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 |
|---|---|---|---|---|---|
BoltzmannBrain/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/ticker.py | 69 | 37420 | """
Tick locating and formatting
============================
This module contains classes to support completely configurable tick
locating and formatting. Although the locators know nothing about
major or minor ticks, they are used by the Axis class to support major
and minor tick locating and formatting. Generic t... | agpl-3.0 |
lfairchild/PmagPy | programs/histplot.py | 1 | 2907 | #!/usr/bin/env python
import sys
import numpy as np
import matplotlib
if matplotlib.get_backend() != "TKAgg":
matplotlib.use("TKAgg")
from matplotlib import pyplot as plt
from pmagpy import pmagplotlib
def main():
"""
NAME
histplot.py
DESCRIPTION
makes histograms for data
OPTIONS
... | bsd-3-clause |
mantidproject/mantid | qt/applications/workbench/workbench/app/start.py | 3 | 8312 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2020 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 |
huobaowangxi/scikit-learn | sklearn/ensemble/voting_classifier.py | 178 | 8006 | """
Soft Voting/Majority Rule classifier.
This module contains a Soft Voting/Majority Rule classifier for
classification estimators.
"""
# Authors: Sebastian Raschka <se.raschka@gmail.com>,
# Gilles Louppe <g.louppe@gmail.com>
#
# Licence: BSD 3 clause
import numpy as np
from ..base import BaseEstimator
f... | bsd-3-clause |
xiaoxiamii/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 272 | 6972 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, wher... | bsd-3-clause |
tomlof/scikit-learn | sklearn/manifold/tests/test_isomap.py | 121 | 4301 | from itertools import product
import numpy as np
from numpy.testing import (assert_almost_equal, assert_array_almost_equal,
assert_equal)
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
fro... | bsd-3-clause |
zorojean/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 |
ChanChiChoi/scikit-learn | sklearn/metrics/tests/test_common.py | 43 | 44042 | from __future__ import division, print_function
from functools import partial
from itertools import product
import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer
from sklearn.utils.multiclass impo... | bsd-3-clause |
shangwuhencc/scikit-learn | examples/linear_model/plot_lasso_and_elasticnet.py | 249 | 1982 | """
========================================
Lasso and Elastic Net for Sparse Signals
========================================
Estimates Lasso and Elastic-Net regression models on a manually generated
sparse signal corrupted with an additive noise. Estimated coefficients are
compared with the ground-truth.
"""
print(... | bsd-3-clause |
padilha/biclustlib | biclustlib/algorithms/wrappers/spectral.py | 1 | 1977 | """
biclustlib: A Python library of biclustering algorithms and evaluation measures.
Copyright (C) 2017 Victor Alexandre Padilha
This file is part of biclustlib.
biclustlib is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by... | gpl-3.0 |
X-DataInitiative/tick | tick/preprocessing/utils.py | 2 | 2833 | # License: BSD 3 clause
import numpy as np
import pandas as pd
from warnings import warn
def safe_array(X, dtype=np.float64):
"""Checks if the X has the correct type, dtype, and is contiguous.
Parameters
----------
X : `pd.DataFrame` or `np.ndarray` or `crs_matrix`
The input data.
