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 |
|---|---|---|---|---|---|
Windy-Ground/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
open-austin/construction-permits | permits/permits.py | 1 | 4110 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# todo:
# throw out matches that match citycenter
# extra column before geocoded fields
# argparser for start_date, end_date
import csv
import logging
from StringIO import StringIO
import time
import arrow
import geocoder
import requests
import github
from html2csv impo... | unlicense |
elcritch/CuteMark | CuteMark/RendererPanel.py | 1 | 3423 | #!/usr/bin/env python3
# Import PySide classes
import sys, collections, json, tabulate, shutil
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from PyQt5.QtWebKitWidgets import QWebView
from PyQt5.QtWebKit import QWebSettings
from PyQt5.QtNetwork import QNetworkRequest
Signal = pyq... | mit |
amozie/amozie | testzie/stock_rl/stock_gym_test.py | 1 | 1393 | from keras.applications.vgg16 import VGG16
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential, Model
from keras.layers import Dense, Activation, Input, Dropout, GlobalAveragePooling2D, \
Flatten, RepeatVector, Permute, Reshape, GlobalMaxPooling2D
from keras.datasets import mnist
... | apache-2.0 |
qifeigit/scikit-learn | examples/exercises/plot_cv_diabetes.py | 231 | 2527 | """
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================
A tutorial exercise which uses cross-validation with linear models.
This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of ... | bsd-3-clause |
dsullivan7/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 387 | 2060 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
Erotemic/hotspotter | hsviz/viz.py | 1 | 37764 | from __future__ import division, print_function
from hscom import __common__
(print, print_, print_on, print_off, rrr, profile, printDBG) = \
__common__.init(__name__, '[viz]', DEBUG=False)
import matplotlib
matplotlib.use('Qt4Agg')
#import re
import warnings
# Scientific
import numpy as np
# Hotspotter
import draw... | apache-2.0 |
pratapvardhan/scikit-learn | examples/manifold/plot_manifold_sphere.py | 16 | 5103 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=============================================
Manifold Learning methods on a severed sphere
=============================================
An application of the different :ref:`manifold` techniques
on a spherical data-set. Here one can see the use of
dimensionality reducti... | bsd-3-clause |
harisbal/pandas | pandas/core/computation/expr.py | 2 | 26587 | """:func:`~pandas.eval` parsers
"""
import ast
from functools import partial
import tokenize
import numpy as np
from pandas.compat import StringIO, lmap, reduce, string_types, zip
import pandas as pd
from pandas import compat
from pandas.core import common as com
from pandas.core.base import StringMixin
from pandas... | bsd-3-clause |
kjyv/FloBaRoID | excitation/optimizer.py | 2 | 25323 | from __future__ import division
from __future__ import print_function
from builtins import range
from builtins import object
from typing import List, Tuple, Dict
import sys
import random
import numpy as np
import numpy.linalg as la
import matplotlib
import matplotlib.pyplot as plt
from distutils.version import LooseV... | lgpl-3.0 |
stulp/dmpbbo | demos_cpp/dynamicalsystems/demoExponentialSystemWrapper.py | 1 | 2205 | # This file is part of DmpBbo, a set of libraries and programs for the
# black-box optimization of dynamical movement primitives.
# Copyright (C) 2014 Freek Stulp, ENSTA-ParisTech
#
# DmpBbo is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as publis... | lgpl-2.1 |
cl4rke/scikit-learn | examples/ensemble/plot_gradient_boosting_oob.py | 230 | 4762 | """
======================================
Gradient Boosting Out-of-Bag estimates
======================================
Out-of-bag (OOB) estimates can be a useful heuristic to estimate
the "optimal" number of boosting iterations.
OOB estimates are almost identical to cross-validation estimates but
they can be compute... | bsd-3-clause |
Rossonero/bmlswp | ch05/classify.py | 20 | 8239 | # 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
import time
start_time = time.time()
import numpy as np
from sklearn.metrics import classification_re... | mit |
ran5515/DeepDecision | tensorflow/examples/learn/iris_custom_model.py | 37 | 3651 | # 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 |
heistermann/wradvis | wradvis/mplcanvas.py | 1 | 3592 | # -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2016, wradlib Development Team. All Rights Reserved.
