repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
draperjames/qtpandas | setup.py | 1 | 4056 | # from __future__ import unicode_literals
# from __future__ import print_function
# from __future__ import division
# from __future__ import absolute_import
#
# from builtins import open
# from future import standard_library
# standard_library.install_aliases()
from setuptools import setup, find_packages
from setuptoo... | mit |
shyamalschandra/scikit-learn | examples/missing_values.py | 71 | 3055 | """
======================================================
Imputing missing values before building an estimator
======================================================
This example shows that imputing the missing values can give better results
than discarding the samples containing any missing value.
Imputing does not ... | bsd-3-clause |
timsf/bayeslib | testing/mix.py | 1 | 1601 | import pdb
import importlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from bayeslib.backends.blocked_gibbs import normal_mixture as mixn, multinomial_mixture as mixm
import bayeslib.api_batch.mixtures as mix
plt.ion()
## test normal mixture on artificial dataset
nobs, ncomp, nvar = 100, 2... | gpl-3.0 |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/matplotlib/backends/tkagg.py | 10 | 1250 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import tkinter as Tk
import numpy as np
from matplotlib.backends import _tkagg
def blit(photoimage, aggimage, bbox=None, colormode=1):
tk = photoimage.tk
if bbox is not Non... | gpl-3.0 |
pxsdirac/tushare | tushare/datayes/equity.py | 17 | 6857 | # -*- coding:utf-8 -*-
"""
通联数据
Created on 2015/08/24
@author: Jimmy Liu
@group : waditu
@contact: jimmysoa@sina.cn
"""
from pandas.compat import StringIO
import pandas as pd
from tushare.util import vars as vs
from tushare.util.common import Client
from tushare.util import upass as up
class Equity():
def _... | bsd-3-clause |
pypot/scikit-learn | sklearn/tests/test_multiclass.py | 72 | 24581 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing ... | bsd-3-clause |
amolkahat/pandas | pandas/tests/test_algos.py | 2 | 71102 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
import struct
from pandas import (Series, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalInd... | bsd-3-clause |
klocey/ScalingMicroBiodiversity | fig-scripts/AppFigs/Fig1_Variants/DataSetComparison.py | 2 | 3727 | from __future__ import division
import os
import sys
import matplotlib.pyplot as plt
from matplotlib.pyplot import setp
mydir = os.path.expanduser("~/GitHub/MicrobialScaling/")
# function for setting the colors of the box plots pairs
def setBoxColors(bp):
#print len(bp['caps'])
#print bp['fliers'][0]
#pr... | gpl-3.0 |
facom/AstrodynTools | tides/potential-contours.py | 1 | 1071 | #!/usr/bin/env python
#-*-coding:utf-8-*-
from constants import *
from numpy import *
from matplotlib.pyplot import *
###################################################
#UTILITIES
###################################################
DEG=pi/180
RAD=180/pi
def P2(psi):
p=0.5*(3*cos(psi)**2-1)
return p
#########... | gpl-2.0 |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/pandas/util/print_versions.py | 7 | 4898 | import os
import platform
import sys
import struct
import subprocess
import codecs
import locale
import importlib
def get_sys_info():
"Returns system information as a dict"
blob = []
# get full commit hash
commit = None
if os.path.isdir(".git") and os.path.isdir("pandas"):
try:
... | mit |
jdmcbr/blaze | blaze/expr/collections.py | 2 | 20040 | from __future__ import absolute_import, division, print_function
from functools import partial
from itertools import chain
import datashape
from datashape import (
DataShape, Option, Record, Unit, dshape, var, Fixed, Var, promote, object_,
)
from datashape.predicates import isscalar, iscollection, isrecord
from t... | bsd-3-clause |
jimgoo/zipline-fork | zipline/protocol.py | 3 | 17052 | #
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
smueller18/solar-thermal-climate-system | consumer/machine-state-prediction/consumer.py | 1 | 3723 | #!/usr/bin/env python3
import os
import time
import logging.config
import pickle
import pandas as pd
from pca import PCAForPandas
import kafka_connector.avro_loop_consumer as avro_loop_consumer
from kafka_connector.avro_loop_consumer import AvroLoopConsumer
from tsfresh.feature_extraction import extract_features
fro... | mit |
iamshang1/Projects | Advanced_ML/Human_Activity_Recognition/LSTM/lstm_within_subject.py | 1 | 8787 | import numpy as np
import theano
import theano.tensor as T
import sys
import random
from sklearn.model_selection import train_test_split
sys.setrecursionlimit(10000)
class lstm(object):
'''
long short term memory network for classifying human activity
from incremental summary statistics
paramete... | mit |
kwilliams-mo/iris | lib/iris/tests/test_trajectory.py | 2 | 8439 | # (C) British Crown Copyright 2010 - 2013, Met Office
