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 |
|---|---|---|---|---|---|
aayushidwivedi01/spark-tk | regression-tests/sparktkregtests/testcases/graph/graph_clustering_coefficient_test.py | 13 | 2059 | # vim: set encoding=utf-8
# Copyright (c) 2016 Intel Corporation
#
# 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 require... | apache-2.0 |
rajat1994/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 |
DSLituiev/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 |
ningchi/scikit-learn | examples/gaussian_process/plot_gp_probabilistic_classification_after_regression.py | 252 | 3490 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
==============================================================================
Gaussian Processes classification example: exploiting the probabilistic output
==============================================================================
A two-dimensional regression exerci... | bsd-3-clause |
maciekswat/dolfin_1.3.0 | bench/plot.py | 4 | 8210 | #!/usr/bin/env python
"""
This script parses logs/bench.log and create plots for each case with
the timings function of time (date plot). It also creates a web page
index.html for easy viewing of the generated plots.
"""
# Copyright (C) 2010 Johannes Ring
#
# This file is part of DOLFIN.
#
# DOLFIN is free software: ... | gpl-3.0 |
mpharrigan/msmbuilder | Tutorial/PlotDihedrals.py | 2 | 2998 | #!/usr/bin/env python
# This file is part of MSMBuilder.
#
# Copyright 2011 Stanford University
#
# MSMBuilder 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 opt... | gpl-2.0 |
haripradhan/MissionPlanner | Lib/site-packages/numpy/lib/recfunctions.py | 58 | 34495 | """
Collection of utilities to manipulate structured arrays.
Most of these functions were initially implemented by John Hunter for matplotlib.
They have been rewritten and extended for convenience.
"""
import sys
import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from nump... | gpl-3.0 |
chanceraine/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_qtagg.py | 73 | 4972 | """
Render to qt from agg
"""
from __future__ import division
import os, sys
import matplotlib
from matplotlib import verbose
from matplotlib.figure import Figure
from backend_agg import FigureCanvasAgg
from backend_qt import qt, FigureManagerQT, FigureCanvasQT,\
show, draw_if_interactive, backend_version, \
... | agpl-3.0 |
rseubert/scikit-learn | sklearn/neighbors/tests/test_ball_tree.py | 30 | 9727 | import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.ball_tree import (BallTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dist_metrics impo... | bsd-3-clause |
liangjg/openmc | tests/regression_tests/filter_energyfun/test.py | 8 | 1854 | import openmc
import pytest
from tests.testing_harness import PyAPITestHarness
@pytest.fixture
def model():
model = openmc.model.Model()
m = openmc.Material()
m.set_density('g/cm3', 10.0)
m.add_nuclide('Am241', 1.0)
model.materials.append(m)
s = openmc.Sphere(r=100.0, boundary_type='vacuum'... | mit |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/tests/tslibs/test_parsing.py | 2 | 6119 | """
Tests for Timestamp parsing, aimed at pandas/_libs/tslibs/parsing.pyx
"""
from datetime import datetime
from dateutil.parser import parse
import numpy as np
import pytest
from pandas._libs.tslibs import parsing
from pandas._libs.tslibs.parsing import parse_time_string
import pandas.util._test_decorators as td
fr... | apache-2.0 |
endolith/scikit-image | doc/examples/segmentation/plot_peak_local_max.py | 6 | 1443 | """
====================
Finding local maxima
====================
The ``peak_local_max`` function returns the coordinates of local peaks (maxima)
in an image. A maximum filter is used for finding local maxima. This operation
dilates the original image and merges neighboring local maxima closer than the
size of the di... | bsd-3-clause |
ilo10/scikit-learn | sklearn/grid_search.py | 103 | 36232 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
jerkern/pyParticleEst | test/manual/paramest/standard_nonlin_model_psaem.py | 1 | 9189 | import numpy
import math
import pyparticleest.utils.kalman as kalman
import pyparticleest.interfaces as interfaces
import pyparticleest.paramest.paramest as param_est
import pyparticleest.paramest.interfaces as pestint
import matplotlib.pyplot as plt
import scipy.optimize
def generate_dataset(steps, P0, Q, R):
x ... | lgpl-3.0 |
natasasdj/OpenWPM | analysis/13_nonregularties.py | 1 | 1067 | import sqlite3
import pandas as pd
from shutil import copyfile
import os
data_dir = '/home/nsarafij/project/data/'
out_dir = '/home/nsarafij/project/OpenWPM/analysis/nonregular/0-pixel_images/'
db = '/home/nsarafij/project/OpenWPM/analysis/results/images.sqlite'
conn= sqlite3.connect(db)
query = 'SELECT * FROM Images... | gpl-3.0 |
xubenben/scikit-learn | sklearn/svm/tests/test_sparse.py | 32 | 12988 | from nose.tools import assert_raises, assert_true, assert_false
import numpy as np
from scipy import sparse
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from sklearn import datasets, svm, linear_model, base
from sklearn.datasets import make_classif... | bsd-3-clause |
dch312/scipy | scipy/special/add_newdocs.py | 8 | 70142 | # Docstrings for generated ufuncs
#
# The syntax is designed to look like the function add_newdoc is being
# called from numpy.lib, but in this file add_newdoc puts the
# docstrings in a dictionary. This dictionary is used in
# generate_ufuncs.py to generate the docstrings for the ufuncs in
# scipy.special at the C lev... | bsd-3-clause |
jostep/tensorflow | tensorflow/examples/learn/text_classification_character_cnn.py | 29 | 5666 | # 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 appl... | apache-2.0 |
liberatorqjw/scikit-learn | examples/model_selection/plot_roc_crossval.py | 247 | 3253 | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.