# Distributed under the MIT License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------... | mit |
wathen/PhD | MHD/FEniCS/FieldSplit/LSC/Convergence/NSpicard3Dnew.py | 1 | 11197 |
#!/opt/local/bin/python
from dolfin import *
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
# from MatrixOperations import *
import numpy as np
#import matplotlib.pylab as plt
import os
import PETScIO as IO
import time
import common
import CheckPetsc4py as CP
import NSprecond
from scip... | mit |
RafaelCosman/pybrain | pybrain/rl/environments/cartpole/cartpole.py | 24 | 4817 | __author__ = 'Thomas Rueckstiess, ruecksti@in.tum.de'
from matplotlib.mlab import rk4
from math import sin, cos
import time
from scipy import eye, matrix, random, asarray
from pybrain.rl.environments.graphical import GraphicalEnvironment
class CartPoleEnvironment(GraphicalEnvironment):
""" This environment impl... | bsd-3-clause |
sightmachine/SimpleCV | SimpleCV/examples/util/ColorCube.py | 13 | 1901 | from SimpleCV import Image, Camera, Display, Color
import pygame as pg
import numpy as np
from pylab import *
from mpl_toolkits.mplot3d import axes3d
from matplotlib.backends.backend_agg import FigureCanvasAgg
import cv2
bins = 8
#precompute
idxs = []
colors = []
offset = bins/2
skip = 255/bins
for x in range(0,bins):... | bsd-3-clause |
Yongliangdu/ThinkStats2 | code/hinc.py | 67 | 1494 | """This file contains code used in "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import numpy as np
import pandas
import thinkplot
import thinkstats2
def Clean(s):... | gpl-3.0 |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/sklearn/exceptions.py | 14 | 4945 | """
The :mod:`sklearn.exceptions` module includes all custom warnings and error
classes used across scikit-learn.
"""
__all__ = ['NotFittedError',
'ChangedBehaviorWarning',
'ConvergenceWarning',
'DataConversionWarning',
'DataDimensionalityWarning',
'EfficiencyWarn... | mit |
cycomachead/info290 | lab6/michael_subm_1_2.py | 2 | 2120 | import numpy as np
import pandas as pd
from sklearn import ensemble
train = pd.read_csv("train.csv")
torig = pd.read_csv("test.csv")
test = pd.read_csv("test.csv")
rf = ensemble.RandomForestClassifier()
# Convert Male / Female ==> 0 / 1
train = train.replace('male', 0)
train = train.replace('female', 1)
test = test.... | bsd-2-clause |
rgommers/statsmodels | statsmodels/sandbox/examples/ex_mixed_lls_timecorr.py | 34 | 7824 | # -*- coding: utf-8 -*-
"""Example using OneWayMixed with within group intertemporal correlation
Created on Sat Dec 03 10:15:55 2011
Author: Josef Perktold
This example constructs a linear model with individual specific random
effects, and uses OneWayMixed to estimate it.
This is a variation on ex_mixed_lls_0.py.
... | bsd-3-clause |
m3rik/nn | CNTK/Assignment1_kinship/TODO1_kinship.py | 1 | 5242 | from Assignment1_kinship.kinship_data import kinship_dataset
from cntk import Trainer, cntk_device, StreamConfiguration, learning_rate_schedule, UnitType
from cntk.utils import *
from cntk.device import cpu, set_default_device
from cntk.learner import *
from cntk.ops import *
from cntk.tensor import *
from cntk.axis i... | apache-2.0 |
Cophy08/ggplot | ggplot/stats/stat_hline.py | 12 | 1317 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pandas as pd
from ggplot.utils import pop, make_iterable, make_iterable_ntimes
from ggplot.utils.exceptions import GgplotError
from .stat import stat
class stat_hline(stat):
DEFAULT_PARAMS = {'geom... | bsd-2-clause |
AnasGhrab/scikit-learn | setup.py | 143 | 7364 | #! /usr/bin/env python
#
# Copyright (C) 2007-2009 Cournapeau David <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# License: 3-clause BSD
descr = """A set of python modules for machine learning and data mining"""
import sys
import os
import shutil
from distutils.command.clean ... | bsd-3-clause |
michigraber/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 225 | 5719 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
tequa/ammisoft | ammimain/WinPython-64bit-2.7.13.1Zero/python-2.7.13.amd64/Lib/site-packages/matplotlib/backends/backend_ps.py | 10 | 61530 | """
A PostScript backend, which can produce both PostScript .ps and .eps
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import StringIO
import glob, math, os, shutil, sys, time
def _fn_name(): return sys._getframe(1).f_code.c... | bsd-3-clause |
pypyrus/pypyrus | jupyter/config/jupyter_notebook_config.py | 1 | 19505 | #--- nbextensions configuration ---
from jupyter_core.paths import jupyter_config_dir, jupyter_data_dir
import os
import sys
# nbextensions #
#data_dir = jupyter_data_dir()
data_dir = os.path.join(os.getcwd(), 'jupyter', 'data')
sys.path.append(os.path.join(data_dir, 'extensions'))
c = get_config()
c.NotebookApp.ser... | gpl-2.0 |
mxlei01/healthcareai-py | healthcareai/common/filters.py | 4 | 3444 | """Filters
This module contains filters for preprocessing data. Most operate on DataFrames and are named appropriately.