#
# This file is part of Iris.
#
# Iris is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any l... | gpl-3.0 |
dhruvesh13/Audio-Genre-Classification | learn.py | 1 | 4259 | import sklearn
from sklearn import linear_model
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import scipy
import os
import sys
import glob
... | mit |
ankurankan/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 204 | 4452 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""... | bsd-3-clause |
fzalkow/scikit-learn | sklearn/utils/tests/test_linear_assignment.py | 421 | 1349 | # Author: Brian M. Clapper, G Varoquaux
# License: BSD
import numpy as np
# XXX we should be testing the public API here
from sklearn.utils.linear_assignment_ import _hungarian
def test_hungarian():
matrices = [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
... | bsd-3-clause |
PeterRochford/SkillMetrics | Examples/taylor3.py | 1 | 4261 | '''
How to create a Taylor diagram with labeled data points and modified axes
A third example of how to create a Taylor diagram given one set of
reference observations and multiple model predictions for the quantity.
This example is a variation on the first example (taylor1) where now the
data points are labeled and ... | gpl-3.0 |
axelmagn/metrics | ai-metrics/aimetrics/metrics.py | 1 | 7181 | import json
import numpy as np
from sklearn.cross_validation import (StratifiedShuffleSplit, StratifiedKFold,
KFold)
from sklearn.preprocessing import binarize, normalize
from sklearn.metrics import (accuracy_score, roc_curve, roc_auc_score,
f1_score, classification_report)
from tor... | apache-2.0 |
aelsen/Systems_Integration_EMG | scripts_acquisition/myo.py | 8 | 3086 | from __future__ import print_function
from collections import Counter, deque
import sys
import time
import numpy as np
try:
from sklearn import neighbors, svm
HAVE_SK = True
except ImportError:
HAVE_SK = False
from common import *
from myo_raw import MyoRaw
SUBSAMPLE = 3
K = 15
class NNClassifier(obje... | mit |
Adai0808/scikit-learn | benchmarks/bench_multilabel_metrics.py | 276 | 7138 | #!/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 |
Garrett-R/scikit-learn | sklearn/neighbors/regression.py | 39 | 10464 | """Nearest Neighbor Regression"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output support by Arna... | bsd-3-clause |
m2dsupsdlclass/lectures-labs | labs/03_neural_recsys/movielens_paramsearch.py | 1 | 9625 | from math import floor, ceil
from time import time
from pathlib import Path
from zipfile import ZipFile
from urllib.request import urlretrieve
from contextlib import contextmanager
import random
from pprint import pprint
import json
import numpy as np
import pandas as pd
import joblib
from sklearn.model_selection impo... | mit |
JosmanPS/scikit-learn | benchmarks/bench_plot_nmf.py | 206 | 5890 | """
Benchmarks of Non-Negative Matrix Factorization
"""
from __future__ import print_function
from collections import defaultdict
import gc
from time import time
import numpy as np
from scipy.linalg import norm
from sklearn.decomposition.nmf import NMF, _initialize_nmf
from sklearn.datasets.samples_generator import... | bsd-3-clause |
JLHulme/MhoPerformance | Voltage_plot.py | 1 | 3051 | #Required imports
import math
import matplotlib.pyplot as plt
#Create time vector, in terms of samples, for a time period of 30cycles
length = 30 #cycles
sampleRate = 4 # samples per cycle
time = []
#create global definition for a
deg120 = (math.pi/180) * 120