ROC curv... | bsd-3-clause |
bhermanmit/openmc | openmc/universe.py | 1 | 18631 | from __future__ import division
from copy import copy
from collections import OrderedDict, Iterable
from numbers import Integral, Real
import random
import sys
from six import string_types
import numpy as np
import openmc
import openmc.checkvalue as cv
from openmc.plots import _SVG_COLORS
# A static variable for au... | mit |
open-mmlab/mmdetection | tools/analysis_tools/analyze_logs.py | 1 | 6252 | import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(log_dicts, args):
for i, log_dict in enumerate(log_dicts):
print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
all_times = ... | apache-2.0 |
gpengzhi/gpengzhi.github.io | markdown_generator/publications.py | 197 | 3887 |
# coding: utf-8
# # Publications markdown generator for academicpages
#
# Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook, with the core python code in publications.py. Run either from the `markdown_g... | mit |
miramzz/sentiment | student_sentiment/skeleton.py | 1 | 5194 | import sys, os
import numpy as np
from operator import itemgetter as ig
from sklearn.linear_model import LogisticRegression as LR
import random
vocab = [] #the features used in the classifier
pos_dir = "/Users/muazzezmira/projects/sentiment/sentiment/student_sentiment/pos/"
neg_dir = "/Users/muazzezmira/projects/senti... | mit |
larsoner/mne-python | mne/viz/_3d.py | 2 | 137424 | # -*- coding: utf-8 -*-
"""Functions to make 3D plots with M/EEG data."""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Mainak Jas <maina... | bsd-3-clause |
robert-digit/superset | superset/connectors/sqla/models.py | 2 | 25725 | from datetime import datetime
import logging
import sqlparse
from past.builtins import basestring
import pandas as pd
from sqlalchemy import (
Column, Integer, String, ForeignKey, Text, Boolean,
DateTime,
)
import sqlalchemy as sa
from sqlalchemy import asc, and_, desc, select
from sqlalchemy.ext.compiler imp... | apache-2.0 |
ldirer/scikit-learn | examples/manifold/plot_compare_methods.py | 52 | 3878 | """
=========================================
Comparison of Manifold Learning methods
=========================================
An illustration of dimensionality reduction on the S-curve dataset
with various manifold learning methods.
For a discussion and comparison of these algorithms, see the
:ref:`manifold module... | bsd-3-clause |
herilalaina/scikit-learn | sklearn/utils/extmath.py | 20 | 24891 | """
Extended math utilities.
