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 |
|---|---|---|---|---|---|
skyfallen/Kaggle-Diabetic-Retinopathy-Detection | Code/svm/svm_test.py | 1 | 1997 | from sklearn.ensemble import RandomForestClassifier
import numpy as np
import os
from scipy.misc import imsave,imread
from sklearn.grid_search import GridSearchCV
from datetime import datetime
import cPickle
def load_subset(subset):
images_labels = {}
path_to_images = '/storage/hpc_anna/Kaggle_DRD/imag... | mit |
ianmtaylor1/pacal | pacal/depvars/copulas.py | 1 | 35256 | """Set of copulas different types"""
from pacal.integration import *
from pacal.interpolation import *
from matplotlib.collections import PolyCollection
import pacal.distr
#from pacal import *
from pacal.segments import PiecewiseDistribution, MInfSegment, PInfSegment, Segment, _segint
from pacal.segments import Piece... | gpl-3.0 |
webmasterraj/FogOrNot | flask/lib/python2.7/site-packages/pandas/tseries/tests/test_converter.py | 13 | 5611 | from datetime import datetime, time, timedelta, date
import sys
import os
import nose
import numpy as np
from numpy.testing import assert_almost_equal as np_assert_almost_equal
from pandas import Timestamp, Period
from pandas.compat import u
import pandas.util.testing as tm
from pandas.tseries.offsets import Second, ... | gpl-2.0 |
akunze3/pytrajectory | pytrajectory/splines.py | 1 | 30029 | import numpy as np
import sympy as sp
import scipy.sparse as sparse
from scipy.sparse.linalg import spsolve
from log import logging
# DEBUG
from IPython import embed as IPS
class Spline(object):
'''
This class provides a representation of a cubic spline function.
It simultaneously enables access to... | bsd-3-clause |
jstoxrocky/statsmodels | statsmodels/examples/ex_kernel_regression_dgp.py | 34 | 1202 | # -*- coding: utf-8 -*-
"""
Created on Sun Jan 06 09:50:54 2013
Author: Josef Perktold
"""
from __future__ import print_function
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.nonparametric.api import KernelReg
import statsmodels.sandbox.nonparametric... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.22/_downloads/0671cb4b44003efe690d32b13faaff5d/plot_seeg.py | 10 | 7388 | """
.. _tut_working_with_seeg:
======================
Working with sEEG data
======================
MNE supports working with more than just MEG and EEG data. Here we show some
of the functions that can be used to facilitate working with
stereoelectroencephalography (sEEG) data.
This example shows how to use:
- sEE... | bsd-3-clause |
justinfinkle/pydiffexp | scripts/analyze_sim_results.py | 1 | 3040 | import ast
import numpy as np
import pandas as pd
from pydiffexp import get_scores, DEResults
from pydiffexp.analyze import pairwise_corr
from scipy import stats
calc_correlation = False
sim_data = pd.read_csv('intermediate_data/sim_stats_censoredtimes.tsv', sep='\t', index_col=[0, 1, 2], header=[0,1])
dea = pd.read_... | gpl-3.0 |
piem/aubio | python/demos/demo_specdesc.py | 5 | 2580 | #! /usr/bin/env python
import sys
import numpy as np
from aubio import source, pvoc, specdesc
win_s = 512 # fft size
hop_s = win_s // 4 # hop size
if len(sys.argv) < 2:
print("Usage: %s <filename> [samplerate]" % sys.argv[0])
sys.exit(1)
filename = sys.argv[1]
samplerate = 0
if len... | gpl-3.0 |
shaneknapp/spark | python/pyspark/pandas/usage_logging/__init__.py | 16 | 8972 | #
# 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 |
lily-zhangying/find_best_mall | recomendation system/filter_demo_data.py | 3 | 486609 | import csv
#from mall_count_dataset import dict
import pandas as pd
dict = {"test mall": {"footwear": 3, "fasion wholesale": 2, "restaurant": 2, "guest services": 1, "eyewear":2},"turtle creek mall": {"hair care": 1, "shoes": 1, "restaurant": 1, "beauty products": 1, "plus size clothing": 1, "women's clothing": 3, "chi... | mit |
nelango/ViralityAnalysis | model/lib/sklearn/gaussian_process/tests/test_gaussian_process.py | 267 | 6813 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Licence: BSD 3 clause
from nose.tools import raises
from nose.tools import assert_true
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process ... | mit |
quantrocket-llc/quantrocket-client | quantrocket/history.py | 1 | 19663 | # Copyright 2017 QuantRocket - All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | apache-2.0 |
CheMcCandless/hyperopt-sklearn | hpsklearn/components.py | 4 | 26520 | import numpy as np
import sklearn.svm
import sklearn.ensemble
import sklearn.neighbors
import sklearn.decomposition
import sklearn.preprocessing
import sklearn.neural_network
import sklearn.linear_model
import sklearn.feature_extraction.text
import sklearn.naive_bayes
from hyperopt.pyll import scope, as_apply
from hype... | bsd-3-clause |
GitYiheng/reinforcement_learning_test | test01_cartpendulum/Feb/t8_cartpole_mc_plot.py | 1 | 7022 | import tensorflow as tf # neural network for function approximation
import gym # environment
import numpy as np # matrix operation and math functions
from gym import wrappers
import gym_morph # customized environment for cart-pole
import matplotlib.pyplot as plt
import time
# Hyperparameters
RANDOM_NUMBER_SEED = 2
# ... | mit |
akionakamura/scikit-learn | sklearn/datasets/base.py | 196 | 18554 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import shutil
from os import environ
from os.pa... | bsd-3-clause |
pompiduskus/scikit-learn | examples/plot_kernel_ridge_regression.py | 230 | 6222 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
kjung/scikit-learn | examples/ensemble/plot_gradient_boosting_regularization.py | 355 | 2843 | """
================================
Gradient Boosting regularization
================================
Illustration of the effect of different regularization strategies
for Gradient Boosting. The example is taken from Hastie et al 2009.