a = math.cos(deg120)+math.sin(deg120)*1j
for x in rang... | bsd-2-clause |
paninski-lab/yass | src/yass/cluster/cluster.py | 1 | 45893 | # Class to do parallelized clustering
import os
import numpy as np
import networkx as nx
from sklearn.decomposition import PCA
from scipy.spatial import cKDTree
from scipy.stats import chi2
from yass.template import shift_chans, align_get_shifts_with_ref
from yass import mfm
from yass.util import absolute_path_to_asse... | apache-2.0 |
benhoff/vexbot | setup.py | 2 | 3549 | import os
import re
from setuptools import find_packages, setup
from vexbot.extension_metadata import extensions
VERSIONFILE = 'vexbot/_version.py'
verstrline = open(VERSIONFILE, 'rt').read()
VSRE = r"^__version__ = ['\"]([^'\"]*)['\"]"
mo = re.search(VSRE, verstrline, re.M)
if mo:
verstr = mo.group(1)
else:
... | gpl-3.0 |
DavidTingley/ephys-processing-pipeline | installation/klustaviewa-0.3.0/build/lib.linux-x86_64-2.7/kwiklib/dataio/tests/test_klustersloader.py | 2 | 17397 | """Unit tests for loader module."""
# -----------------------------------------------------------------------------
# Imports
# -----------------------------------------------------------------------------
import os
from collections import Counter
import numpy as np
import numpy.random as rnd
import pandas ... | gpl-3.0 |
zigahertz/2013-Sep-HR-ML-sprint | py/randomforest.py | 1 | 3790 | """ random forest """
import numpy as np
import csv as csv
from sklearn.ensemble import RandomForestClassifier
csv_file_object = csv.reader(open('train.csv', 'rb')) #Load in the training csv file
header = csv_file_object.next() #Skip the fist line as it is a header
train_data=[] #Creat a variable called 'train_data'
... | mit |
Lawrence-Liu/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 |
andim/scipy | scipy/stats/morestats.py | 3 | 92737 | # Author: Travis Oliphant, 2002
#
# Further updates and enhancements by many SciPy developers.
#
from __future__ import division, print_function, absolute_import
import math
import warnings
from collections import namedtuple
import numpy as np
from numpy import (isscalar, r_, log, around, unique, asarray,
... | bsd-3-clause |
piyush8311/ns3-arp | src/core/examples/sample-rng-plot.py | 188 | 1246 | # -*- Mode:Python; -*-
# /*
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License version 2 as
# * published by the Free Software Foundation
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRA... | gpl-2.0 |
microsoft/task_oriented_dialogue_as_dataflow_synthesis | src/dataflow/onmt_helpers/evaluate_onmt_predictions.py | 1 | 6406 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
Semantic Machines\N{TRADE MARK SIGN} software.
Evaluates 1best predictions.
Computes both turn-level and dialogue-level accuracy.
"""
import argparse
import csv
import json
from dataclasses import dataclass
from typing import List, Option... | mit |
ningchi/scikit-learn | examples/text/hashing_vs_dict_vectorizer.py | 284 | 3265 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the e... | bsd-3-clause |
chatcannon/scipy | scipy/signal/windows.py | 20 | 54134 | """The suite of window functions."""
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from scipy import fftpack, linalg, special
from scipy._lib.six import string_types
__all__ = ['boxcar', 'triang', 'parzen', 'bohman', 'blackman', 'nuttall',
'blackmanhar... | bsd-3-clause |
TheArbiter/Networks | lab4/lab4exercise1/util/helper.py | 6 | 3381 | '''
Helper module for the plot scripts.