dtype... | bsd-3-clause |
rogerallen/kaggle | utils/utils.py | 1 | 7643 | from __future__ import division,print_function
import math, os, json, sys, re
import cPickle as pickle
from glob import glob
import numpy as np
from matplotlib import pyplot as plt
from operator import itemgetter, attrgetter, methodcaller
from collections import OrderedDict
import itertools
from itertools import chain
... | apache-2.0 |
Extintor/piva | practica3/p3script2.py | 1 | 1848 | # -*- coding: utf-8 -*-
"""
Created on Fri Mar 11 13:07:07 2016
@author: paul
"""
import matplotlib.pyplot as plt
import numpy as np
def separaimatge(secret,bitsred,bitsgreen,bitsblue):
secretredshift = np.right_shift(secret,8-bitsred)
secretgreenshift = np.mod(np.right_shift(secret,8-bitsred-bitsgreen),2**... | gpl-3.0 |
aflaxman/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 38 | 16445 | import sys
import numpy as np
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from scipy.special import psi
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
_diri... | bsd-3-clause |
vybstat/scikit-learn | sklearn/discriminant_analysis.py | 19 | 26162 | """
Linear Discriminant Analysis and Quadratic Discriminant Analysis
"""
# Authors: Clemens Brunner
# Martin Billinger
# Matthieu Perrot
# Mathieu Blondel
# License: BSD 3-Clause
from __future__ import print_function
import warnings
import numpy as np
from scipy import linalg
from .extern... | bsd-3-clause |
philrosenfield/TPAGB-calib | tpagb_calibration/sfhs/star_formation_histories.py | 1 | 16329 | from __future__ import print_function
import logging
import os
import matplotlib.pylab as plt
import numpy as np
import ResolvedStellarPops as rsp
from ResolvedStellarPops.convertz import convertz
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
__all__ = ['StarFormationHistories', 'pars... | bsd-3-clause |
victorbergelin/scikit-learn | sklearn/cluster/tests/test_hierarchical.py | 230 | 19795 | """
Several basic tests for hierarchical clustering procedures
"""
# Authors: Vincent Michel, 2010, Gael Varoquaux 2012,
# Matteo Visconti di Oleggio Castello 2014
# License: BSD 3 clause
from tempfile import mkdtemp
import shutil
from functools import partial
import numpy as np
from scipy import sparse
from... | bsd-3-clause |
Dmitry94/Image_Processing | lab_5_fft/run.py | 1 | 1656 | import cv2
import numpy as np
from matplotlib import pyplot as plt
def show_spectrum_and_original(image):
f = cv2.dft(np.float32(image), flags = cv2.DFT_COMPLEX_OUTPUT)
fshift = np.fft.fftshift(f)
magnitude_spectrum = 20*np.log(cv2.magnitude(fshift[:,:,0], fshift[:,:,1]))
plt.subplot(121), plt.imshow... | mit |
cseed/hail | hail/python/hail/backend/spark_backend.py | 1 | 13877 | import pkg_resources
import sys
import os
import json
import socket
import socketserver
from threading import Thread
import py4j
import pyspark
from hail.utils.java import Env, scala_package_object, scala_object
from hail.expr.types import dtype
from hail.expr.table_type import ttable
from hail.expr.matrix_type import... | mit |
jmetzen/scikit-learn | sklearn/svm/setup.py | 321 | 3157 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('svm', parent_package, top_path)
config.add_subpackage('tests')
# Section L... | bsd-3-clause |
tanayz/Kaggle | BCI/btb_gbm.py | 1 | 2694 | __author__ = 'tanay'
## author: phalaris
## kaggle bci challenge gbm benchmark
from __future__ import division
import numpy as np
import pandas as pd
import sklearn.ensemble as ens
train_subs = ['02','06','07','11','12','13','14','16','17','18','20','21','22','23','24','26']
test_subs = ['01','03','04','05','08','09'... | apache-2.0 |
pgmpy/pgmpy | pgmpy/models/BayesianNetwork.py | 2 | 38570 | #!/usr/bin/env python3
import itertools
from collections import defaultdict
import logging
from operator import mul
from functools import reduce
import networkx as nx
import numpy as np
import pandas as pd
from tqdm import tqdm
from joblib import Parallel, delayed
from pgmpy.base import DAG
from pgmpy.factors.discre... | mit |
rsouza01/eos.maxwell.construction | src/eos.maxwell.construction/eos_maxwell_construction.py | 1 | 6444 | #!/usr/bin/python
# eos.maxwell.construction - EoS merger based on the Maxwell Construction
# Copyright (C) 2015 Rodrigo Souza <rsouza01@gmail.com>
# 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 Foundati... | gpl-2.0 |
WangWenjun559/Weiss | summary/sumy/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... | apache-2.0 |
adamgreenhall/scikit-learn | sklearn/metrics/tests/test_common.py | 83 | 41144 | from __future__ import division, print_function
from functools import partial
from itertools import product