"""
from sklearn.base import TransformerMixin
from pandas.core.frame import DataFrame
from healthcareai.common.healthcareai_error import HealthcareAIError
def validate_dataframe_input(possible_da... | mit |
cbertinato/pandas | pandas/tests/io/parser/test_read_fwf.py | 1 | 18906 | """
Tests the 'read_fwf' function in parsers.py. This
test suite is independent of the others because the
engine is set to 'python-fwf' internally.
"""
from datetime import datetime
from io import BytesIO, StringIO
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, DatetimeIndex
impor... | bsd-3-clause |
tomlof/scikit-learn | sklearn/utils/graph.py | 24 | 6326 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
paladin74/neural-network-animation | matplotlib/tests/test_colorbar.py | 9 | 9374 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import numpy as np
from numpy import ma
import matplotlib
from matplotlib.testing.decorators import image_comparison, cleanup
import matplotlib.pyplot as plt
from matplotlib import rcParams
from mat... | mit |
Haleyo/spark-tk | python/sparktk/frame/ops/histogram.py | 13 | 4898 | # 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 |
dsquareindia/scikit-learn | sklearn/cluster/birch.py | 23 | 23648 | # Authors: Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Joel Nothman <joel.nothman@gmail.com>
# License: BSD 3 clause
from __future__ import division
import warnings
import numpy as np
from scipy import sparse
from math import sqrt
fro... | bsd-3-clause |
boada/HETDEXCluster | legacy/stats/rejectOutliers_group.py | 3 | 5612 | import glob
import pandas as pd
import pylab as pyl
from astLib import astCoords as aco
from astLib import astStats as ast
from astLib import astCalc as aca
c = 2.99E5 # speed of light in km/s
def parseResults(files):
''' Reads all of the results files and puts them into a list with the
results. Returns field... | mit |
JeffAbrahamson/UNA_compta | una_canonical.py | 2 | 6371 | #!/usr/bin/python3
"""Convert the EBP or gnucash export to a canonical form.
The input is a text export of the current year's books.
"""
import argparse
import datetime
import numpy as np
import pandas as pd
def get_data_ebp_v19(book_filename):
"""Fetch book data, return as a pandas DataFrame.
Assume input... | gpl-3.0 |
CVML/scikit-learn | sklearn/ensemble/gradient_boosting.py | 126 | 65552 | """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 |
fengzhyuan/scikit-learn | sklearn/preprocessing/tests/test_label.py | 156 | 17626 | import numpy as np
from scipy.sparse import issparse
from scipy.sparse import coo_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import dok_matrix
from scipy.sparse import lil_matrix
from sklearn.utils.multiclass import type_of_target
from sklearn.utils.testing impor... | bsd-3-clause |
infinnovation/piwall-cvtools | piwall.py | 1 | 52153 | #!/usr/bin/env python
'''
Prototype .piwall generator to find monitor geometry from photograph of a piwall.
Can operate sequentially on a set of photos to compare and contrast results.
Commit Summary
-
Repeat test of the basic rectangle matching (c055d3a) against red backgrounds.