The loss function used is binomial deviance. Regularization via
shrinkage (``lear... | bsd-3-clause |
davemccormick/pyAnimalTrack | src/pyAnimalTrack/ui/Controller/DeadReckoningWindow.py | 1 | 4620 | import os
from PyQt5 import uic
import matplotlib.pyplot as plt
import pandas as pd
from PyQt5.QtWidgets import QMainWindow, QFileDialog
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from pyAnimalTrack.backend.deadreckoning.dead_reckoning import DeadReckoning
from pyAnimalTrack.back... | gpl-3.0 |
samuel1208/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 |
plugaai/pytrthree | pytrthree/utils.py | 1 | 5462 | import datetime
import io
import logging
import os
import re
import sys
import time
from zeep.exceptions import Fault
import pandas as pd
import yaml
from zeep.xsd.valueobjects import CompoundValue
logger = logging.getLogger('pytrthree')
def make_logger(name, config=None) -> logging.Logger:
log_path = os.path.e... | mit |
RobertABT/heightmap | build/matplotlib/examples/pylab_examples/contourf_log.py | 9 | 1350 | '''
Demonstrate use of a log color scale in contourf
'''
from matplotlib import pyplot as P
import numpy as np
from numpy import ma
from matplotlib import colors, ticker, cm
from matplotlib.mlab import bivariate_normal
N = 100
x = np.linspace(-3.0, 3.0, N)
y = np.linspace(-2.0, 2.0, N)
X, Y = np.meshgrid(x, y)
# A ... | mit |
shoyer/xarray | xarray/tests/test_computation.py | 1 | 40215 | import functools
import operator
import pickle
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal
import xarray as xr
from xarray.core.computation import (
_UFuncSignature,
apply_ufunc,
broadcast_compat_data,
collect_dict_values,
join_dict_keys,
o... | apache-2.0 |
jorge2703/scikit-learn | examples/tree/plot_iris.py | 271 | 2186 | """
================================================================
Plot the decision surface of a decision tree on the iris dataset
================================================================
Plot the decision surface of a decision tree trained on pairs
of features of the iris dataset.
See :ref:`decision tree ... | bsd-3-clause |
bthcode/cmake_scipy_ctypes_example | src/python/plotting_utils.py | 2 | 1927 | import matplotlib
################################################################################
# Utility functions intended for use by other functions an classes in this module
################################################################################
def get_plottable( input_axis, object_type ):
'''Get... | bsd-3-clause |
anntzer/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 44 | 2463 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the ... | bsd-3-clause |
xuewei4d/scikit-learn | examples/ensemble/plot_gradient_boosting_regularization.py | 68 | 2848 | """
================================
Gradient Boosting regularization
================================
Illustration of the effect of different regularization strategies
for Gradient Boosting. The example is taken from Hastie et al 2009 [1]_.
The loss function used is binomial deviance. Regularization via
shrinkage (`... | bsd-3-clause |
csgwon/dl-pipeline | train/tools.py | 1 | 1551 | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
name_data = pd.read_csv('data/names/names_train_new.csv', sep='\t')
name_data = name_data.dropna()
labels = ['Arabic', 'Chinese', 'Czech', 'Dutch', 'English', 'French', 'German', 'Greek', 'Irish', 'Italian', 'Japanese', 'Korean', 'Polish', 'Portuguese', ... | apache-2.0 |
CodeMonkeyJan/hyperspy | hyperspy/drawing/_widgets/label.py | 3 | 3731 | # -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy 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 of the License, or
# (at... | gpl-3.0 |
fyffyt/scikit-learn | sklearn/externals/joblib/parallel.py | 79 | 35628 | """
Helpers for embarrassingly parallel code.
