repo_name stringlengths 9 55 | path stringlengths 7 120 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 169k | license stringclasses 12
values |
|---|---|---|---|---|---|
joshbohde/scikit-learn | examples/plot_permutation_test_for_classification.py | 2 | 2049 | """
=================================================================
Test with permutations the significance of a classification score
=================================================================
In order to test if a classification score is significative a technique
in repeating the classification procedure aft... | bsd-3-clause |
CG-F16-24-Rutgers/steersuite-rutgers | steerstats/tools/plotting/plotMultiObjectiveData.py | 8 | 1340 |
import csv
import matplotlib.pyplot as plt
import sys
import numpy as np
# filename = '../../data/optimization/sf/multiObjective/SteerStatsOpt2.csv'
filename = sys.argv[1]
xs = []
ys = []
if len(sys.argv) == 2:
csvfile = open(filename, 'r')
spamreader = csv.reader(csvfile, delimiter=',')
xs = []
ys =... | gpl-3.0 |
maxlikely/scikit-learn | sklearn/pipeline.py | 1 | 13051 | """
The :mod:`sklearn.pipeline` module implements utilites to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Licence: BSD
import numpy as np
from scipy import sparse
from .base impo... | bsd-3-clause |
martinggww/lucasenlights | MachineLearning/python_tutorial/KNearestNeighborhood.py | 1 | 1274 | '''
Classification algorithm
Create a model that seperate a dataset
proximity probability nearest neighbors
What the hack is K?
if K=2, find the closet 2 points
We want K = odd numbers, K=3, 5, 7...
'''
'''
- - +, 66.7% confidence, confidence, accuracy
Euclid distance, euclid distance middle point
Dataset and the rela... | cc0-1.0 |
treycausey/scikit-learn | sklearn/feature_selection/__init__.py | 244 | 1088 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | bsd-3-clause |
chenyyx/scikit-learn-doc-zh | examples/zh/cluster/plot_dict_face_patches.py | 9 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | gpl-3.0 |
jrcapriles/gameSimulator | gameSimulator.py | 1 | 6837 | # -*- coding: utf-8 -*-
"""
Created on Thu Sep 18 19:27:57 2014
@author: joser
"""
import pygame, ode, random, Buttons
from math import atan2, acos, asin, sin, cos
import matplotlib.pyplot as plt
from pygame.locals import *
from numpy import *
from Point import *
from Buttons import *
class gameSimulator( object ... | mit |
stevenzhang18/Indeed-Flask | lib/pandas/tests/test_expressions.py | 9 | 16557 | # -*- coding: utf-8 -*-
from __future__ import print_function
# pylint: disable-msg=W0612,E1101
import nose
import re
from numpy.random import randn
import operator
import numpy as np
from pandas.core.api import DataFrame, Panel
from pandas.computation import expressions as expr
from pandas import compat
from pand... | apache-2.0 |
drusk/pml | pml/unsupervised/clustering.py | 1 | 11112 | # Copyright (C) 2012 David Rusk
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distr... | mit |
r39132/airflow | tests/contrib/operators/test_hive_to_dynamodb_operator.py | 7 | 5053 | # -*- 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 |
jiangzhonglian/MachineLearning | src/py2.x/ml/6.SVM/svm-complete_Non-Kernel.py | 1 | 13440 | #!/usr/bin/python
# coding:utf8
"""
Created on Nov 4, 2010
Update on 2017-05-18
Chapter 5 source file for Machine Learing in Action
Author: Peter/geekidentity/片刻
GitHub: https://github.com/apachecn/AiLearning
"""
from __future__ import print_function
from numpy import *
import matplotlib.pyplot as plt
class optStruc... | gpl-3.0 |
Titan-C/sympy | sympy/physics/quantum/circuitplot.py | 6 | 12937 | """Matplotlib based plotting of quantum circuits.
Todo:
* Optimize printing of large circuits.
* Get this to work with single gates.
* Do a better job checking the form of circuits to make sure it is a Mul of
Gates.
* Get multi-target gates plotting.
* Get initial and final states to plot.