-
c055d3a Basic demonstra... | gpl-3.0 |
kmather73/ggplot | ggplot/scales/scale_y_continuous.py | 12 | 1202 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from .scale import scale
from copy import deepcopy
from matplotlib.pyplot import FuncFormatter
dollar = lambda x, pos: '$%1.2f' % x
currency = dollar
comma = lambda x, pos: '{:0,d}'.format(int(x))
millions... | bsd-2-clause |
eranchetz/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/rcsetup.py | 69 | 23344 | """
The rcsetup module contains the default values and the validation code for
customization using matplotlib's rc settings.
Each rc setting is assigned a default value and a function used to validate any
attempted changes to that setting. The default values and validation functions
are defined in the rcsetup module, ... | agpl-3.0 |
ibis-project/ibis | ibis/backends/dask/tests/execution/test_operations.py | 1 | 30603 | import operator
from operator import methodcaller
import dask.array as da
import dask.dataframe as dd
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
from dask.dataframe.utils import tm
import ibis
import ibis.expr.datatypes as dt
from ...execution import execute
pytestmark = pytest... | apache-2.0 |
vivekmishra1991/scikit-learn | sklearn/linear_model/passive_aggressive.py | 97 | 10879 | # Authors: Rob Zinkov, Mathieu Blondel
# License: BSD 3 clause
from .stochastic_gradient import BaseSGDClassifier
from .stochastic_gradient import BaseSGDRegressor
from .stochastic_gradient import DEFAULT_EPSILON
class PassiveAggressiveClassifier(BaseSGDClassifier):
"""Passive Aggressive Classifier
Read mor... | bsd-3-clause |
ceb8/astroquery | astroquery/hips2fits/core.py | 1 | 17849 | # -*- coding: utf-8 -*
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from ..query import BaseQuery
from ..utils.class_or_instance import class_or_instance
from ..utils import async_to_sync
from . import conf
from astropy import wcs
__all__ = ['hips2fits', 'hips2fitsClass']
__doctest_skip__ = ['hip... | bsd-3-clause |
zihua/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 77 | 3825 | # 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 |
ethen8181/machine-learning | big_data/sparkml/get_data.py | 1 | 1071 | import os
import re
import requests
import pandas as pd
def main():
file_path = 'adult.csv'
if not os.path.isfile(file_path):
def chunks(input_list, n_chunk):
"""take a list and break it up into n-size chunks"""
for i in range(0, len(input_list), n_chunk):
yield... | mit |
tiamat-studios/pandascore-python | tests/test_lol.py | 1 | 8074 | import unittest
import responses
from pandascore import lol
from tests.base_test import BaseTest
class TestLeagueOfLegends(BaseTest):
def setUp(self):
super(TestLeagueOfLegends, self).setUp()
self.lol = lol.LeagueOfLegends(access_token=self.access_token)
@responses.activate
def test_get_... | mit |
jreback/pandas | pandas/tests/io/test_feather.py | 3 | 6836 | """ test feather-format compat """
from distutils.version import LooseVersion
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
from pandas.io.feather_format import read_feather, to_feather # isort:skip
pyarrow = pytest.importorskip("pyarro... | bsd-3-clause |
great-expectations/great_expectations | examples/expectations/column_map_expectation_template.py | 1 | 8562 | import json
#!!! This giant block of imports should be something simpler, such as:
# from great_exepectations.helpers.expectation_creation import *
from great_expectations.execution_engine import (
PandasExecutionEngine,
SparkDFExecutionEngine,
SqlAlchemyExecutionEngine,
)
from great_expectations.expectati... | apache-2.0 |
djgagne/scikit-learn | sklearn/metrics/regression.py | 175 | 16953 | """Metrics to assess performance on regression task
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.gramfort@inria.fr>
# Ma... | bsd-3-clause |
khkaminska/bokeh | examples/plotting/file/boxplot.py | 43 | 2269 | import numpy as np
import pandas as pd
from bokeh.plotting import figure, show, output_file
# Generate some synthetic time series for six different categories
cats = list("abcdef")
yy = np.random.randn(2000)
g = np.random.choice(cats, 2000)
for i, l in enumerate(cats):
yy[g == l] += i // 2
df = pd.DataFrame(dict(s... | bsd-3-clause |
johannfaouzi/pyts | pyts/approximation/sfa.py | 1 | 6684 | """Code for Symbolic Fourier Approximation."""