import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils.multiclass import type_of_target
fro... | bsd-3-clause |
anntzer/scikit-learn | sklearn/linear_model/tests/test_bayes.py | 8 | 10014 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
from math import log
import numpy as np
from scipy.linalg import pinvh
import pytest
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing im... | bsd-3-clause |
lmallin/coverage_test | python_venv/lib/python2.7/site-packages/pandas/tests/io/parser/converters.py | 7 | 4914 | # -*- coding: utf-8 -*-
"""
Tests column conversion functionality during parsing
for all of the parsers defined in parsers.py
"""
from datetime import datetime
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas._libs.lib import Timestamp
from pandas import DataFrame, ... | mit |
moonbury/pythonanywhere | github/MasteringMLWithScikit-learn/8365OS_04_Codes/ch42.py | 3 | 1763 | import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.metrics import precision_score, recall_score, roc_auc_score, auc, confusion_matrix
import numpy as np
from scipy.sparse i... | gpl-3.0 |
GitYiheng/reinforcement_learning_test | test00_previous_files/save_a_video.py | 1 | 1303 | import gym
from gym import wrappers
import numpy as np
import matplotlib.pyplot as plt
import time
def get_action(s, w):
return 1 if s.dot(w) > 0 else 0
def play_one_episode(env, params):
observation = env.reset()
done = False
t = 0
while not done and t < 1000:
#env.render()
#time.sleep(0.01)
t += 1
act... | mit |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/examples/api/artist_demo.py | 3 | 3442 | """
Show examples of matplotlib artists
http://matplotlib.sourceforge.net/api/artist_api.html
Several examples of standard matplotlib graphics primitives (artists)
are drawn using matplotlib API. Full list of artists and the
documentation is available at
http://matplotlib.sourceforge.net/api/artist_api.html
Copyright... | gpl-2.0 |
mxjl620/scikit-learn | examples/linear_model/plot_multi_task_lasso_support.py | 249 | 2211 | #!/usr/bin/env python
"""
=============================================
Joint feature selection with multi-task Lasso
=============================================
The multi-task lasso allows to fit multiple regression problems
jointly enforcing the selected features to be the same across
tasks. This example simulates... | bsd-3-clause |
dssg/education-college-public | code/etl/pipeline/tableuploader.py | 1 | 6366 | ''' Defines two classes that create tables & load data to our Postgres database.
These are used to create and populate all the tables that are in our database.
'''
import pandas as pd
import psycopg2
import re
import os
import tempfile
from util import cred # load SQL credentials
from util.SQL_helpers import connect_... | mit |
ClimbsRocks/scikit-learn | sklearn/utils/tests/test_shortest_path.py | 303 | 2841 | from collections import defaultdict
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.utils.graph import (graph_shortest_path,
single_source_shortest_path_length)
def floyd_warshall_slow(graph, directed=False):
N = graph.shape[0]
#set nonzer... | bsd-3-clause |
mariusvniekerk/impyla | impala/tests/test_bdf.py | 2 | 2471 | # Copyright 2014 Cloudera 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 writing... | apache-2.0 |
taohaoge/vincent | examples/grouped_bar_examples.py | 11 | 2923 | # -*- coding: utf-8 -*-
"""
Vincent Grouped Bar Examples
"""
#Build a Grouped Bar Chart from scratch
import pandas as pd
from vincent import *
from vincent.core import KeyedList
farm_1 = {'apples': 10, 'berries': 32, 'squash': 21, 'melons': 13, 'corn': 18}
farm_2 = {'apples': 15, 'berries': 40, 'squash': 17, 'melo... | mit |
jiajunshen/partsNet | pnet/rotatable_extensionParts_layer_new.py | 1 | 9794 | from __future__ import division, print_function, absolute_import
import matplotlib as mpl
mpl.use('Agg')
from scipy.special import logit
import numpy as np
import itertools as itr
import amitgroup as ag
from pnet.layer import Layer
from pnet.cyfuncs import index_map_pooling
import pnet
@Layer.register('rotatable_ext... | bsd-3-clause |
xavierwu/scikit-learn | sklearn/mixture/tests/test_dpgmm.py | 261 | 4490 | import unittest
import sys
import numpy as np
from sklearn.mixture import DPGMM, VBGMM
from sklearn.mixture.dpgmm import log_normalize
from sklearn.datasets import make_blobs
from sklearn.utils.testing import assert_array_less, assert_equal
from sklearn.mixture.tests.test_gmm import GMMTester
from sklearn.externals.s... | bsd-3-clause |
person142/scipy | scipy/io/wavfile.py | 3 | 14263 | """
Module to read / write wav files using NumPy arrays
Functions
---------
`read`: Return the sample rate (in samples/sec) and data from a WAV file.