"""
# Authors: Gael Varoquaux
# Alexandre Gramfort
# Alexandre T. Passos
# Olivier Grisel
# Lars Buitinck
# Stefan van der Walt
# Kyle Kastner
# Giorgio Patrini
# License: BSD 3 clause
from __future__ import division
import war... | bsd-3-clause |
bzero/statsmodels | examples/python/regression_plots.py | 33 | 9585 |
## Regression Plots
from __future__ import print_function
from statsmodels.compat import lzip
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import ols
### Duncan's Prestige Dataset
#### Load the Data
# We can use a utility function... | bsd-3-clause |
ngoix/OCRF | examples/cluster/plot_cluster_iris.py | 350 | 2593 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
snnn/tensorflow | tensorflow/contrib/gan/python/estimator/python/stargan_estimator_test.py | 13 | 12094 | # Copyright 2017 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 |
RomainBrault/scikit-learn | examples/gaussian_process/plot_gpc.py | 103 | 3927 | """
====================================================================
Probabilistic predictions with Gaussian process classification (GPC)
====================================================================
This example illustrates the predicted probability of GPC for an RBF kernel
with different choices of the hy... | bsd-3-clause |
rajat1994/scikit-learn | benchmarks/bench_plot_svd.py | 325 | 2899 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklearn.datasets.s... | bsd-3-clause |
raghavrv/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more impor... | bsd-3-clause |
amolkahat/pandas | pandas/tests/tseries/offsets/test_offsets.py | 1 | 131392 | from distutils.version import LooseVersion
from datetime import date, datetime, timedelta
import pytest
import pytz
from pandas.compat import range
from pandas import compat
import numpy as np
from pandas.compat.numpy import np_datetime64_compat
from pandas.core.series import Series
from pandas._libs.tslibs import ... | bsd-3-clause |
mvpossum/machine-learning | tp2/plot_2d_dataset.py | 2 | 1362 | #! /usr/bin/env python
#Read arguments
from sys import argv
if len(argv)!=2:
print("Usage: {} <dataset_name>".format(argv[0]))
exit(1)
PREFIX=argv[1]
#Read data
import csv
DATA_FILE=PREFIX
x,y,clase=[],[],[]
with open(DATA_FILE,'r') as f:
reader = csv.reader(f)
for row in reader:
if '\t' in r... | mit |
pnedunuri/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 305 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
Martinfx/yodaqa | data/ml/forest_graphviz.py | 3 | 5896 | """
This module defines export functions for decision trees and forests.
"""
# Based on the export_graphviz module.
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satraji... | apache-2.0 |
Neurita/darwin | darwin/features.py | 1 | 7893 | # -*- coding: utf-8 -*-
#------------------------------------------------------------------------------
#Authors:
# Alexandre Manhaes Savio <alexsavio@gmail.com>
# Darya Chyzhyk <darya.chyzhyk@gmail.com>
# Borja Ayerdi <ayerdi.borja@gmail.com>
# Grupo de Inteligencia Computational <www.ehu.es/ccwintco>
#
# BSD 3-Claus... | bsd-3-clause |
Eric89GXL/scikit-learn | examples/ensemble/plot_gradient_boosting_regression.py | 8 | 2490 | """
============================
Gradient Boosting regression
============================
Demonstrate Gradient Boosting on the boston housing dataset.
This example fits a Gradient Boosting model with least squares loss and
500 regression trees of depth 4.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prett... | bsd-3-clause |
rosswhitfield/mantid | qt/python/mantidqt/widgets/sliceviewer/lineplots.py | 3 | 14766 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
# T... | gpl-3.0 |
warmspringwinds/scikit-image | doc/examples/plot_ssim.py | 15 | 2238 | """
===========================
Structural similarity index
===========================
When comparing images, the mean squared error (MSE)--while simple to
implement--is not highly indicative of perceived similarity. Structural
similarity aims to address this shortcoming by taking texture into account
[1]_, [2]_.
T... | bsd-3-clause |
QuLogic/burnman | examples/example_premite_isothermal.py | 1 | 3877 | # BurnMan - a lower mantle toolkit
# Copyright (C) 2012, 2013, Heister, T., Unterborn, C., Rose, I. and Cottaar, S.
# Released under GPL v2 or later.
"""
This example is under construction.
requires:
teaches:
"""
import os, sys, numpy as np, matplotlib.pyplot as plt
#hack to allow scripts to be placed in subdirec... | gpl-2.0 |
ottogroup/dstoolbox | dstoolbox/transformers/tests/test_slicing.py | 1 | 6971 | """Tests for transformers.slicing.py."""
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
import pytest
class TestItemSelector:
@pytest.fixture
def item_selector_cls(self):
from dstoolbox.transformers import ItemSelector
return ItemSelector
@pytest.fix... | apache-2.0 |
ConeyLiu/spark | python/pyspark/testing/sqlutils.py | 9 | 7813 | #
# 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 "License"); you may not us... | apache-2.0 |
smile4life/uwaterloo-igem-2015 | models/tridimensional/data_kleinstiver/cas9_mutants_stats.py | 8 | 9578 | """
Gathering statistics on the Cas9 mutants that were able to bind alternative PAMs
TODO:
- mutations which always co-occur (regionally? or by AA class?)