'''
import re
import itertools
import matplotlib as m
import os
if os.uname()[0] == "Darwin":
m.use("MacOSX")
else:
m.use("Agg")
import matplotlib.pyplot as plt
import argparse
import math
def read_list(fname, delim=','):
lines = open(fname).xreadlines()
ret = [... | gpl-3.0 |
jklenzing/pysat | pysat/ssnl/plot.py | 2 | 3420 | from __future__ import print_function
from __future__ import absolute_import
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import warnings
def scatterplot(inst, labelx, labely, data_label, datalim, xlim=None,
ylim=None):
"""Ret... | bsd-3-clause |
0x0all/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/patches.py | 69 | 110325 | # -*- coding: utf-8 -*-
from __future__ import division
import math
import matplotlib as mpl
import numpy as np
import matplotlib.cbook as cbook
import matplotlib.artist as artist
import matplotlib.colors as colors
import matplotlib.transforms as transforms
from matplotlib.path import Path
# these are not available ... | gpl-3.0 |
larsoner/mne-python | examples/visualization/plot_3d_to_2d.py | 15 | 4941 | """
.. _ex-electrode-pos-2d:
====================================================
How to convert 3D electrode positions to a 2D image.
====================================================
Sometimes we want to convert a 3D representation of electrodes into a 2D
image. For example, if we are using electrocorticography ... | bsd-3-clause |
PmagPy/PmagPy | programs/conversion_scripts2/bgc_magic2.py | 3 | 8598 | #!/usr/bin/env python
from __future__ import division
from __future__ import print_function
from builtins import str
from past.utils import old_div
import pandas as pd
import sys
import os
import numpy as np
import pmagpy.pmag as pmag
def main(command_line=True, **kwargs):
"""
NAME
bgc_magic.py
D... | bsd-3-clause |
jpinedaf/pyspeckit | pyspeckit/spectrum/plotters.py | 4 | 35855 | """
=======
Plotter
=======
.. moduleauthor:: Adam Ginsburg <adam.g.ginsburg@gmail.com>
"""
from __future__ import print_function
import matplotlib
import matplotlib.figure
import numpy as np
import astropy.units as u
import copy
import inspect
from astropy import log
# this mess is to handle a nested hell of differe... | mit |
rishikksh20/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 |
saebrahimi/Emotion-Recognition-EmotiW2015 | common/disptools.py | 2 | 11942 | #!/usr/bin/env python
#-*- coding: utf-8 -*-
import os
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
def pred_visualization(fname, arrays, picks, img_shape, tile_spacing=(0,0),
scale_rows_to_unit_interval=True,
output_pixel_vals=True):
"""Used ... | mit |
JackKelly/neuralnilm_prototype | scripts/e334.py | 2 | 5407 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectio... | mit |
jreback/pandas | pandas/io/pytables.py | 1 | 167728 | """
High level interface to PyTables for reading and writing pandas data structures
to disk
"""
from contextlib import suppress
import copy
from datetime import date, tzinfo
import itertools
import os
import re
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List... | bsd-3-clause |
ToFuProject/tofu | tofu/data/_core.py | 1 | 174350 | # -*- coding: utf-8 -*-
# Built-in
import sys
import os
import itertools as itt
import copy
import warnings
from abc import ABCMeta, abstractmethod
import inspect
# Common
import numpy as np
import scipy.interpolate as scpinterp
import matplotlib.pyplot as plt
from matplotlib.tri import Triangulation as mplTri
# to... | mit |
gregcaporaso/q2d2 | setup.py | 1 | 1808 | #!/usr/bin/env python
import re
import ast
from setuptools import find_packages, setup
# version parsing from __init__ pulled from Flask's setup.py
# https://github.com/mitsuhiko/flask/blob/master/setup.py
_version_re = re.compile(r'__version__\s+=\s+(.*)')
with open('q2d2/__init__.py', 'rb') as f:
hit = _versio... | bsd-3-clause |
pythonvietnam/scikit-learn | examples/linear_model/plot_omp.py | 385 | 2263 | """
===========================
Orthogonal Matching Pursuit