`write`: Write a NumPy array as a WAV file.
"""
import sys
import numpy
import struct
import warnings
__all__ = [
'WavFileWarning',
'read',
'write'
]
c... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.21/_downloads/51837e4937aff2566886026e28ab3651/plot_30_epochs_metadata.py | 9 | 7846 | """
.. _tut-epochs-metadata:
Working with Epoch metadata
===========================
This tutorial shows how to add metadata to :class:`~mne.Epochs` objects, and
how to use :ref:`Pandas query strings <pandas:indexing.query>` to select and
plot epochs based on metadata properties.
.. contents:: Page contents
:loca... | bsd-3-clause |
aarchiba/scipy | scipy/signal/filter_design.py | 3 | 159923 | """Filter design.
"""
from __future__ import division, print_function, absolute_import
import math
import operator
import warnings
import numpy
import numpy as np
from numpy import (atleast_1d, poly, polyval, roots, real, asarray,
resize, pi, absolute, logspace, r_, sqrt, tan, log10,
... | bsd-3-clause |
WarrenWeckesser/scikits-image | doc/examples/plot_seam_carving.py | 8 | 2357 | """
============
Seam Carving
============
This example demonstrates how images can be resized using seam carving [1]_.
Resizing to a new aspect ratio distorts image contents. Seam carving attempts
to resize *without* distortion, by removing regions of an image which are less
important. In this example we are using th... | bsd-3-clause |
chenjun0210/tensorflow | tensorflow/python/client/notebook.py | 109 | 4791 | # Copyright 2015 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 |
TomAugspurger/pandas | pandas/tests/indexes/interval/test_base.py | 1 | 3184 | import numpy as np
import pytest
from pandas import IntervalIndex, Series, date_range
import pandas._testing as tm
from pandas.tests.indexes.common import Base
class TestBase(Base):
"""
Tests specific to the shared common index tests; unrelated tests should be placed
in test_interval.py or the specific t... | bsd-3-clause |
sumspr/scikit-learn | sklearn/datasets/__init__.py | 176 | 3671 | """
The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
from .base import load_diabetes
from .base import load_digits
from .base import load_files
from .base import load_iris
from .... | bsd-3-clause |
numpy/numpy-refactor | numpy/core/code_generators/ufunc_docstrings.py | 57 | 85797 | # Docstrings for generated ufuncs
docdict = {}
def get(name):
return docdict.get(name)
def add_newdoc(place, name, doc):
docdict['.'.join((place, name))] = doc
add_newdoc('numpy.core.umath', 'absolute',
"""
Calculate the absolute value element-wise.