- mutations which co-occur between NGA and NGC PAMs
- mutations which differ between NGA and NGC PAMs
"""
import operator
import numpy as np
import matplotlib.pyplo... | mit |
raincoatrun/basemap | examples/barb_demo.py | 4 | 2853 | from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# read in data.
file = open('fcover.dat','r')
ul=[];vl=[];pl=[]
nlons=73; nlats=73
dellat = 2.5; dellon = 5.
for line in file.readlines():
l = line.replace('\n','').split()
ul.append(float(l[0]))
vl.append(float(l[1]))... | gpl-2.0 |
vantares/trading-with-python | cookbook/reconstructVXX/downloadVixFutures.py | 77 | 3012 | #-------------------------------------------------------------------------------
# Name: download CBOE futures
# Purpose: get VIX futures data from CBOE, process data to a single file
#
#
# Created: 15-10-2011
# Copyright: (c) Jev Kuznetsov 2011
# Licence: BSD
#-----------------------------... | bsd-3-clause |
Dapid/numpy | 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... | bsd-3-clause |
aborovin/trading-with-python | cookbook/reconstructVXX/downloadVixFutures.py | 77 | 3012 | #-------------------------------------------------------------------------------
# Name: download CBOE futures
# Purpose: get VIX futures data from CBOE, process data to a single file
#
#
# Created: 15-10-2011
# Copyright: (c) Jev Kuznetsov 2011
# Licence: BSD
#-----------------------------... | bsd-3-clause |
miloharper/neural-network-animation | matplotlib/testing/jpl_units/UnitDblFormatter.py | 23 | 1485 | #===========================================================================
#
# UnitDblFormatter
#
#===========================================================================
"""UnitDblFormatter module containing class UnitDblFormatter."""
#==========================================================================... | mit |
cybernet14/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
almarklein/bokeh | bokeh/models/sources.py | 1 | 7168 | from __future__ import absolute_import
from ..plot_object import PlotObject
from ..properties import HasProps
from ..properties import Any, Int, String, Instance, List, Dict, Either
class DataSource(PlotObject):
""" A base class for data source types. ``DataSource`` is
not generally useful to instantiate on i... | bsd-3-clause |
keflavich/agpy | radex/plot_grids.py | 6 | 11182 | #!/Library/Frameworks/Python.framework/Versions/Current/bin/python
from pylab import *
import pyfits
import numpy
from agpy import readcol,asinh_norm
import matplotlib
import sys
"""
Two procedures:
plot_radex is for contour plotting a subset of a radex cube
gridcube is to turn a parameter cube into a .fits d... | mit |
henridwyer/scikit-learn | examples/decomposition/plot_incremental_pca.py | 244 | 1878 | """
===============
Incremental PCA
===============
Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memo... | bsd-3-clause |
mattwthompson/mdtraj | mdtraj/formats/pdb/pdbfile.py | 2 | 29740 | ##############################################################################
# MDTraj: A Python Library for Loading, Saving, and Manipulating
# Molecular Dynamics Trajectories.
# Copyright 2012-2013 Stanford University and the Authors
#
# Authors: Peter Eastman, Robert McGibbon
# Contributors: Carlos Hernande... | lgpl-2.1 |
rubikloud/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
Shuailong/CCGSupertagging | code/utils.py | 1 | 1914 | #!/usr/bin/env python
# encoding: utf-8
"""
utils.py
Created by Shuailong on 2017-02-14.