* Get measurements to plo... | bsd-3-clause |
LiaoPan/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
joernhees/scikit-learn | sklearn/ensemble/weight_boosting.py | 29 | 41090 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
NolanBecker/aima-python | grading/neuralNet-submissions.py | 4 | 2217 | import importlib
import traceback
from grading.util import roster, print_table
# from logic import FolKB
# from utils import expr
import os
from sklearn.neural_network import MLPClassifier
mlpc = MLPClassifier()
def indent(howMuch = 1):
space = ' '
for i in range(1, howMuch):
space += ' '
return s... | mit |
guillemborrell/gtable | tests/test_table_creation.py | 1 | 4044 | from gtable import Table
import numpy as np
import pandas as pd
def test_empty_table():
t = Table()
assert t.data == []
def test_simple_table():
t = Table({'a': [1, 2, 3], 'b': np.array([4, 5, 6])})
assert t.to_dict()['a'][2] == 3
assert np.all(t.index == np.ones((2, 3), dtype=np.uint8))
... | bsd-3-clause |
sysid/nbs | ml_old/Prognose/Evaluator.py | 1 | 3134 | from twBase import * # NOQA
from pandas import DataFrame
from pandas import read_csv
from pandas import datetime
from sklearn.preprocessing import MinMaxScaler
# date-time parsing function for loading the dataset
def parser(x):
return datetime.strptime('190'+x, '%Y-%m')
def get_time():
return time.strftime(... | mit |
jjo31/ATHAM-Fluidity | tests/gls-Kato_Phillips-mixed_layer_depth/mixed_layer_depth_all.py | 4 | 4600 | #!/usr/bin/env python
from numpy import arange,concatenate,array,argsort
import os
import sys
import vtktools
import math
from pylab import *
from matplotlib.ticker import MaxNLocator
import re
from scipy.interpolate import UnivariateSpline
import glob
#### taken from http://www.codinghorror.com/blog/archives/001018... | lgpl-2.1 |
OpenSourcePolicyCenter/multi-country | Python/Archive/Stage4/AuxiliaryClass.py | 2 | 116307 | from __future__ import division
import csv
import time
import numpy as np
import scipy as sp
import scipy.optimize as opt
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import AuxiliaryDemographics as demog
#from pure_cython import cy_fillca
class OLG(object):
"""
This object... | mit |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/pandas/tests/indexes/datetimes/test_tools.py | 6 | 62998 | """ test to_datetime """
import sys
import pytest
import locale
import calendar
import numpy as np
from datetime import datetime, date, time
from distutils.version import LooseVersion
import pandas as pd
from pandas._libs import tslib, lib
from pandas.core.tools import datetimes as tools
from pandas.core.tools.dateti... | mit |
deepesch/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 297 | 8265 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
lucidjuvenal/quis-custodiet | twitter_feed/twittest.py | 1 | 1922 | import twitter # python-twitter package
from matplotlib.pyplot import pause
import re
############################################
# secret data kept in separate file
with open('twitdat.txt') as f:
fromFile = {}
for line in f:
line = line.split() # to skip blank lines
if len(line)==3 : #
... | gpl-3.0 |
rustychris/stompy | stompy/plot/plot_utils.py | 1 | 45515 | from __future__ import division
from __future__ import print_function
from builtins import str
from builtins import zip
from builtins import range
from builtins import object
import time
from matplotlib.collections import LineCollection
from matplotlib.transforms import Transform,Affine2D
import matplotlib.transforms ... | mit |
davidsoncasey/quiver-server | plot_equation.py | 1 | 3888 | from __future__ import division
import re
from math import sqrt
import multiprocessing
import Queue
import sympy
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
class DiffEquation(object):
'''
Class that contains equation informa... | mit |
shernshiou/CarND | Term1/05-CarND-Vehicle-Detection/vehicle_detection.py | 1 | 3518 | import glob
import cv2
import numpy as np
import os
from util.draw import generate_sliding_windows
from util.draw import extract_heatmap
from util.classifier import svm_classifier
from util.classifier import transform_features
from sklearn.preprocessing import StandardScaler
from moviepy.editor import VideoFileClip
he... | mit |
vibhorag/scikit-learn | sklearn/metrics/setup.py | 299 | 1024 | import os
import os.path
import numpy
from numpy.distutils.misc_util import Configuration
from sklearn._build_utils import get_blas_info
def configuration(parent_package="", top_path=None):
config = Configuration("metrics", parent_package, top_path)
cblas_libs, blas_info = get_blas_info()
if os.name ==... | bsd-3-clause |
BhallaLab/moose-full | moose-examples/snippets/MULTI/minchan.py | 3 | 12176 | # minimal.py ---