Parameters
----------
x : array_like... | bsd-3-clause |
jmetzen/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 |
zuku1985/scikit-learn | examples/model_selection/plot_validation_curve.py | 141 | 1931 | """
==========================
Plotting Validation Curves
==========================
In this plot you can see the training scores and validation scores of an SVM
for different values of the kernel parameter gamma. For very low values of
gamma, you can see that both the training score and the validation score are
low. ... | bsd-3-clause |
rodluger/planetplanet | docs/conf.py | 1 | 6109 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# planetplanet documentation build configuration file, created by
# sphinx-quickstart on Wed Aug 9 19:49:05 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this... | gpl-3.0 |
spallavolu/scikit-learn | benchmarks/bench_sgd_regression.py | 283 | 5569 | """
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import pylab as pl
import gc
from time import time
from sklearn.linear_model i... | bsd-3-clause |
rna-seq/raisin.recipe.dashboard | raisin/recipe/dashboard/nested.py | 1 | 4970 | import pandas as pd
import csv
import itertools
from types import StringType
MEASURE = None
class Coordinates:
def __init__(self):
self.coordinates = []
def __iter__(self):
for item in self.coordinates:
yield item
def __len__(self):
return len(self.coordinates)
... | gpl-3.0 |
numenta-archive/htmresearch | projects/wavelet_dataAggregation/runDatetimeEncoderExperiment.py | 11 | 8678 | # ----------------------------------------------------------------------
# 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 apply:
#
# This progra... | agpl-3.0 |
mrustl/flopy | examples/Testing/flopy3_CrossSectionExample.py | 3 | 3478 | import sys
import os
import platform
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
import flopy
#Set name of MODFLOW exe
# assumes executable is in users path statement
version = 'mf2005'
exe_name = 'mf2005'
if platform.system() == 'Windows':
exe_name = 'mf2005.exe'
mfexe = exe_name... | bsd-3-clause |
kseetharam/genPolaron | datagen_static_cart.py | 1 | 4937 | import numpy as np
import pandas as pd
import xarray as xr
import Grid
import pf_static_cart
import os
from timeit import default_timer as timer
import sys
if __name__ == "__main__":
start = timer()
# ---- INITIALIZE GRIDS ----
(Lx, Ly, Lz) = (21, 21, 21)
(dx, dy, dz) = (0.375, 0.375, 0.375)
x... | mit |
marcdata/pynba-tfo | tfo_gameqtr.py | 1 | 12485 |
# Look at how and whether teams end up with the last shot of the quarter.
#
# Reconstruct mini-game log, of shots.
# Reshape data to support game-quarter type of analysis.
# ------------------------------------------------------------------------------
# Imports, Load in bigdf dataset.
# ----------------------... | gpl-2.0 |
vitaly-krugl/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/blocking_input.py | 69 | 12119 | """
This provides several classes used for blocking interaction with figure windows:
:class:`BlockingInput`
creates a callable object to retrieve events in a blocking way for interactive sessions
:class:`BlockingKeyMouseInput`
creates a callable object to retrieve key or mouse clicks in a blocking way for int... | agpl-3.0 |
mlperf/training_results_v0.7 | NVIDIA/benchmarks/ssd/implementations/pytorch/visualize.py | 5 | 5886 | # Copyright (c) 2018, NVIDIA CORPORATION. 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 applic... | apache-2.0 |
rayNymous/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 |
chrisburr/scikit-learn | sklearn/linear_model/ridge.py | 3 | 47708 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
impor... | bsd-3-clause |
astrolitterbox/SAMI | utils.py | 1 | 6843 | from __future__ import division
import numpy as np
from astropy.coordinates.distances import Distance
import matplotlib.pyplot as plt
import pyfits
import db
import pyfits
from string import *
from astroML.plotting import hist
from geom import getIncl
def simple_plot(x, y, vel, filename):
fig = plt.figure(figsize=(1... | gpl-2.0 |
meduz/scikit-learn | examples/model_selection/plot_confusion_matrix.py | 63 | 3231 | """
================
Confusion matrix
================
Example of confusion matrix usage to evaluate the quality
of the output of a classifier on the iris data set. The
diagonal elements represent the number of points for which