===========================
Using orthogonal matching pursuit for recovering a sparse signal from a noisy
measurement encoded with a dictionary
"""
print(__doc__)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import OrthogonalM... | bsd-3-clause |
schets/scikit-learn | sklearn/neural_network/tests/test_rbm.py | 142 | 6276 | import sys
import re
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
from sklearn.utils.testing import (assert_almost_equal, assert_array_equal,
assert_true)
from sklearn.datasets import load_digits
from sklearn.externals.six.moves import cStringIO as ... | bsd-3-clause |
spbguru/repo1 | examples/opf/clients/hotgym/anomaly/one_gym/run.py | 15 | 4940 | #!/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 ... | gpl-3.0 |
theDataGeek/pyhsmm | setup.py | 2 | 3380 | from distutils.core import setup, Extension
import numpy as np
import sys
import os
from glob import glob
PYHSMM_VERSION = "0.1.3"
###########################
# compilation arguments #
###########################
extra_link_args = []
extra_compile_args = []
if '--with-old-clang' in sys.argv:
sys.argv.remove('... | mit |
pepper-johnson/Erudition | Thesis/Mallet/notebooks/models/imports/features.py | 1 | 1856 | import numpy as np
import pandas as pd
FEATURE_COLUMNS_WITH_DATE = [
'date',
'chibs',
'hm',
'is',
'lr',
'price'
]
INPUT_COLUMNS = [
'chibs',
'hm',
'is',
'lr',
]
OUTPUT_COLUMNS = [
'price'
]
def create_dataset(df):
## assert the input is a pandas dataframe
ass... | apache-2.0 |
jonwright/ImageD11 | ImageD11/depreciated/rebin2d.py | 1 | 3961 | # ImageD11_v0.4 Software for beamline ID11
# Copyright (C) 2005 Jon Wright
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later ver... | gpl-2.0 |
tawsifkhan/scikit-learn | examples/plot_digits_pipe.py | 250 | 1809 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================
The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the predictio... | bsd-3-clause |
ABoothInTheWild/baseball-research | true2018PlayoffPreds.py | 1 | 18268 | # -*- coding: utf-8 -*-
"""
Created on Sun Aug 05 22:49:05 2018
@author: Alexander
"""
#2018 Preseason Playoff Odds
import heapq
from collections import Counter
import pandas as pd
import numpy as np
from scipy.stats import beta
import os
#read data
os.chdir('C:/Users/abooth/Documents/Python Scripts/... | gpl-3.0 |
CG-F16-24-Rutgers/steersuite-rutgers | steerstats/tools/plotting/plotMultiObjectiveData3D.py | 8 | 2231 |
import csv
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
import sys
import scipy
from scipy.interpolate import bisplrep
from scipy.interpolate import bisplev
import numpy as np
sys.path.append('../../')
from util import readCSVDictToMutliObjData
from util import get... | gpl-3.0 |
JKarathiya/Lean | Algorithm.Python/NLTKSentimentTradingAlgorithm.py | 1 | 3025 | # QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect 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 Lic... | apache-2.0 |
MartinDelzant/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | 233 | 7819 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
jreback/pandas | pandas/tests/frame/test_query_eval.py | 2 | 47508 | from io import StringIO
import operator
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, date_range
import pandas._testing as tm
from pandas.core.computation.check import NUMEXPR_INSTALLED
PARSERS = "python", "pan... | bsd-3-clause |
Midafi/scikit-image | doc/examples/plot_multiblock_local_binary_pattern.py | 22 | 2498 | """
===========================================================
Multi-Block Local Binary Pattern for texture classification
===========================================================
This example shows how to compute multi-block local binary pattern (MB-LBP)
features as well as how to visualize them.
The features ar... | bsd-3-clause |
dhomeier/astropy | astropy/nddata/utils.py | 3 | 32016 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module includes helper functions for array operations.