# Author: Johann Faouzi <johann.faouzi@gmail.com>
# License: BSD-3-Clause
from sklearn.base import BaseEstimator
from sklearn.pipeline import Pipeline
from sklearn.utils.validation import check_is_fitted
from .dft import DiscreteFourierTransform
from .mcb import Multiple... | bsd-3-clause |
chaluemwut/fbserver | venv/lib/python2.7/site-packages/sklearn/feature_extraction/tests/test_dict_vectorizer.py | 8 | 3217 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
assert_false, assert_true)
from skle... | apache-2.0 |
hollabaq86/haikuna-matata | env/lib/python2.7/site-packages/nltk/tokenize/texttiling.py | 7 | 16850 | # Natural Language Toolkit: TextTiling
#
# Copyright (C) 2001-2017 NLTK Project
# Author: George Boutsioukis
#
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
import re
import math
try:
import numpy
except ImportError:
pass
from nltk.tokenize.api import TokenizerI
BLOCK_COMPARISON, VOCA... | mit |
mfjb/scikit-learn | examples/missing_values.py | 233 | 3056 | """
======================================================
Imputing missing values before building an estimator
======================================================
This example shows that imputing the missing values can give better results
than discarding the samples containing any missing value.
Imputing does not ... | bsd-3-clause |
atantet/ergoPack | example/plot/plotSpectrumUnfold.py | 1 | 15008 | import os
import numpy as np
import matplotlib.pyplot as plt
import pylibconfig2
import ergoPlot
ergoPlot.dpi = 2000
#configFile = '../cfg/OU2d.cfg'
#compName1 = 'x_1'
#compName2 = 'x_2'
#configFile = '../cfg/Battisti1989.cfg'
configFile = '../cfg/Suarez1988.cfg'
compName1 = r'y'
compName2 = r'y'
#configFile = '../cf... | gpl-3.0 |
OpenElectronicsLab/eeg-mouse | src/generate_filter_coef.py | 1 | 2331 | #!/usr/bin/python
import numpy as np
from scipy import signal
from matplotlib import pyplot as plt
nyquest_freq = 250./2;
# 7-14 Hz elliptic bandpass filter
x_filter = signal.iirdesign(
wp = [7./nyquest_freq, 14./nyquest_freq],
ws = [4./nyquest_freq, 20./nyquest_freq],
gstop=40, gpass=3, ftype='ellip'
)
... | gpl-3.0 |
dandanvidi/in-vivo-enzyme-kinetics | scripts/central_metabolism_vs_biosynthesis.py | 3 | 3095 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 1 10:48:23 2016
@author: dan
"""
from sklearn.cluster import KMeans
from capacity_usage import CAPACITY_USAGE
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy.stats import ranksums
def despine(ax, fontsize=15):
ax.tick_params(right=0... | mit |
tjd08a/open-catalog-generator | scripts/metrics.py | 3 | 13286 | #!/usr/bin/python
#James Tobat, 2014
import json
import sys
import time
#import matplotlib.pyplot as plt
import csv
import os.path
# Command line arguments and global constants
active_content_file = sys.argv[1]
deployed_content_file = sys.argv[2]
data_dir = sys.argv[3]
metric_log_dir = sys.argv[4]
date = time.strftime... | apache-2.0 |
samcervantes/scikit-learn-tutorials | statistical-learning-for-scientific-data-processing/setting-estimator-object/reshaping-dataset.py | 1 | 1692 | """
Scikit-learn requires an input dataset consisting of a 2D array of samples & features.
The Digits dataset consists of a 3D array (1797, 8, 8) so it must first be transformed (shaped)
into a 2D array.