# Upi Bhalla, NCBS Bangalore 2014.
#
# Commentary:
#
# Minimal model for loading rdesigneur: reac-diff elec signaling in neurons
#
# 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 Founda... | gpl-2.0 |
pavelchristof/gomoku-ai | tensorflow/contrib/labeled_tensor/python/ops/ops.py | 77 | 46403 | # Copyright 2016 The TensorFlow Authors. 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 applica... | apache-2.0 |
StongeEtienne/dipy | dipy/data/__init__.py | 1 | 12766 | """
Read test or example data
"""
from __future__ import division, print_function, absolute_import
import sys
import json
from nibabel import load
from os.path import join as pjoin, dirname
import gzip
import numpy as np
from dipy.core.gradients import GradientTable, gradient_table
from dipy.core.sphere import Sphe... | bsd-3-clause |
McIntyre-Lab/papers | fear_sem_sd_2015/scripts/dspr_gene_ggm_neighborhood_analysis.py | 1 | 4687 | #!/usr/bin/env python
import os
import logging
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import pickle
def setLogger(fname,loglevel):
""" Function to handle error logging """
logging.basicConfig(filename=fname, filemode='w', level=loglevel, format='%(asctime)s - %(levelname)s - ... | lgpl-3.0 |
jlegendary/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 |
liangz0707/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 |
bmazin/SDR | Projects/NewDataPacket/test_packet.py | 1 | 2865 | #!/bin/usr/python
import numpy as np
import matplotlib.pyplot as plt
import struct
import sys
from bin import *
bin_data_0=str(np.load('bin_data_0.npy'))
bin_data_1=str(np.load('bin_data_1.npy'))
phase_timestream=np.loadtxt('phase_timestream.txt')
bin_max=len(bin_data_1)/4
addr0=969
addr1=3132
median = -0.04140515... | gpl-2.0 |
ychfan/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 72 | 12865 | # Copyright 2016 The TensorFlow Authors. 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 applica... | apache-2.0 |
giorgiop/scikit-learn | examples/semi_supervised/plot_label_propagation_structure.py | 55 | 2433 | """
==============================================
Label Propagation learning a complex structure
==============================================
Example of LabelPropagation learning a complex internal structure
to demonstrate "manifold learning". The outer circle should be
labeled "red" and the inner circle "blue". Be... | bsd-3-clause |
muthujothi/CrowdAnalytix-CrimeRates-PredictiveModelling | selectFeatures.py | 1 | 1038 | import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
import csv
from scipy.stats.stats import pearsonr
from collections import OrderedDict
from collections import defaultdict
#Load the train data
df_1 = pd.read_csv('C:/Pst Files/CrowdAnalytix/CrimeRates/CA_Crime_Rate_... | mit |
schae234/gingivere | tests/lr.py | 2 | 1543 | import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import StratifiedKFold
import numpy as np
from sklearn.metrics import classification_report
from sklearn.metrics import roc_auc_score
store = pd.HDFStore("D:/gingive... | mit |
huazhisong/graduate_text | src/contrib_cnn/cnn_shallow.py | 1 | 10681 | # Copyright 2016 The TensorFlow Authors. 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... | agpl-3.0 |
ruchee/vimrc | vimfiles/bundle/vim-python/submodules/pydocstyle/src/tests/test_cases/canonical_numpy_examples.py | 3 | 5315 | """This is the docstring for the example.py module. Modules names should
have short, all-lowercase names. The module name may have underscores if
this improves readability.