the predicted label is equal to the true label, while
off-diagonal elements are those that ... | bsd-3-clause |
platinhom/ManualHom | Coding/Python/scipy-html-0.16.1/generated/scipy-stats-gengamma-1.py | 1 | 1124 | from scipy.stats import gengamma
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
# Calculate a few first moments:
a, c = 4.42, 3.12
mean, var, skew, kurt = gengamma.stats(a, c, moments='mvsk')
# Display the probability density function (``pdf``):
x = np.linspace(gengamma.ppf(0.01, a, c),
... | gpl-2.0 |
miha-skalic/ITEKA | qt_design/__init__.py | 1 | 14477 | """
Main window and functions for ITEKA
"""
# windows
from qt_design.main_ui import *
from qt_design.widget_windows import *
import calculations
import qt_design.reaction_plots as reaction_plots
from qt_design.calc_functions import *
import pickle
import sys
import os
import PyQt4.QtCore as qc
QtCore.QLocale.setDef... | gpl-3.0 |
douggeiger/gnuradio | gr-filter/examples/reconstruction.py | 49 | 5015 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your ... | gpl-3.0 |
mlyundin/scikit-learn | sklearn/neighbors/approximate.py | 71 | 22357 | """Approximate nearest neighbor search"""
# Author: Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# Joel Nothman <joel.nothman@gmail.com>
import numpy as np
import warnings
from scipy import sparse
from .base import KNeighborsMixin, RadiusNeighborsMixin
from ..base import BaseEstimator
from ..utils.va... | bsd-3-clause |
amolkahat/pandas | pandas/tests/arrays/sparse/test_array.py | 1 | 41433 | from pandas.compat import range
import re
import operator
import pytest
import warnings
from numpy import nan
import numpy as np
import pandas as pd
from pandas.core.sparse.api import SparseArray, SparseSeries, SparseDtype
from pandas._libs.sparse import IntIndex
from pandas.util.testing import assert_almost_equal
i... | bsd-3-clause |
michaelaye/planet4 | planet4/dbscan.py | 1 | 22144 | #!/usr/bin/env python
import logging
import math
from itertools import product
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyaml
import seaborn as sns
from scipy.stats import circmean, circstd
from sklearn.cluster import DBSCAN
from . import io, markings
log... | isc |
jaantollander/Fourier-Legendre | src/analysis/convergence.py | 8 | 1917 | # coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numba
import numpy as np
import pandas
from numba import float64, int64
@numba.jit(float64(float64, float64, float64, float64), nopython=True, cac... | mit |
nmayorov/scikit-learn | sklearn/ensemble/gradient_boosting.py | 18 | 71095 | """Gradient Boosted Regression Trees
This module contains methods for fitting gradient boosted regression trees for
both classification and regression.
The module structure is the following:
- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regressio... | bsd-3-clause |
istellartech/OpenTsiolkovsky | bin/make_plot.py | 1 | 18051 | # -*- coding: utf-8 -*-
# Copyright (c) 2017 Interstellar Technologies Inc. All Rights Reserved.
# Authors : Takahiro Inagawa
#
# Lisence : MIT Lisence
#
# 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 th... | mit |
brunojulia/ultracoldUB | brightsolitons/Alejandro/bs_v1.py | 1 | 14708 |
# coding: utf-8
# ## FFT solver for 1D Gross-Pitaevski equation
# We look for the complex function $\psi(x)$ satisfying the GP equation
#
# $ i\partial_t \psi = \frac{1}{2}(-i\partial_x - \Omega)^2\psi+ V(x)\psi + g|\psi|^2\psi $,
#
# with periodic boundary conditions.
#
# Integration: pseudospectral method with ... | gpl-3.0 |
PatrickOReilly/scikit-learn | examples/linear_model/plot_sgd_separating_hyperplane.py | 84 | 1221 | """
=========================================
SGD: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.
"""
print(__doc__)
import numpy as n... | bsd-3-clause |
tmills/neural-assertion | scripts/keras/singletask/assertion_train-and-package.py | 1 | 2599 | #!/usr/bin/env python
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
#from sklearn.datasets import load_svmlight_file
import sklearn as sk
import sklearn.cross_validation
import numpy as np
from ctakesneural.io i... | apache-2.0 |
pgroth/independence-indicators | Temporal-Coauthor-Networks/vincent/examples/stacked_bar_examples.py | 11 | 2691 | # -*- coding: utf-8 -*-
"""
Vincent Stacked Bar Examples
"""
#Build a Stacked Bar Chart from scratch
import pandas as pd