"""
from copy import deepcopy
import sys
import types
import warnings
import numpy as np
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.utils import la... | bsd-3-clause |
andyjost/slang | scripts/neural_nets.py | 1 | 1536 | #!/usr/bin/env python
from itertools import *
from sklearn import datasets
from sklearn import preprocessing
from sklearn import svm
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import *
from sklearn.neural_network import MLPClassifier
impor... | gpl-3.0 |
amolkahat/pandas | pandas/tests/arithmetic/test_numeric.py | 1 | 32501 | # -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for numeric dtypes
from decimal import Decimal
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.compat import PY3, Itera... | bsd-3-clause |
Tong-Chen/scikit-learn | sklearn/metrics/tests/test_pairwise.py | 4 | 18398 | import numpy as np
from numpy import linalg
from scipy.sparse import dok_matrix, csr_matrix, issparse
from scipy.spatial.distance import cosine, cityblock, minkowski
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_al... | bsd-3-clause |
Hbl15/ThinkStats2 | code/populations.py | 68 | 2609 | """This file contains code used in "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2010 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import csv
import logging
import sys
import numpy as np
import pandas
import thinkpl... | gpl-3.0 |
scikit-learn-contrib/categorical-encoding | tests/test_count.py | 1 | 10805 | import pandas as pd
from unittest import TestCase # or `from unittest import ...` if on Python 3.4+
import numpy as np
import category_encoders as encoders
X = pd.DataFrame({
'none': [
'A', 'A', 'B', None, None, 'C', None, 'C', None, 'B',
'A', 'A', 'C', 'B', 'B', 'A', 'A', None, 'B', None
],
... | bsd-3-clause |
zihua/scikit-learn | examples/cluster/plot_face_segmentation.py | 71 | 2839 | """
===================================================
Segmenting the picture of a raccoon face in regions
===================================================
This example uses :ref:`spectral_clustering` on a graph created from
voxel-to-voxel difference on an image to break this image into multiple
partly-homogeneous... | bsd-3-clause |
weinbe58/QuSpin | examples/scripts/example3.py | 3 | 5466 | from __future__ import print_function, division
import sys,os
# line 4 and line 5 below are for development purposes and can be removed
qspin_path = os.path.join(os.getcwd(),"../../")
sys.path.insert(0,qspin_path)
########################################################################################
# ... | bsd-3-clause |
dyoung418/tensorflow | tensorflow/python/estimator/inputs/pandas_io.py | 86 | 4503 | # 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 |
giorgiop/scipy | doc/source/tutorial/examples/normdiscr_plot2.py | 84 | 1642 | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
npoints = 20 # number of integer support points of the distribution minus 1
npointsh = npoints / 2
npointsf = float(npoints)
nbound = 4 #bounds for the truncated normal
normbound = (1 + 1 / npointsf) * nbound #actual bounds of truncated normal
... | bsd-3-clause |
aev3/trading-with-python | lib/yahooFinance.py | 76 | 8290 | # -*- coding: utf-8 -*-
# Author: Jev Kuznetsov <jev.kuznetsov@gmail.com>
# License: BSD
"""
Toolset working with yahoo finance data
This module includes functions for easy access to YahooFinance data
Functions
----------
- `getHistoricData` get historic data for a single symbol
- `getQuote` get cur... | bsd-3-clause |
awacha/sastool | tests/centering/beamfinding_test_evaluate.py | 1 | 1581 | import os
import numpy as np
import matplotlib.pyplot as plt
import re
import sastool
import matplotlib
matplotlib.rcParams['font.size']=8
xmin=40
xmax=210
ymin=40
ymax=210
modes=['slice','azim','azimfold']
bcxfiles=[f for f in os.listdir('.') if re.match('bcx[a-z]+_([0-9]+).npy',f)]
print(bcxfiles)
fsns=set([re.mat... | bsd-3-clause |
bakfu/bakfu | bakfu/process/vectorize/vec_sklearn.py | 2 | 3972 | # -*- coding: utf-8 -*-
'''
This is an interface to sklearn vectorizer classes.
'''
import sklearn
from sklearn.feature_extraction.text import CountVectorizer as SKCountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer as SKTfidfVectorizer
from ...core.routes import register
from .base import... | bsd-3-clause |
bhargav/scikit-learn | examples/linear_model/plot_theilsen.py | 100 | 3846 | """
====================
Theil-Sen Regression
====================
Computes a Theil-Sen Regression on a synthetic dataset.