Every module should have a docstring at the very top of the file. The
module's docstring may extend over multiple lines. If your docstring doe... | mit |
magicrub/MissionPlanner | Lib/site-packages/numpy/fft/fftpack.py | 59 | 39653 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1)
ifft(a, n=None, axis=-1)
rfft(a, n=None, axis=-1)
irfft(a, n=None, axis=-1)
hfft(a, n=None, axis=-1)
ihfft(a, n=None, axis=-1)
fftn(a, s=None, axes=None)
ifftn(a, s=None, axes=None)
rfftn(a, s=None, axes=None)
irfftn(a, s=None, axes=None... | gpl-3.0 |
ketjow4/NOV | Lib/site-packages/numpy/fft/fftpack.py | 59 | 39653 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1)
ifft(a, n=None, axis=-1)
rfft(a, n=None, axis=-1)
irfft(a, n=None, axis=-1)
hfft(a, n=None, axis=-1)
ihfft(a, n=None, axis=-1)
fftn(a, s=None, axes=None)
ifftn(a, s=None, axes=None)
rfftn(a, s=None, axes=None)
irfftn(a, s=None, axes=None... | gpl-3.0 |
Adai0808/scikit-learn | sklearn/neighbors/nearest_centroid.py | 199 | 7249 | # -*- coding: utf-8 -*-
"""
Nearest Centroid Classification
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import warnings
import numpy as np
from scipy import sparse as sp
from ..base import BaseEstimator, ClassifierMixin
from ..met... | bsd-3-clause |
adamhaney/airflow | setup.py | 1 | 12977 | # -*- 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 |
pushpajnc/models | creating_customer_segments/renders.py | 1 | 4134 | import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pandas as pd
import numpy as np
from sklearn.decomposition import pca
def pca_results(good_data, pca):
'''
Create a DataFrame of the PCA results
Includes dimension feature weights and explained variance
Visualizes the PCA results
'''
# Dimension ... | mit |
vsoch/nidmviewer | nidmviewer/sparql.py | 1 | 4830 | '''
sparql.py: part of the nidmviewer package
Sparql queries
Copyright (c) 2014-2018, Vanessa Sochat
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the abo... | bsd-3-clause |
YinongLong/scikit-learn | sklearn/linear_model/tests/test_perceptron.py | 378 | 1815 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_raises
from sklearn.utils import check_random_state
from sklearn.datasets import load_iris
from sklearn.linear_model import Pe... | bsd-3-clause |
ZenDevelopmentSystems/scikit-learn | sklearn/tree/tests/test_export.py | 130 | 9950 | """
Testing for export functions of decision trees (sklearn.tree.export).
"""
from re import finditer
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingClassifier
from sklearn... | bsd-3-clause |
wmunters/py4sp | plotting/plot_sp.py | 1 | 4504 | import numpy as np
import load_sp as lsp
import matplotlib.pyplot as plt
import os
import windfarm as wf
def set_equal_tight(ax=plt.gca()):
ax.set_aspect('equal')
ax.autoscale(tight=True)
def plot_field_turbines(fieldfile='BL_field.dat', key='u', k=16):
bl = lsp.load_BLfield_real(fieldfile)
field = bl... | gpl-2.0 |
ishanic/scikit-learn | examples/ensemble/plot_adaboost_multiclass.py | 354 | 4124 | """
=====================================
Multi-class AdaBoosted Decision Trees
=====================================
This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can
improve prediction accuracy on a multi-class problem. The classification
dataset is constructed by taking a ten-dimensional ... | bsd-3-clause |
z/xonotic-map-repository | bin/entities_map.py | 1 | 2980 | #!/usr/bin/env python3
# Description: Plots entities on radars
# Author: Tyler "-z-" Mulligan
from matplotlib import pyplot as plt
import numpy as np
import matplotlib as mpl
import matplotlib.font_manager as font_manager
import struct
import sys
import os
from xmr.entities import *
path_entities = 'resources/entiti... | mit |
shivaenigma/electrum | plugins/plot.py | 5 | 3566 | from PyQt4.QtGui import *
from electrum.plugins import BasePlugin, hook
from electrum.i18n import _