from vincent import *
farm_1 = {'apples': 10, 'berries': 32, 'squash': 21, 'melons': 13, 'corn': 18}
farm_2 = {'apples': 15, 'berries': 40, 'squash': 17, 'melons': 10, 'corn': 22}
farm_3 = {'app... | gpl-2.0 |
ctogle/dilapidator | src/dilap/BROKEN/graph/graph.py | 1 | 15113 | import dilap.core.base as db
import dilap.core.tools as dpr
import dilap.core.vector as dpv
import dilap.core.pointset as dps
#import dilap.core.graphnode as gnd
#import dilap.core.graphedge as geg
import dilap.mesh.tools as dtl
import matplotlib.pyplot as plt
import pdb
class geometry(db.base):
def radius(se... | mit |
sunlightlabs/fcc-net-neutrality-comments | scripts/feature_agglomeration.py | 1 | 5336 | import sys
import os
import json
import csv
from glob import glob
sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
filename='log/feature_agglomeration.log', filemode='a',
... | mit |
NelisVerhoef/scikit-learn | sklearn/qda.py | 140 | 7682 | """
Quadratic Discriminant Analysis
"""
# Author: Matthieu Perrot <matthieu.perrot@gmail.com>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import BaseEstimator, ClassifierMixin
from .externals.six.moves import xrange
from .utils import check_array, check_X_y
from .utils.validation import ... | bsd-3-clause |
irhete/predictive-monitoring-benchmark | transformers/LastStateTransformer.py | 1 | 1601 | from sklearn.base import TransformerMixin
import pandas as pd
from time import time
class LastStateTransformer(TransformerMixin):
def __init__(self, case_id_col, cat_cols, num_cols, fillna=True):
self.case_id_col = case_id_col
self.cat_cols = cat_cols
self.num_cols = num_cols
s... | apache-2.0 |
iproduct/course-social-robotics | 11-dnn-keras/venv/Lib/site-packages/matplotlib/tests/test_mathtext.py | 1 | 14629 | import io
import os
import re
import numpy as np
import pytest
import matplotlib as mpl
from matplotlib.testing.decorators import check_figures_equal, image_comparison
import matplotlib.pyplot as plt
from matplotlib import mathtext
math_tests = [
r'$a+b+\dot s+\dot{s}+\ldots$',
r'$x \doteq y$',
r'\$100.... | gpl-2.0 |
sprax/python | txt/sim_tfidf_nltk.py | 1 | 21467 | #!/usr/bin/env python3
'''Text similarity (between words, phrases, or short sentences) using NLTK'''
import heapq
import string
import time
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
import pdb
import qa_csv
import text_fio
STEMMER = nltk.stem.porter.PorterStemmer()
TRANS_NO_PUNCT = str.m... | lgpl-3.0 |
ppp2006/runbot_number0 | qbo_stereo_anaglyph/hrl_lib/src/hrl_lib/geometry.py | 4 | 5946 | #
# Copyright (c) 2009, Georgia Tech Research Corporation
# 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 above copyright
# notice, thi... | lgpl-2.1 |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/tree/export.py | 35 | 16873 | """
This module defines export functions for decision trees.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Trevor... | mit |
TIGER-NET/Processing-SWAT | OSFWF_Assimilate_a.py | 2 | 4123 | """
***************************************************************************
OSFWF_Assimilate_a.py
-------------------------------------
Copyright (C) 2014 TIGER-NET (www.tiger-net.org)
***************************************************************************
* This plugin is part of the Water Obser... | gpl-3.0 |
gauthiier/mailinglists | analyse.py | 1 | 7237 | import os
# matplot view/windows
import matplotlib
matplotlib.interactive(True)
# pd display
import pandas as pd
pd.set_option('display.max_colwidth', 100)
from analysis.archive import Archive
from analysis.query import Query
from analysis.plot import Plot
import analysis.format
# spectre: slategrey
# nettime: red... | gpl-3.0 |
ouedraog/quantfi-project | base/views.py | 1 | 1557 | """ Views for the base application """
from django.shortcuts import render
import QSTK.qstkutil.qsdateutil as du
import QSTK.qstkutil.tsutil as tsu
import QSTK.qstkutil.DataAccess as da
# Third Party Imports
import datetime as dt
import pandas as pd
def home(request):
""" Default view for the root """
return ... | bsd-3-clause |
wackymaster/QTClock | Libraries/matplotlib/testing/jpl_units/StrConverter.py | 8 | 5340 | #===========================================================================
#
# StrConverter
#
#===========================================================================
"""StrConverter module containing class StrConverter."""