"""
from sklearn import datasets
from sklearn import svm
digits = datasets.load_digits()
print("The shape of the d... | bsd-3-clause |
gifford-lab/bcbio-nextgen | bcbio/bam/coverage.py | 7 | 6031 | """
calculate coverage across a list of regions
"""
import os
import six
import matplotlib as mpl
mpl.use('Agg', force=True)
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib
import seaborn as sns
import pandas as pd
import pybedtools
from bcbio.utils import rbind,... | mit |
gerritholl/pyatmlab | pyatmlab/physics.py | 1 | 36969 | #!/usr/bin/env python
# coding: utf-8
"""Various small physics functions
Mostly obtained from PyARTS
"""
import logging
import numbers
import datetime
import calendar
import itertools
import numpy
import scipy.interpolate
import matplotlib
import matplotlib.dates
import numexpr
import pyproj
import pint
from .c... | bsd-3-clause |
chedeti/Bumpiness_Detector | aggregateBumpiness.py | 2 | 2574 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.font_manager
import sys
import collections
import argparse
## Get user inputs and check if they are entered correctly
parser = argparse.ArgumentParser()
parser.add_argument("--featureFile", default="MBHxFeatures.npy")
parser.add_argument("--outputFi... | mit |
zhenwendai/RGP | autoreg/benchmark/run.py | 1 | 2773 | # Copyright (c) 2015, Zhenwen Dai
# Licensed under the BSD 3-clause license (see LICENSE.txt)
from __future__ import print_function
from evaluation import RMSE
from methods import Autoreg_onelayer, Autoreg_onelayer_bfgs
from tasks import all_tasks
from outputs import PickleOutput, CSV_Summary
import numpy as np
import... | bsd-3-clause |
azjps/bokeh | examples/charts/server/interactive_excel.py | 6 | 3225 | import xlwings as xw
import pandas as pd
from pandas.util.testing import assert_frame_equal
from bokeh.client import push_session
from bokeh.charts import Line, Bar
from bokeh.charts.operations import blend
from bokeh.io import curdoc
from bokeh.layouts import row, column
from bokeh.models import Paragraph
wb = xw.Wo... | bsd-3-clause |
HIPS/Kayak | examples/poisson_glm.py | 3 | 1224 | import numpy as np
import numpy.random as npr
import matplotlib.pyplot as plt
import sys
sys.path.append('..')
import kayak
N = 10000
D = 5
P = 1
learn = 0.00001
batch_size = 500
# Random inputs.
X = npr.randn(N,D)
true_W = npr.randn(D,P)
lam = np.exp(np.dot(X, true_W))
Y = npr.poisson(lam)
kyk_batcher = k... | mit |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/io/common.py | 7 | 14893 | """Common IO api utilities"""
import sys
import os
import csv
import codecs
import mmap
import zipfile
from contextlib import contextmanager, closing
from pandas.compat import StringIO, BytesIO, string_types, text_type
from pandas import compat
from pandas.formats.printing import pprint_thing
from pandas.core.common ... | apache-2.0 |
skyglobe/geotop | tests/compare_version.py | 2 | 9624 | #!/usr/bin/env python
# -*- coding: utf-8 -*-#
# @(#)test_runner.py.in
#
#
# Copyright (C) 2013, GC3, University of Zurich. All rights reserved.
#
#
# 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 Foundatio... | gpl-3.0 |
wazeerzulfikar/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | 24 | 14995 | import numpy as np
from numpy.testing import assert_approx_equal
from sklearn.utils.testing import (assert_equal, assert_array_almost_equal,
assert_array_equal, assert_true,
assert_raise_message)
from sklearn.datasets import load_linnerud
from sklea... | bsd-3-clause |
open2c/cooltools | tests/test_dotfinder_chunking.py | 1 | 10245 | # create a test for the chunking versions of 'get_adjusted_expected_tile_some_nans':
import numpy as np
import pandas as pd
import os.path as op
from cooltools import dotfinder
from cooltools.lib.numutils import LazyToeplitz
# adjust the path for data:
testdir = op.realpath(op.dirname(__file__))
# mock input data... | mit |
schevalier/Whetlab-Python-Client | examples/tutorial_example.py | 1 | 1465 |
from sklearn.datasets import fetch_mldata
data_set = fetch_mldata('yahoo-web-directory-topics')
train_set = (data_set['data'][:1000],data_set['target'][:1000])
validation_set = (data_set['data'][1000:],data_set['target'][1000:])