"""
from __future__ import print_function
from __future__ import division
from keras.metrics import categorical_accuracy
from sklearn.model_selection import KFold
def extract_features(X):
'''
X: [[word11, word12, ...]... | mit |
mdegis/machine-learning | tools/feature_format.py | 25 | 4390 | #!/usr/bin/python
"""
A general tool for converting data from the
dictionary format to an (n x k) python list that's
ready for training an sklearn algorithm
n--no. of key-value pairs in dictonary
k--no. of features being extracted
dictionary keys are names of persons in dataset
dictiona... | gpl-3.0 |
jmetzen/scikit-learn | sklearn/ensemble/weight_boosting.py | 23 | 40739 | """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 |
webmasterraj/FogOrNot | flask/lib/python2.7/site-packages/pandas/tools/plotting.py | 2 | 116811 | # being a bit too dynamic
# pylint: disable=E1101
import datetime
import warnings
import re
from math import ceil
from collections import namedtuple
from contextlib import contextmanager
from distutils.version import LooseVersion
import numpy as np
from pandas.util.decorators import cache_readonly, deprecate_kwarg
im... | gpl-2.0 |
toobaz/pandas | pandas/core/arrays/sparse.py | 1 | 71362 | """
SparseArray data structure
"""
from collections import abc
import numbers
import operator
import re
from typing import Any, Callable
import warnings
import numpy as np
from pandas._libs import index as libindex, lib
import pandas._libs.sparse as splib
from pandas._libs.sparse import BlockIndex, IntIndex, SparseIn... | bsd-3-clause |
zaxtax/scikit-learn | sklearn/utils/random.py | 37 | 10511 | # Author: Hamzeh Alsalhi <ha258@cornell.edu>
#
# License: BSD 3 clause
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ =... | bsd-3-clause |
sanuj/shogun | examples/undocumented/python_modular/graphical/regression_lars.py | 26 | 3327 | #!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
from modshogun import RegressionLabels, RealFeatures
from modshogun import LeastAngleRegression, LinearRidgeRegression, LeastSquaresRegression
from modshogun import MeanSquaredError
# we compare LASSO with ordinary least-squares (OLE)
# in the idea... | gpl-3.0 |
rosswhitfield/mantid | qt/applications/workbench/workbench/plotting/figureinteraction.py | 3 | 37878 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2019 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
# T... | gpl-3.0 |
johannes-scharlach/pod-control | src/script.py | 1 | 25080 | from __future__ import division, print_function
import math
import numpy as np
from scipy import linalg
from matplotlib.pyplot import plot, subplot, legend, figure
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import example2sys as e2s
import analysis
def optionPricingScript():
N = 1000
... | mit |
cbertinato/pandas | pandas/tests/io/json/test_ujson.py | 1 | 36474 | try:
import json
except ImportError:
import simplejson as json
import calendar
import datetime
import decimal
import locale
import math
import re
import time
import dateutil
import numpy as np
import pytest
import pytz
import pandas._libs.json as ujson
from pandas._libs.tslib import Timestamp
import pandas.co... | bsd-3-clause |
bmmalone/as-auto-sklearn | as_asl/as_asl_command_line_utils.py | 1 | 5873 | ###
# This module contains helpers to ensure consistency across the command line
# parameters for the Algorithm Selection auto-sklearn wrapper project.
###
import misc.math_utils as math_utils
import numpy as np
###
# C
###
def add_config(parser):
""" Add the (required) config parameter to the parser
"""... | mit |
pavelchristof/gomoku-ai | tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py | 92 | 4535 | # 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 |
dallascard/guac | core/personas/cluster_input_vectors.py | 1 | 1752 | from optparse import OptionParser
import os
from sklearn.cluster import KMeans
from ..util import dirs
from ..util import file_handling as fh
def main():
usage = "%prog project input_vector_file"
parser = OptionParser(usage=usage)
parser.add_option('-n', dest='n_clusters', default=5000,
... | apache-2.0 |
yanlend/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 18 | 3768 | """
=======================================================================================
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
=======================================================================================
This is an example of applying Non-negative Matrix ... | bsd-3-clause |
cogeorg/econlib | networkx/drawing/nx_pylab.py | 22 | 27761 | """
**********
Matplotlib
**********
Draw networks with matplotlib.