import datetime
from electrum.util import format_satoshis
from electrum.bitcoin import COIN
try:
import matplotlib.pyplot as plt
import matplotlib.dates as md
from matplotlib.patches import Ellipse
fro... | gpl-3.0 |
jreback/pandas | pandas/tests/series/test_unary.py | 3 | 1755 | import pytest
from pandas import Series
import pandas._testing as tm
class TestSeriesUnaryOps:
# __neg__, __pos__, __inv__
def test_neg(self):
ser = tm.makeStringSeries()
ser.name = "series"
tm.assert_series_equal(-ser, -1 * ser)
def test_invert(self):
ser = tm.makeStrin... | bsd-3-clause |
ajylee/gpaw-rtxs | gpaw/testing/old_molecule_test.py | 1 | 6841 | # -*- coding: utf-8 -*-
import sys
import pickle
import traceback
import os.path as path
from ase.data.g2_1 import data
from ase.structure import molecule
from ase.data.molecules import latex
from ase.atoms import string2symbols
from ase.parallel import paropen
from ase.parallel import rank, barrier
from ase.io.trajec... | gpl-3.0 |
jls713/jfactors | flattened/sampler.py | 1 | 9246 | ## Generate samples from triaxiality distributions for Figures 9 & 10 and Table 5 of Sanders, Evans & Geringer-Sameth
## ============================================================================
import numpy as np
from numpy import sqrt,cos,sin
import emcee
# import corner
import sys
sys.path.append('/home/jls/work... | mit |
cerrno/neurokernel | examples/testLPU/visualize_testLPU.py | 1 | 1818 | #@author: Amol Kapoor
#date: 3-13-15
#Visualizer for simpleLPU stuff
import matplotlib as mpl
mpl.use('agg')
import neurokernel.LPU.utils.visualizer as vis
import networkx as nx
# Temporary fix for bug in networkx 1.8:
nx.readwrite.gexf.GEXF.convert_bool = {'false':False, 'False':False,
... | bsd-3-clause |
datapythonista/pandas | pandas/core/arrays/floating.py | 3 | 13304 | from __future__ import annotations
import warnings
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
from pandas._typing import (
ArrayLike,
DtypeObj,
)
from pandas.compat.numpy import function as nv
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes... | bsd-3-clause |
tylerjereddy/scipy | scipy/cluster/tests/test_hierarchy.py | 12 | 42543 | #
# Author: Damian Eads
# Date: April 17, 2008
#
# Copyright (C) 2008 Damian Eads
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this... | bsd-3-clause |
jlegendary/scikit-learn | examples/classification/plot_classification_probability.py | 242 | 2624 | """
===============================
Plot classification probability
===============================
Plot the classification probability for different classifiers. We use a 3
class dataset, and we classify it with a Support Vector classifier, L1
and L2 penalized logistic regression with either a One-Vs-Rest or multinom... | bsd-3-clause |
rohanp/scikit-learn | sklearn/cross_decomposition/cca_.py | 151 | 3192 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
Atmosferica/Turbolenza | scripts/KDE/main.py | 1 | 1050 | #!/usr/bin/python
#default module
import numpy as np
import matplotlib.pyplot as plt
import scipy.fftpack as fftp
import scipy.optimize as opt
import sys
import os
import string
from statsmodels.nonparametric.kernel_density import KDEMultivariate
from bcolors import *
from funct import *
def kde_m(x, x_grid, bandw... | gpl-3.0 |
FYECorpusProject/thesaurus-and-citation | Histograms20150820/histoparagraphone.py | 2 | 38019 | #!/usr/bin/env python ## use python 2.7
import sys
from collections import defaultdict
from os import listdir
from histogramcode import Histogram
from histosentence import HistoSentence
##
import numpy as np
import matplotlib.pyplot as plt
ALIGNMENTDUMMYPARA = -9
ALIGNMENTDUMMYSUB = -99
DISTANCEDUMMY = -999
########... | gpl-2.0 |
BhallaLab/moose-full | moose-examples/tutorials/ChemicalBistables/propagationBis.py | 2 | 6232 | #########################################################################
