Author: Vincent Michel <vincent.michel@inria.fr>
# Minor fixes by Fabian Pedre... | bsd-3-clause |
nicjhan/MOM6-examples | tools/analysis/meridional_overturning.py | 4 | 6978 | #!/usr/bin/env python
import io
import netCDF4
import numpy
import m6plot
import m6toolbox
import matplotlib.pyplot as plt
import os
import sys
def run():
try: import argparse
except: raise Exception('This version of python is not new enough. python 2.7 or newer is required.')
parser = argparse.ArgumentParser(d... | gpl-3.0 |
fspaolo/scikit-learn | examples/cluster/plot_digits_agglomeration.py | 7 | 1651 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Feature agglomeration
=========================================================
These images how similar features are merged together using
feature agglomeration.
"""
print(__doc__)
# Code source: Gaël Varoquaux
#... | bsd-3-clause |
mehdidc/scikit-learn | sklearn/tests/test_calibration.py | 2 | 11711 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
... | bsd-3-clause |
silky/sms-tools | lectures/03-Fourier-properties/plots-code/fft-zero-phase.py | 24 | 1140 | import matplotlib.pyplot as plt
import numpy as np
from scipy.fftpack import fft, fftshift
import sys
sys.path.append('../../../software/models/')
import utilFunctions as UF
(fs, x) = UF.wavread('../../../sounds/oboe-A4.wav')
N = 512
M = 401
hN = N/2
hM = (M+1)/2
start = .8*fs
xw = x[start-hM:start+hM-1] * np.h... | agpl-3.0 |
JosePedroMatos/Tethys | gpu/functions.py | 1 | 9061 | '''
Created on 10 nov. 2016
@author: José Pedro Matos
'''
import time
import warnings
import numpy as np
import matplotlib.pyplot as plt
import mpld3
from mpld3 import utils, plugins
from queue import Empty
from scipy.interpolate.interpolate import interp1d
def plot(queue):
fig = plt.figure(figsi... | mit |
xiaoxiamii/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 216 | 13290 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises_regexp
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
f... | bsd-3-clause |
lotrus28/TaboCom | linear_model/model_test/random_forest/create_som_visualisation.py | 1 | 3687 | from mvpa2.suite import *
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
# /usr/local/lib/python2.7/dist-packages/
sz = (10, 10)
p_agp = '/media/theo/Files/Lab/FHM/Complement/cross_valid_AGP_PRJEB/agp/'
p_prjeb = '/media/theo/Files/Lab/FHM/Complement/cross_valid_AGP_PRJEB/prjeb/... | apache-2.0 |
hsiaoyi0504/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
vortex-ape/scikit-learn | examples/feature_selection/plot_select_from_model_boston.py | 17 | 1531 | """
===================================================
Feature selection using SelectFromModel and LassoCV
===================================================
Use SelectFromModel meta-transformer along with Lasso to select the best
couple of features from the Boston dataset.
"""
# Author: Manoj Kumar <mks542@nyu.edu>... | bsd-3-clause |
GuessWhoSamFoo/pandas | pandas/core/computation/expr.py | 2 | 26830 | """:func:`~pandas.eval` parsers
"""
import ast
from functools import partial
import tokenize
import numpy as np
from pandas.compat import StringIO, lmap, reduce, string_types, zip
import pandas as pd
from pandas import compat
from pandas.core import common as com
from pandas.core.base import StringMixin
from pandas... | bsd-3-clause |
vibhorag/scikit-learn | examples/decomposition/plot_sparse_coding.py | 247 | 3846 | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` esti... | bsd-3-clause |
jorge2703/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 242 | 5885 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
sinhrks/scikit-learn | examples/missing_values.py | 71 | 3055 | """
======================================================
Imputing missing values before building an estimator
======================================================
This example shows that imputing the missing values can give better results
than discarding the samples containing any missing value.