See Also
--------
matplotlib: http://matplotlib.sourceforge.net/
pygraphviz: http://networkx.lanl.gov/pygraphviz/
"""
# Copyright (C) 2004-2012 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Sw... | gpl-3.0 |
maxalbert/blaze | blaze/__init__.py | 13 | 2475 | from __future__ import absolute_import, division, print_function
try:
import h5py # if we import h5py after tables we segfault
except ImportError:
pass
from pandas import DataFrame
from odo import odo, convert, append, resource, drop
from odo.backends.csv import CSV
from odo.backends.json import JSON, JSONLi... | bsd-3-clause |
jaytlennon/Emergence | tools/DiversityTools/macroecotools/test_macroecotools.py | 12 | 4915 | """Test suite for macroecotools"""
from pandas import Series, DataFrame
from numpy import array, array_equal
from macroecotools import *
comp_data = DataFrame({'site': [1, 1, 2, 3, 3, 3, 4],
'year': [1, 2, 1, 1, 1, 2, 2],
'genus': ['a', 'a', 'a', 'a', 'a', 'd', 'f'],
... | mit |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/tests/sparse/test_combine_concat.py | 15 | 13923 | # pylint: disable-msg=E1101,W0612
import numpy as np
import pandas as pd
import pandas.util.testing as tm
class TestSparseSeriesConcat(object):
def test_concat(self):
val1 = np.array([1, 2, np.nan, np.nan, 0, np.nan])
val2 = np.array([3, np.nan, 4, 0, 0])
for kind in ['integer', 'block'... | apache-2.0 |
pandalibin/backtrader-cn | tests/test_datas_tushare.py | 1 | 3551 | import unittest
import unittest.mock as um
import datetime as dt
import pandas as pd
import backtradercn.datas.tushare as bdt
from backtradercn.settings import settings as conf
from backtradercn.libs import models
class TsHisDataTestCase(unittest.TestCase):
def test_run(self):
self._test_download_delta_d... | gpl-3.0 |
wazeerzulfikar/scikit-learn | examples/linear_model/plot_ridge_coeffs.py | 157 | 2785 | """
==============================================================
Plot Ridge coefficients as a function of the L2 regularization
==============================================================
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regression is the estimator used in this example.
Each color in the le... | bsd-3-clause |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/tensorflow/models/lfads/plot_lfads.py | 2 | 8146 | # Copyright 2017 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... | bsd-2-clause |
decvalts/landlab | setup.py | 1 | 3267 | #! /usr/bin/env python
from ez_setup import use_setuptools # uncomented for dev build
use_setuptools() # uncommented for dev build
from setuptools import setup, find_packages, Extension
from setuptools.command.install import install
from setuptools.command.develop import develop
from distutils.core import setup # ne... | mit |
cauchycui/scikit-learn | sklearn/manifold/tests/test_isomap.py | 226 | 3941 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/numpy/lib/function_base.py | 19 | 164441 | from __future__ import division, absolute_import, print_function
import collections
import operator
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import linspace, atleast_1d, atleast_2d, transpose
from numpy.core.numeric import (
ones, zeros, arange, conc... | gpl-3.0 |
steve855/F_UNCLE | F_UNCLE/Models/Isentrope.py | 1 | 22644 | #!/usr/bin/pyton
"""
Isentrope
Abstract class for an isentrope
Authors
-------
- Stephen Andrews (SA)
- Andrew M. Fraser (AMF)
Revisions
---------
0 -> Initial class creation (03-16-2016)
ToDo
----
None
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_fun... | gpl-2.0 |
mhallett/MeDaReDa | demos/demo1/plotccys.py | 1 | 2727 | # plot10ccy.py
'''
Plot the ccy rates, and the product
'''
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import datetime
import medareda_lib
def get_conn():
return medareda_lib.get_conn()
# select count from vPrice
connpg = get_conn()
curpg = connpg.cursor()
cu... | mit |
wesleyktatum/Nanowire_Measurements | NaRWHAL/knn_method.py | 1 | 1138 | import numpy as np
import math
import matplotlib.pyplot as plt
wire_image = np.loadtxt('100-0_72_NW-003')
#side = np.linspace(1,len(wire_image),len(wire_image))
side = np.linspace(1, 40, 40)
x, y = np.meshgrid(side, side)
plt.figure(1)
plt.contourf(x, y, wire_image[321:361, 321:361], cmap='bwr')
plt.colorbar()
plt.sho... | gpl-3.0 |
Rossonero/bmlswp | ch01/gen_webstats.py | 23 | 1289 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
# This script generates web traffic data for our hypothetical
# web startup "MLASS" in chapter 01
impo... | mit |
mlyundin/scikit-learn | examples/linear_model/plot_lasso_lars.py | 363 | 1080 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regulariza... | bsd-3-clause |
vamsirajendra/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/contour.py | 69 | 42063 | """
These are classes to support contour plotting and
labelling for the axes class
"""
from __future__ import division
import warnings
import matplotlib as mpl
import numpy as np
from numpy import ma
import matplotlib._cntr as _cntr
import matplotlib.path as path
import matplotlib.ticker as ticker
import matplotlib.cm... | agpl-3.0 |
njhenry/batch_geocode | batch_geocode.py | 1 | 10145 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 17 10:37:07 2017
@author: nathenry
This module includes a series of functions that allow for automated geocoding
using the Google Maps, OpenStreetMaps, and GeoNames APIs.