## This program is part of 'MOOSE', the
## Messaging Object Oriented Simulation Environment.
## Copyright (C) 2014 Upinder S. Bhalla. and NCBS
## It is made available under the terms of the
## GNU Lesser General Public License version 2... | gpl-2.0 |
mne-tools/mne-tools.github.io | 0.19/_downloads/f911a8ff6ce16e7e3e5057bbf8b5a690/plot_stats_cluster_time_frequency_repeated_measures_anova.py | 2 | 10044 | """
.. _tut-timefreq-twoway-anova:
====================================================================
Mass-univariate twoway repeated measures ANOVA on single trial power
====================================================================
This script shows how to conduct a mass-univariate repeated measures
ANOVA. ... | bsd-3-clause |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/sklearn/feature_selection/tests/test_base.py | 98 | 3681 | import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
from sklearn.utils import check_array
from sklearn.utils.testing import assert_raises, assert_equal
class StepSelector(Select... | mit |
adelomana/schema | conditionedFitness/figureMutagenized/script.2.3.py | 2 | 2965 | import matplotlib,numpy,sys,scipy,pickle
import matplotlib.pyplot
sys.path.append('../lib')
import calculateStatistics
### MAIN
matplotlib.rcParams.update({'font.size':36,'font.family':'Times New Roman','xtick.labelsize':28,'ytick.labelsize':28})
thePointSize=12
jarDir='/Users/adriandelomana/scratch/'
# mutagenized... | gpl-3.0 |
kerimlcr/ab2017-dpyo | ornek/osmnx/osmnx-0.3/osmnx/save_load.py | 1 | 16292 | ###################################################################################################
# Module: save_load.py
# Description: Save and load networks to/from disk
# License: MIT, see full license in LICENSE.txt
# Web: https://github.com/gboeing/osmnx
##########################################################... | gpl-3.0 |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/matplotlib/legend_handler.py | 4 | 22859 | """
This module defines default legend handlers.
It is strongly encouraged to have read the :ref:`legend guide
<plotting-guide-legend>` before this documentation.
Legend handlers are expected to be a callable object with a following
signature. ::
legend_handler(legend, orig_handle, fontsize, handlebox)
Where *l... | bsd-2-clause |
samuel1208/scikit-learn | sklearn/semi_supervised/tests/test_label_propagation.py | 307 | 1974 | """ test the label propagation module """
import nose
import numpy as np
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {'kernel': 'rbf'}),
(label_propa... | bsd-3-clause |
zfrenchee/pandas | pandas/tests/reshape/merge/test_merge_ordered.py | 2 | 2966 | import pandas as pd
from pandas import DataFrame, merge_ordered
from pandas.util import testing as tm
from pandas.util.testing import assert_frame_equal
from numpy import nan
class TestMergeOrdered(object):
def setup_method(self, method):
self.left = DataFrame({'key': ['a', 'c', 'e'],
... | bsd-3-clause |
dsockwell/trading-with-python | lib/backtest.py | 74 | 7381 | #-------------------------------------------------------------------------------
# Name: backtest
# Purpose: perform routine backtesting tasks.
# This module should be useable as a stand-alone library outide of the TWP package.
#
# Author: Jev Kuznetsov
#
# Created: 03/07/2014
... | bsd-3-clause |
fulmicoton/pylearn2 | pylearn2/cross_validation/dataset_iterators.py | 29 | 19389 | """
Cross-validation dataset iterators.
"""
__author__ = "Steven Kearnes"
__copyright__ = "Copyright 2014, Stanford University"
__license__ = "3-clause BSD"
import numpy as np
import warnings
try:
from sklearn.cross_validation import (KFold, StratifiedKFold, ShuffleSplit,
... | bsd-3-clause |
raghavrv/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 79 | 2849 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from sklearn.utils.testing import ... | bsd-3-clause |
B3AU/waveTree | sklearn/cluster/tests/test_spectral.py | 5 | 9160 | """Testing for Spectral Clustering methods"""
from sklearn.externals.six.moves import cPickle
from sklearn.metrics.pairwise import kernel_metrics
dumps, loads = cPickle.dumps, cPickle.loads
import numpy as np
from scipy import sparse
from sklearn.utils import check_random_state
from sklearn.utils.testing import ass... | bsd-3-clause |
laurent-george/bokeh | examples/app/stock_applet/stock_app.py | 42 | 7786 | """
This file demonstrates a bokeh applet, which can either be viewed
directly on a bokeh-server, or embedded into a flask application.
See the README.md file in this directory for instructions on running.