Imputing does not ... | bsd-3-clause |
acapet/GHER-POSTPROC | Examples/MonthlyMapsBottomAge_2Ddist.py | 1 | 3445 | import numpy as np
import numpy.ma as ma
from netCDF4 import Dataset
#from mpl_toolkits.basemap import Basemap
#from multiprocessing import Pool
#import gsw ... | gpl-3.0 |
tjduigna/exatomic | exatomic/core/editor.py | 3 | 2918 | # -*- coding: utf-8 -*-
# Copyright (c) 2015-2018, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
Atomic Editor
###################
This module provides a text file editor that can be used to transform commonly
found file formats directly into :class:`~exatomic.container.Univ... | apache-2.0 |
dmnfarrell/mirnaseq | smallrnaseq/analysis.py | 2 | 12574 | #!/usr/bin/env python
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that ... | gpl-3.0 |
TimothyTickle/quickplots | histogram.py | 1 | 2009 | #!/usr/bin/env python
__author__ = "Timothy Tickle"
__copyright__ = "Copyright 2014"
__credits__ = [ "Timothy Tickle" ]
__license__ = "MIT"
__maintainer__ = "Timothy Tickle"
__email__ = "ttickle@broadinstitute.org"
__status__ = "Development"
import matplotlib.pyplot as plt
import quickPlot as qp
class Histogram( q... | mit |
AnasGhrab/scikit-learn | doc/sphinxext/gen_rst.py | 142 | 40026 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
i... | bsd-3-clause |
Technologicat/python-wlsqm | examples/expertsolver_example.py | 1 | 6402 | # -*- coding: utf-8 -*-
"""A minimal usage example for ExpertSolver.
JJ 2017-03-28
"""
from __future__ import division, print_function, absolute_import
import numpy as np
import scipy.spatial.ckdtree
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d
import wlsqm
def project_onto_regular_grid_2D... | bsd-2-clause |
dtorresf/TheGrapher | Port.py | 1 | 1049 | import Methods
import pandas as pd
class Port:
'''Class that represents a Port from a Switch with atributtes to graph
'''
def __init__(self):
self.name = ''
self.switchname = ''
self.rx = pd.DataFrame()
self.tx = pd.DataFrame()
def meanrx(self):
return self.rx.mean()
def meantx(self):
return self.tx.... | gpl-3.0 |
xubenben/scikit-learn | sklearn/linear_model/tests/test_theil_sen.py | 234 | 9928 | """
Testing for Theil-Sen module (sklearn.linear_model.theil_sen)
"""
# Author: Florian Wilhelm <florian.wilhelm@gmail.com>
# License: BSD 3 clause
from __future__ import division, print_function, absolute_import
import os
import sys
from contextlib import contextmanager
import numpy as np
from numpy.testing import ... | bsd-3-clause |
lbillingham/commit_opener | commit_opener/commit_opener.py | 1 | 2151 | # -*- coding: utf-8 -*-
import click
import os
import pandas as pd
from shutil import rmtree
from . tree_scrape import author_minded
from . query_pmc import pmc_data
OUT_SUBFOLDER = 'contrib_data'
AUTHOR_DATA = 'author_data.json'
def verify_local_repo_location(repo):
if not os.path.isdir(repo):
raise IOE... | gpl-3.0 |
minimumcut/UnnamedEngine | UnnamedEngine/Vendor/bullet/examples/pybullet/testrender.py | 3 | 1357 | import numpy as np
import matplotlib.pyplot as plt
import pybullet
pybullet.connect(pybullet.GUI)
pybullet.loadURDF("r2d2.urdf")
camTargetPos = [0.,0.,0.]
cameraUp = [0,0,1]
cameraPos = [1,1,1]
yaw = 40
pitch = 10.0
roll=0
upAxisIndex = 2
camDistance = 4
pixelWidth = 320
pixelHeight = 240
nearPlane = 0.01
farPlane =... | mit |
ericmckean/syzygy | syzygy/build/create_virtualenv.py | 8 | 5865 | # Copyright 2012 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | apache-2.0 |
RayMick/scikit-learn | sklearn/metrics/tests/test_ranking.py | 127 | 40813 | from __future__ import division, print_function
import numpy as np
from itertools import product
import warnings
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn import svm
from sklearn import ensemble
from sklearn.datasets import make_multilabel_classification
from sklearn.random_projec... | bsd-3-clause |
roxyboy/scikit-learn | examples/classification/plot_lda_qda.py | 164 | 4806 | """
====================================================================
Linear and Quadratic Discriminant Analysis with confidence ellipsoid
====================================================================
Plot the confidence ellipsoids of each class and decision boundary
"""
print(__doc__)
from scipy import lin... | bsd-3-clause |
ces0712/mtools | mtools/mplotqueries/plottypes/range_type.py | 7 | 3323 | from mtools.mplotqueries.plottypes.base_type import BasePlotType
from datetime import timedelta
import argparse
try:
from matplotlib.dates import date2num, num2date
except ImportError:
raise ImportError("Can't import matplotlib. See https://github.com/rueckstiess/mtools/blob/master/INSTALL.md for \
ins... | apache-2.0 |
carrillo/scikit-learn | sklearn/linear_model/coordinate_descent.py | 59 | 76336 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Gael Varoquaux <gael.varoquaux@inria.fr>
#
# License: BSD 3 clause
import sys
import warnings
from abc import ABCMeta, abstractmethod
import n... | bsd-3-clause |
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