#===========================================================================
# Place al... | mit |
jkarnows/scikit-learn | sklearn/metrics/classification.py | 42 | 65685 | """Metrics to assess performance on classification task given classe prediction
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gram... | bsd-3-clause |
anurag313/scikit-learn | sklearn/linear_model/tests/test_logistic.py | 23 | 27579 | import numpy as np
import scipy.sparse as sp
from scipy import linalg, optimize, sparse
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.util... | bsd-3-clause |
ilo10/scikit-learn | examples/ensemble/plot_partial_dependence.py | 249 | 4456 | """
========================
Partial Dependence Plots
========================
Partial dependence plots show the dependence between the target function [1]_
and a set of 'target' features, marginalizing over the
values of all other features (the complement features). Due to the limits
of human perception the size of t... | bsd-3-clause |
maropu/spark | python/pyspark/pandas/usage_logging/usage_logger.py | 14 | 4949 | #
# 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 |
holdenk/spark | python/pyspark/sql/pandas/serializers.py | 23 | 12308 | #
# 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 |
yyjiang/scikit-learn | sklearn/tests/test_learning_curve.py | 225 | 10791 | # Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import warnings
from sklearn.base import BaseEstimator
from sklearn.learning_curve import learning_curve, validation_curve
from sklearn.u... | bsd-3-clause |
gauthiier/mailinglists | analysis/archive.py | 1 | 4681 | import numpy as np
import pandas as pd
import email, email.parser
import os, datetime, json, gzip, re
import analysis.util
import analysis.query
import search.archive ## circular...
def filter_date(msg, archive_name):
time_tz = analysis.util.format_date(msg, archive_name)
if not time_tz:
return None
dt = dat... | gpl-3.0 |
nschloe/quadpy | tests/test_c3.py | 1 | 1869 | import numpy as np
import orthopy
import pytest
from helpers import find_best_scheme
from matplotlib import pyplot as plt
import quadpy
@pytest.mark.parametrize("scheme", quadpy.c3.schemes.values())
def test_scheme(scheme, print_degree=False):
scheme = scheme()
assert scheme.points.dtype in [np.float64, np.... | mit |
tasoc/photometry | run_ffimovie.py | 1 | 17717 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Create movie of FFIs and extracted backgrounds.
This program will create a MP4 movie file with an animation of the extracted
backgrounds and flags from an HDF5 file created by the photometry pipeline.
This program requires the program `FFmpeg <https://ffmpeg.org/>`_ ... | gpl-3.0 |
qianfengzh/ML-source-code | algorithms/treeExplore.py | 1 | 2429 | #coding=utf-8
'''
用于构建树管理器界面的 Tkinter 小部件
'''
import numpy as np
from Tkinter import *
import reTrees
def reDraw(tolS, tolN):
pass
def drawNewTree():
pass
root = Tk()
Label(root, text='Plot Place Holder').grid(row=0, columnspan=3)
Label(root, text='tolN').grid(row=1, column=0)
tolNentry = Entry(root)
tolNen... | gpl-2.0 |
c-benko/Molecular_Alignment | Align_SEq_Obj_RK4/ffa_sim.py | 1 | 2110 | import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import ode
import time
# import my classes
from laser import *
from molecule import *
from integrator import *
from const import *
from expectation_values import *
# close old plots.
# plt.close('all')
class ffa_sim:
'''
simulator of fi... | mit |
ishanic/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 |
fdeheeger/mpld3 | mpld3/__init__.py | 20 | 1109 | """
Interactive D3 rendering of matplotlib images
=============================================
Functions: General Use
----------------------
:func:`fig_to_html`
convert a figure to an html string
:func:`fig_to_dict`
convert a figure to a dictionary representation
:func:`show`
launch a web server to view... | bsd-3-clause |
cni/MRS | MRS/version.py | 2 | 1899 | """MRS version/release information"""
# Format expected by setup.py and doc/source/conf.py: string of form "X.Y.Z"
_version_major = 0
_version_minor = 1
_version_micro = '' # use '' for first of series, number for 1 and above
_version_extra = 'dev'
_version_extra = '' # Uncomment this for full releases
# Construct ... | mit |
probcomp/cgpm | src/factor/factor.py | 1 | 13620 | # -*- coding: utf-8 -*-
# Copyright (c) 2015-2016 MIT Probabilistic Computing Project
# 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
# Unles... | apache-2.0 |
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