"""
import logging
logging.basicConfig(level=logging.DEBUG)
from os import listdir
from os.path import dirname,... | bsd-3-clause |
logpai/logparser | benchmark/LKE_benchmark.py | 1 | 5357 | #!/usr/bin/env python
import sys
sys.path.append('../')
from logparser import LKE, evaluator
import os
import pandas as pd
input_dir = '../logs/' # The input directory of log file
output_dir = 'LKE_result/' # The output directory of parsing results
benchmark_settings = {
'HDFS': {
'log_file'... | mit |
bsipocz/statsmodels | examples/python/kernel_density.py | 33 | 1805 |
## Kernel Density Estimation
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.distributions.mixture_rvs import mixture_rvs
##### A univariate example.
np.random.seed(12345)
obs_dist1 = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, sta... | bsd-3-clause |
ypid/series60-remote | pc/widget/StatisticCanvas.py | 1 | 2637 | # -*- coding: utf-8 -*-
# Copyright (c) 2008 - 2009 Lukas Hetzenecker <LuHe@gmx.at>
from PyQt4.QtCore import *
from PyQt4.QtGui import *
# Matplotlib
try:
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4 import NavigationToolbar2QT as Navig... | gpl-2.0 |
kakaba2009/MachineLearning | python/src/mylib/mlstm.py | 1 | 9814 | import math
import os.path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import src.mylib.mfile as mfile
import src.mylib.mcalc as mcalc
from matplotlib import style
from keras.utils import np_utils
from keras.optimizers import Adam, RMSprop
from sklearn.preprocessing import MinMaxSca... | apache-2.0 |
tochikuji/chainer-libDNN | examples/mnist/AE.py | 1 | 1210 | # example of Convolutional Auto-encoder with layer visualization
from libdnn import AutoEncoder
import chainer
import chainer.functions as F
import chainer.optimizers as Opt
import numpy
from sklearn.datasets import fetch_mldata
model = chainer.FunctionSet(
fh1=F.Linear(28 ** 2, 100),
fh3=F.Linear(100, 28 **... | mit |
kdebrab/pandas | asv_bench/benchmarks/reshape.py | 3 | 3829 | from itertools import product
import numpy as np
from pandas import DataFrame, MultiIndex, date_range, melt, wide_to_long
from .pandas_vb_common import setup # noqa
class Melt(object):
goal_time = 0.2
def setup(self):
self.df = DataFrame(np.random.randn(10000, 3), columns=['A', 'B', 'C'])
... | bsd-3-clause |
decvalts/cartopy | lib/cartopy/tests/mpl/test_images.py | 1 | 6074 | # (C) British Crown Copyright 2011 - 2018, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option)... | gpl-3.0 |
endolith/scipy | scipy/spatial/kdtree.py | 11 | 33807 | # Copyright Anne M. Archibald 2008
# Released under the scipy license
import numpy as np
import warnings
from .ckdtree import cKDTree, cKDTreeNode
__all__ = ['minkowski_distance_p', 'minkowski_distance',
'distance_matrix',
'Rectangle', 'KDTree']
def minkowski_distance_p(x, y, p=2):
"""Compu... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.22/_downloads/e8440d4a71ce3cd53b39ebc6f55d87ec/plot_linear_regression_raw.py | 18 | 2385 | """
========================================
Regression on continuous data (rER[P/F])
========================================
This demonstrates how rER[P/F]s - regressing the continuous data - is a
generalisation of traditional averaging. If all preprocessing steps
are the same, no overlap between epochs exists, and ... | bsd-3-clause |
MikeDelaney/sentiment | skeleton.py | 1 | 2705 | import sys, os
import numpy as np
from operator import itemgetter as ig
from sklearn.linear_model import LogisticRegression as LR
from collections import Counter
import string
# vocab = [] # the features used in the classifier
# build vocabulary
def buildvocab(numwords):
vocab = []
temp_words = []
base... | mit |
BDannowitz/polymath-progression-blog | jlab-ml-lunch-2/src/jlab.py | 1 | 8171 | import re
from io import StringIO
import pandas as pd
import numpy as np
from math import floor
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from tensorflow.keras.preprocessing.sequence import pad_sequences
COLS = ['x', 'y', 'z', 'px', 'py', 'pz', 'x1', 'y1', 'z1', 'px1', 'py1',
'pz... | gpl-2.0 |
meduz/scikit-learn | examples/preprocessing/plot_function_transformer.py | 158 | 1993 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you ca... | bsd-3-clause |
dsm054/pandas | asv_bench/benchmarks/panel_ctor.py | 3 | 1731 | import warnings
from datetime import datetime, timedelta
from pandas import DataFrame, Panel, DatetimeIndex, date_range
class DifferentIndexes(object):
def setup(self):
self.data_frames = {}
start = datetime(1990, 1, 1)
end = datetime(2012, 1, 1)
for x in range(100):
e... | bsd-3-clause |
aewhatley/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 213 | 3359 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
alpenwasser/laborjournal | versuche/skineffect/python/vollzylinder_highfreq_approx_low.py | 1 | 8960 | #!/usr/bin/env python3
from sympy import *
from mpmath import *
from matplotlib.pyplot import *
import matplotlib.ticker as plticker
#init_printing() # make things prettier when we print stuff for debugging.