See :ref:`theil_sen_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the Theil-Sen
estimator is robust against outliers. It has a breakd... | bsd-3-clause |
scikit-nano/scikit-nano | setup.py | 2 | 10936 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Python toolkit for generating and analyzing nanostructure data"""
from __future__ import absolute_import, division, print_function, \
unicode_literals
__docformat__ = 'restructuredtext en'
import os
import sys
import shutil
import subprocess
from distutils.command... | bsd-2-clause |
poppingtonic/BayesDB | bayesdb/tests/experiments/estimate_the_full_joint_dist.py | 2 | 6142 | #
# Copyright (c) 2010-2014, MIT Probabilistic Computing Project
#
# Lead Developers: Jay Baxter and Dan Lovell
# Authors: Jay Baxter, Dan Lovell, Baxter Eaves, Vikash Mansinghka
# Research Leads: Vikash Mansinghka, Patrick Shafto
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may... | apache-2.0 |
chrishavlin/nyc_taxi_viz | src/taxi_main.py | 1 | 12620 | """
taxi_main.py
module for loading the raw csv taxi files.
Copyright (C) 2016 Chris Havlin, <https://chrishavlin.wordpress.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 Foundation, either ... | gpl-3.0 |
ettm2012/MissionPlanner | Lib/site-packages/numpy/fft/fftpack.py | 59 | 39653 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1)
ifft(a, n=None, axis=-1)
rfft(a, n=None, axis=-1)
irfft(a, n=None, axis=-1)
hfft(a, n=None, axis=-1)
ihfft(a, n=None, axis=-1)
fftn(a, s=None, axes=None)
ifftn(a, s=None, axes=None)
rfftn(a, s=None, axes=None)
irfftn(a, s=None, axes=None... | gpl-3.0 |
maximus009/kaggle-galaxies | predict_augmented_npy_maxout2048_extradense_pysexgen1_dup.py | 7 | 9736 | """
Load an analysis file and redo the predictions on the validation set / test set,
this time with augmented data and averaging. Store them as numpy files.
"""
import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime... | bsd-3-clause |
hdmetor/scikit-learn | examples/plot_multilabel.py | 87 | 4279 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/tests/plotting/common.py | 6 | 19480 | #!/usr/bin/env python
# coding: utf-8
import pytest
import os
import warnings
from pandas import DataFrame, Series
from pandas.compat import zip, iteritems
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.api import is_list_like
import pandas.util.testing as tm
from pandas.util.testing impor... | mit |
BiaDarkia/scikit-learn | examples/ensemble/plot_forest_importances.py | 168 | 1793 | """
=========================================
Feature importances with forests of trees
=========================================
This examples shows the use of forests of trees to evaluate the importance of
features on an artificial classification task. The red bars are the feature
importances of the forest, along wi... | bsd-3-clause |
Adai0808/scikit-learn | sklearn/linear_model/ransac.py | 191 | 14261 | # coding: utf-8
# Author: Johannes Schönberger
#
# License: BSD 3 clause
import numpy as np
from ..base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.random import sample_without_replacement
from ..utils.valid... | bsd-3-clause |
markovianlabs/pychain | src/mcmc.py | 1 | 6943 | """
Markov Chain Monte Carlo (MCMC) method using Metropolis-Hastings algorithm.
Copyright: MarkovianLabs
"""
import numpy as np
import matplotlib.pyplot as plt
from sys import exit
#----------------------------------------------------------
__author__ = ("Irshad Mohammed <creativeishu@gmail.com>")
#---------------... | mit |
Barmaley-exe/scikit-learn | examples/plot_digits_pipe.py | 250 | 1809 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================
The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the predictio... | bsd-3-clause |
RuthAngus/chronometer | chronometer/fit_dispersion.py | 1 | 2000 | import numpy as np
from action_age_evolution import calc_dispersion
import emcee
import corner
import matplotlib.pyplot as plt
plotpar = {'axes.labelsize': 18,
'font.size': 10,
'legend.fontsize': 15,
'xtick.labelsize': 18,
'ytick.labelsize': 18,
'text.usetex': Tru... | mit |
spallavolu/scikit-learn | sklearn/datasets/tests/test_mldata.py | 384 | 5221 | """Test functionality of mldata fetching utilities."""