parameters = { 'C':{'min':0.01, 'max':1000.0,'type':'float'},
'degree':{'... | bsd-3-clause |
pravsripad/mne-python | examples/preprocessing/plot_define_target_events.py | 29 | 3376 | """
============================================================
Define target events based on time lag, plot evoked response
============================================================
This script shows how to define higher order events based on
time lag between reference and target events. For
illustration, we will... | bsd-3-clause |
mhallsmoore/qstrader | tests/unit/system/rebalance/test_end_of_month_rebalance.py | 1 | 1449 | import pandas as pd
import pytest
import pytz
from qstrader.system.rebalance.end_of_month import EndOfMonthRebalance
@pytest.mark.parametrize(
"start_date,end_date,pre_market,expected_dates,expected_time",
[
(
'2020-03-11', '2020-12-31', False, [
'2020-03-31', '2020-04-30'... | mit |
Abraxos/clustering_tsp_solver | tsp.py | 1 | 27673 | from re import compile
from math import sqrt
from collections import defaultdict
from sys import maxsize
from time import clock
from itertools import product, permutations
from sklearn.cluster import KMeans
from sklearn.cluster import Birch
from sklearn.cluster import DBSCAN
from sklearn.cluster import AgglomerativeCl... | gpl-3.0 |
fbagirov/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
a... | bsd-3-clause |
DanOlds/CATS | cats_v1.py | 1 | 64341 | from __future__ import print_function
import sys
from PyQt4 import QtCore, QtGui, uic
import danfinitions as dan
import os
import glob
import numpy as np
import matplotlib.pyplot as plt
import time
from scipy.optimize import curve_fit
import math
import pandas as pd
import warnings
qtCreatorFile = "gui_cats_v1.ui" # ... | apache-2.0 |
gabrielcnr/sinteglas | df.py | 1 | 4037 | from PyQt4.QtCore import *
from PyQt4.QtGui import *
import pandas as pd
import numpy as np
class Column(object):
def __init__(self, title, key, align='left', fmt='',
cell_style_callback=None):
self.title = title
self.key = key
self.align = align
self.fmt = fmt
... | mit |
bmmalone/pymisc-utils | setup.py | 1 | 3066 | from setuptools import find_packages, setup
from setuptools.command.install import install as _install
from setuptools.command.develop import develop as _develop
import importlib
###
# Console scripts
###
console_scripts = []
###
# Dependencies
###
install_requires = [
'cython',
'dask[complete]',
'docopt... | mit |
mfitzp/padua | padua/utils.py | 1 | 9476 | import numpy as np
import scipy as sp
import scipy.interpolate
import requests
from io import StringIO
def qvalues(pv, m = None, verbose = False, lowmem = False, pi0 = None):
"""
Copyright (c) 2012, Nicolo Fusi, University of Sheffield
All rights reserved.
Estimates q-values from p-values
Args
... | bsd-2-clause |
Eric89GXL/scikit-learn | sklearn/linear_model/randomized_l1.py | 8 | 22876 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import issparse
from scipy import sparse
from scipy.in... | bsd-3-clause |
richardwolny/sms-tools | lectures/09-Sound-description/plots-code/mfcc.py | 25 | 1103 | import numpy as np
import matplotlib.pyplot as plt
import essentia.standard as ess
M = 1024
N = 1024
H = 512
fs = 44100
spectrum = ess.Spectrum(size=N)
window = ess.Windowing(size=M, type='hann')
mfcc = ess.MFCC(numberCoefficients = 12)
x = ess.MonoLoader(filename = '../../../sounds/speech-male.wav', sampleRate = fs)(... | agpl-3.0 |
huzq/scikit-learn | sklearn/ensemble/_hist_gradient_boosting/tests/test_histogram.py | 14 | 9004 | import numpy as np
import pytest
from numpy.testing import assert_allclose
from numpy.testing import assert_array_equal
from sklearn.ensemble._hist_gradient_boosting.histogram import (
_build_histogram_naive,
_build_histogram,
_build_histogram_no_hessian,
_build_histogram_root_no_hessian,
_build_h... | bsd-3-clause |
mingit/mstcp | arch/sim/test/buildtop/source/ns-3-dce/example/matplotlib-mptcp-lte-wifi-v6.py | 2 | 1961 | from matplotlib.pylab import *
import time
import numpy as np
import subprocess
# interaction mode needs to be turned off
ion()
fig = gcf()
fig.canvas.set_window_title('Multipath TCP Throughput')
while(True): # we'll limit ourselves to 5 seconds.