# ************************************************************************** #
# B-Field, Cylinder Coi... | mit |
XianliangJ/collections | RCP-NS2/scripts/plot.py | 1 | 3051 | import math
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy
import sys
# Parse command-line arguments.
RCP_DATA_FILE = sys.argv[1]
TCP_DATA_FILE = sys.argv[2]
PLOT_FILE = sys.argv[3]
# Bottleneck link speed in b/s.
C = float(sys.argv[4]) * 1000000000
# RTT in seconds.
RTT = float... | gpl-3.0 |
Titan-C/scikit-learn | examples/linear_model/plot_iris_logistic.py | 119 | 1679 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <https://en.wikipedia.org/wiki/Iris_... | bsd-3-clause |
aarchiba/numpy | doc/example.py | 81 | 3581 | """This is the docstring for the example.py module. Modules names should
have short, all-lowercase names. The module name may have underscores if
this improves readability.
Every module should have a docstring at the very top of the file. The
module's docstring may extend over multiple lines. If your docstring doe... | bsd-3-clause |
chenyyx/scikit-learn-doc-zh | examples/en/cluster/plot_mini_batch_kmeans.py | 53 | 4096 | """
====================================================================
Comparison of the K-Means and MiniBatchKMeans clustering algorithms
====================================================================
We want to compare the performance of the MiniBatchKMeans and KMeans:
the MiniBatchKMeans is faster, but give... | gpl-3.0 |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/matplotlib/pylab.py | 8 | 11110 | """
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
MATLAB |reg| [*]_ analogs and similar arguments.
.. |reg| unicode:: 0xAE
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate som... | apache-2.0 |
Ryanglambert/pybrain | examples/rl/environments/linear_fa/bicycle.py | 26 | 14462 | from __future__ import print_function
"""An attempt to implement Randlov and Alstrom (1998). They successfully
use reinforcement learning to balance a bicycle, and to control it to drive
to a specified goal location. Their work has been used since then by a few
researchers as a benchmark problem.
We only implement th... | bsd-3-clause |
Maplenormandy/list-62x | python/testAlgorithms.py | 1 | 13897 | import cv2
import math
import pandas as pd
import numpy as np
import time, sys, os, shutil
import yaml
from multiprocessing import Process, Queue
from Queue import Empty
import random
import imageFeatures as imf
import pickle
from sklearn import gaussian_process
"""
# This script collects data
if len(sys.argv) < 2:
... | mit |
LohithBlaze/scikit-learn | examples/ensemble/plot_bias_variance.py | 357 | 7324 | """
============================================================
Single estimator versus bagging: bias-variance decomposition
============================================================
This example illustrates and compares the bias-variance decomposition of the
expected mean squared error of a single estimator again... | bsd-3-clause |
tudarmstadt-lt/sensegram | eval/significance.py | 1 | 2090 | from scipy.stats import binom
from pandas import read_csv
import numpy as np
import argparse
def mcnemar_midp(b, c):
"""Compute McNemar's test using the "mid-p" variant suggested by:
M.W. Fagerland, S. Lydersen, P. Laake. 2013. The McNemar test for
binary matched-pairs data: Mid-p and asymptotic are... | apache-2.0 |
hainm/scikit-learn | sklearn/neural_network/tests/test_rbm.py | 142 | 6276 | import sys
import re
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
from sklearn.utils.testing import (assert_almost_equal, assert_array_equal,
assert_true)
from sklearn.datasets import load_digits
from sklearn.externals.six.moves import cStringIO as ... | bsd-3-clause |
steelee/fishbowl-notebooks | ipython/profile_nbserver/ipython_config.py | 2 | 20465 | # Configuration file for ipython.
c = get_config()
#------------------------------------------------------------------------------
# InteractiveShellApp configuration
#------------------------------------------------------------------------------
# A Mixin for applications that start InteractiveShell instances.
#
#... | mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.