import os
import shutil
import tempfile
import scipy as sp
from sklearn import datasets
from sklearn.datasets import mldata_filename, fetch_mldata
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.test... | bsd-3-clause |
robbymeals/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 121 | 6117 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.base import ClassifierMixin
from skle... | bsd-3-clause |
public-ink/public-ink | server/appengine/lib/matplotlib/offsetbox.py | 10 | 54984 | """
The OffsetBox is a simple container artist. The child artist are meant
to be drawn at a relative position to its parent. The [VH]Packer,
DrawingArea and TextArea are derived from the OffsetBox.
The [VH]Packer automatically adjust the relative postisions of their
children, which should be instances of the OffsetBo... | gpl-3.0 |
tlhr/plumology | plumology/vis.py | 1 | 16471 | """vis - Visualisation and plotting tools"""
from typing import Union, Sequence, Optional, List, Tuple
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.collections import RegularPolyCollection
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
from ... | mit |
DTMilodowski/LiDAR_canopy | src/inventory_based_LAD_profiles.py | 1 | 40714 | # This contains code to estimate LAD profiles based on field measurements of
# tree height and crown dimensions, in addition to crown depth based on a
# regional allometric equation.
import numpy as np
from scipy import stats
pi=np.pi
# linear regression with confidence intervals and prediction intervals
def linear_r... | gpl-3.0 |
gfyoung/numpy | numpy/lib/npyio.py | 2 | 84419 | from __future__ import division, absolute_import, print_function
import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter, index as opindex
import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core.multiarray import packbits, unpa... | bsd-3-clause |
Shekharrajak/mubosym | mubosym/mubosym_core.py | 1 | 95748 | # -*- coding: utf-8 -*-
"""
all mubosym related core classes
================================
Created on Sun Mar 8 11:50:46 2015
@author: oliver
"""
from __future__ import print_function, absolute_import
import os.path,sys,time,copy
try:
import pandas as pd
no_pandas = False
except:
print( 'can not use p... | mit |
yonglehou/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 |
f3r/scikit-learn | sklearn/decomposition/truncated_svd.py | 30 | 7896 | """Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
"""
# Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# Olivier Grisel <olivier.grisel@ensta.org>
# Michael Becker <mike@beckerfuffle.com>
# License: 3-clause BSD.
import numpy as np
import scipy.sparse as sp
try:
from scipy.sp... | bsd-3-clause |
tsaoyu/D3HRE | D3HRE/core/mission_utility.py | 1 | 7731 | import numpy as np
import pandas as pd
import nvector as nv
from math import radians, cos, sin, asin, sqrt
from datetime import timedelta
from D3HRE.core.get_hash import hash_value
from D3HRE.core.dataframe_utility import full_day_cut
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle dis... | gpl-3.0 |
n7jti/machine_learning | adaboost/newsgroups.py | 1 | 4192 | #!/usr/bin/python2
from scipy import *
import scipy.sparse as sp
import scipy.linalg as la
#See http://scikit-learn.org/stable/modules/feature_extraction.html
import sklearn.feature_extraction as fe
import tok
import dstump as ds
import pylab as pl
import numpy as np
import operator
from datetime import datetime
def ... | apache-2.0 |
mhue/scikit-learn | sklearn/pipeline.py | 162 | 21103 | """
The :mod:`sklearn.pipeline` module implements utilities to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Lars Buitinck
# Licence: BSD
from collections import defaultdict... | bsd-3-clause |
pradyu1993/scikit-learn | sklearn/svm/tests/test_bounds.py | 6 | 2069 | import nose
from nose.tools import assert_true
import numpy as np
from scipy import sparse as sp
from sklearn.svm.bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model.logistic import LogisticRegression
dense_X = [[-1, 0], [0, 1], [1, 1], [1, 1]]
sparse_X = sp.csr_matrix(dense_X)
Y1 = ... | bsd-3-clause |
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