You can edit the paths within this program, or else call it from the comm... | gpl-3.0 |
meduz/scikit-learn | sklearn/linear_model/sag.py | 18 | 11273 | """Solvers for Ridge and LogisticRegression using SAG algorithm"""
# Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# License: BSD 3 clause
import numpy as np
import warnings
from ..exceptions import ConvergenceWarning
from ..utils import check_array
from ..utils.extmath import row_norms
from .base import ... | bsd-3-clause |
Vimos/scikit-learn | sklearn/decomposition/nmf.py | 4 | 45047 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck
# Mathieu Blondel <mathieu@mblondel.org>
# Tom Dupre la Tour
# License: BSD 3 clause
from __future__ import division, print_function
from math import sqrt
import warnings
import numbers
import time
import numpy ... | bsd-3-clause |
flavour/ifrc_qa | tests/travis/generate_requirements_file.py | 32 | 1088 | #!/usr/bin/python
# usage - python generate_requirements.py [folder where the file should be generated] [list of requirements file]
# example - python tests/travis/generate_requirements_file.py tests/travis requirements.txt optional_requirements.txt
from sys import argv
# numpy - preinstalled
# matplotlib, lxml - ins... | mit |
giltis/xray-vision | xray_vision/messenger/mpl/__init__.py | 6 | 5498 | # ######################################################################
# Copyright (c) 2014, Brookhaven Science Associates, Brookhaven #
# National Laboratory. All rights reserved. #
# #
# Redistribution and use in ... | bsd-3-clause |
mattilyra/scikit-learn | examples/linear_model/plot_ols_3d.py | 350 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature... | bsd-3-clause |
jeffersonfparil/GTWAS_POOL_RADseq_SIM | BACKUP_SCRIPTS_20170930/plotROC.py | 1 | 3345 | #!/usr/bin/env python
import os, subprocess, sys, math
import pandas
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from collections import Counter
from sklearn import metrics
# #for testing:
# workDIR = "/mnt/SIMULATED/test/DNA/"
# GWAS_OUT = "GWAlpha_${rep}-${npool}POOLS-$... | gpl-3.0 |
jselsing/XSGRB_reduction_scrips | py/XSHcomb_1D.py | 2 | 1578 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import glob
import numpy as np
import matplotlib.pyplot as pl
from util import *
def main():
data_dir = "/Users/jselsing/Work/work_rawDATA/XSGRB/"
object_name = data_dir + "GRB121229A/"
arms = ["UVB", "VIS", "... | gpl-3.0 |
r-mart/scikit-learn | examples/classification/plot_lda_qda.py | 78 | 5046 | """
====================================================================
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 |
mattpitkin/scotchcorner | scotchcorner.py | 2 | 38545 | from __future__ import print_function, division
__version__ = "0.2.1"
__author__ = "Matthew Pitkin (matthew.pitkin@glasgow.ac.uk)"
__copyright__ = "Copyright 2016 Matthew Pitkin, Ben Farr and Will Farr"
import numpy as np
import pandas as pd
import scipy.stats as ss
import math
import matplotlib as mpl
from matplotl... | mit |
Jimmy-Morzaria/scikit-learn | sklearn/calibration.py | 3 | 18615 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
from __future__ impo... | bsd-3-clause |
Agent007/deepchem | deepchem/splits/splitters.py | 1 | 34827 | """
Contains an abstract base class that supports chemically aware data splits.
"""
from __future__ import division
from __future__ import unicode_literals
import random
__author__ = "Bharath Ramsundar, Aneesh Pappu "
__copyright__ = "Copyright 2016, Stanford University"
__license__ = "MIT"
import tempfile
import nu... | mit |
aflaxman/scikit-learn | sklearn/linear_model/tests/test_randomized_l1.py | 30 | 8448 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from tempfile import mkdtemp
import shutil
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
ferdinandvwyk/gs2_analysis | films.py | 2 | 16388 | import os
import sys
import gc
# Third Party
import numpy as np
from netCDF4 import Dataset
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import seaborn as sns
import pyfilm as pf
plt.rcParams.update({'figure.autolayout': True})
mpl.rcPa... | gpl-2.0 |
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