clf()
time.sleep (1)
# lte
with ope... | gpl-2.0 |
dr-nate/msmbuilder | msmbuilder/msm/core.py | 10 | 22913 | # Author: Robert McGibbon <rmcgibbo@gmail.com>
# Contributors:
# Copyright (c) 2014, Stanford University
# All rights reserved.
from __future__ import print_function, division, absolute_import
import collections
import numpy as np
import scipy.linalg
from scipy.sparse import csgraph, csr_matrix, coo_matrix
from skle... | lgpl-2.1 |
fyffyt/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 |
elingg/tensorflow | tensorflow/contrib/learn/python/learn/estimators/__init__.py | 6 | 11427 | # 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 |
ningchi/scikit-learn | examples/text/document_clustering.py | 31 | 8036 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
jinghaomiao/apollo | modules/tools/prediction/data_pipelines/cruise_models.py | 3 | 5275 | #!/usr/bin/env python3
###############################################################################
# Copyright 2018 The Apollo 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... | apache-2.0 |
pompiduskus/scikit-learn | examples/ensemble/plot_voting_probas.py | 316 | 2824 | """
===========================================================
Plot class probabilities calculated by the VotingClassifier
===========================================================
Plot the class probabilities of the first sample in a toy dataset
predicted by three different classifiers and averaged by the
`VotingC... | bsd-3-clause |
BoltzmannBrain/nupic.research | projects/sequence_prediction/discrete_sequences/plotPerturbExperiment.py | 5 | 3263 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | agpl-3.0 |
huahbo/src | user/karl/rsf2numpy3.py | 5 | 1730 | #!/usr/bin/env python
import rsf.api as rsf
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import sys
import os
import c_m8r as c_rsf
# next challenge is to get input file names from command line
# use Msfin.c as example
print "program name",sys.argv[0]
print "type sys.argv=",type(sys.argv)
if... | gpl-2.0 |
dotsdl/seaborn | seaborn/axisgrid.py | 1 | 48212 | from __future__ import division
from itertools import product
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from six import string_types
from . import utils
from .palettes import color_palette
class Grid(object):
"""Base class for grids of subplots."""
_marg... | bsd-3-clause |
mhue/scikit-learn | benchmarks/bench_multilabel_metrics.py | 86 | 7286 | #!/usr/bin/env python
"""
A comparison of multilabel target formats and metrics over them
"""
from __future__ import division
from __future__ import print_function
from timeit import timeit
from functools import partial
import itertools
import argparse
import sys
import matplotlib.pyplot as plt
import scipy.sparse as... | bsd-3-clause |
etsy/skyline | src/analyzer/algorithms.py | 9 | 9794 | import pandas
import numpy as np
import scipy
import statsmodels.api as sm
import traceback
import logging
from time import time
from msgpack import unpackb, packb
from redis import StrictRedis
from settings import (
ALGORITHMS,
CONSENSUS,
FULL_DURATION,
MAX_TOLERABLE_BOREDOM,
MIN_TOLERABLE_LENGTH,... | mit |
andrebrener/crypto_predictor | rsi.py | 1 | 1655 | # =============================================================================
# File: rsi.py
# Author: Andre Brener
# Created: 03 Jun 2017
# Last Modified: 07 Jun 2017
# Description: description
# =============================================================================
import pandas as pd... | mit |
stscieisenhamer/glue | glue/core/tests/test_pandas.py | 1 | 2513 | from __future__ import absolute_import, division, print_function
import sys
import numpy as np
import pandas as pd
from mock import MagicMock
from pandas.util.testing import (assert_series_equal,
assert_frame_equal)
from ...external.six import PY3
from ..component import Component, De... | bsd-3-clause |
smartscheduling/scikit-learn-categorical-tree | examples/mixture/plot_gmm_sin.py | 248 | 2747 | """
=================================
Gaussian Mixture Model Sine Curve
=================================
This example highlights the advantages of the Dirichlet Process:
complexity control and dealing with sparse data. The dataset is formed
by 100 points loosely spaced following a noisy sine curve. The fit by
the GMM... | bsd-3-clause |
jrper/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 |
ahill818/MetPy | examples/gridding/Find_Natural_Neighbors_Verification.py | 3 | 2746 | # Copyright (c) 2008-2016 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
Find Natural Neighbors Verification
===================================
Finding natural neighbors in a triangulation
A triangle is a natural neighbor of a point if that po... | bsd-3-clause |
BCCN-Prog/database | plotting.py | 1 | 13274 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import operator
import click
import datetime
import calendar
from weather_loading import load_dataframe
from scipy.stats import linregress
from pylab import rcParams
rcParams['figure.figsize'] = 20, 3 #setting plots size
d... | bsd-3-clause |
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