"""
# Author: Gael Varoquaux < gael dot varoquaux at normalesup dot org >
# Copyright: 2010, Gael Varoquaux
# License: BSD 3 clause
from __future__ import division
import os
import sys
import gc
import warnings
from math import sqrt
import functools
import time
import thr... | bsd-3-clause |
mfjb/scikit-learn | sklearn/cluster/tests/test_bicluster.py | 226 | 9457 | """Testing for Spectral Biclustering methods"""
import numpy as np
from scipy.sparse import csr_matrix, issparse
from sklearn.grid_search import ParameterGrid
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from... | bsd-3-clause |
3manuek/scikit-learn | examples/calibration/plot_compare_calibration.py | 241 | 5008 | """
========================================
Comparison of Calibration of Classifiers
========================================
Well calibrated classifiers are probabilistic classifiers for which the output
of the predict_proba method can be directly interpreted as a confidence level.
For instance a well calibrated (bi... | bsd-3-clause |
ChanderG/scikit-learn | examples/ensemble/plot_forest_iris.py | 335 | 6271 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
... | bsd-3-clause |
alphaBenj/zipline | zipline/assets/synthetic.py | 3 | 9273 | from itertools import product
from string import ascii_uppercase
import pandas as pd
from pandas.tseries.offsets import MonthBegin
from six import iteritems
from .futures import CME_CODE_TO_MONTH
def make_rotating_equity_info(num_assets,
first_start,
frequ... | apache-2.0 |
golden1232004/webrtc_new | tools/cpu/cpu_mon.py | 6 | 2057 | #!/usr/bin/env python
#
# Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
#
# Use of this source code is governed by a BSD-style license
# that can be found in the LICENSE file in the root of the source
# tree. An additional intellectual property rights grant can be found
# in the file PATENTS. All... | gpl-3.0 |
Garrett-R/scikit-learn | examples/mixture/plot_gmm_classifier.py | 250 | 3918 | """
==================
GMM classification
==================
Demonstration of Gaussian mixture models for classification.
See :ref:`gmm` for more information on the estimator.
Plots predicted labels on both training and held out test data using a
variety of GMM classifiers on the iris dataset.
Compares GMMs with sp... | bsd-3-clause |
ryfeus/lambda-packs | Tensorflow_Pandas_Numpy/source3.6/pandas/core/reshape/melt.py | 1 | 14965 | # pylint: disable=E1101,E1103
# pylint: disable=W0703,W0622,W0613,W0201
import numpy as np
from pandas.core.dtypes.common import is_list_like
from pandas import compat
from pandas.core.arrays import Categorical
from pandas.core.dtypes.generic import ABCMultiIndex
from pandas.core.frame import _shared_docs
from panda... | mit |
tomolaf/trading-with-python | lib/interactivebrokers.py | 77 | 18140 | """
Copyright: Jev Kuznetsov
Licence: BSD
Interface to interactive brokers together with gui widgets
"""
import sys
# import os
from time import sleep
from PyQt4.QtCore import (SIGNAL, SLOT)
from PyQt4.QtGui import (QApplication, QFileDialog, QDialog, QVBoxLayout, QHBoxLayout, QDialogButtonBox,
... | bsd-3-clause |
kpj/OsciPy | old/reconstruction.py | 1 | 7299 | """
Try to reconstruct system parameters from observed solutions
"""
import sys
import numpy as np
import pandas as pd
import networkx as nx
import seaborn as sns
import matplotlib as mpl
import matplotlib.pylab as plt
from tqdm import tqdm, trange
from utils import DictWrapper as DW, save, solve_system
from inves... | mit |
djnugent/mavlink | pymavlink/tools/mavgpslag.py | 43 | 3446 | #!/usr/bin/env python
'''
calculate GPS lag from DF log
'''
import sys, time, os
from argparse import ArgumentParser
parser = ArgumentParser(description=__doc__)
parser.add_argument("--plot", action='store_true', default=False, help="plot errors")
parser.add_argument("--minspeed", type=float, default=6, help="minimu... | lgpl-3.0 |
samuel1208/scikit-learn | sklearn/manifold/tests/test_t_sne.py | 162 | 9771 | import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises_regexp
... | bsd-3-clause |
aimalz/qp | docs/desc-0000-qp-photo-z_approximation/research/production_script.py | 1 | 3763 | def do_case(i):
print(cases[i])
(dirname, n_pdfs, n_params) = cases[i]
(z_low, z_high) = datasets[dirname]['z_ends']
z = np.arange(z_low, z_high, 0.01, dtype='float')
z_range = z_high - z_low
delta_z = z_range / len(z)
with open(datasets[dirname]['filename'], 'rb') as data_file:
li... | mit |
anand-c-goog/tensorflow | tensorflow/examples/tutorials/input_fn/boston.py | 19 | 2448 | # 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 |
ctogle/msched | src/msched/controllers/user.py | 1 | 4035 | import msched.core.settings as st
import msched.core.tools as tl
import msched.core.ctree as ct
import msched.core.stage as sg
import msched.core.controller as cn
import matplotlib.pyplot as plt
import time,random,mpi4py
from mpi4py import MPI
##########################################################################... | mit |
ConeyLiu/spark | python/pyspark/sql/session.py | 6 | 29784 | #
# 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 |
wmvanvliet/mne-python | examples/time_frequency/plot_compute_source_psd_epochs.py | 25 | 3523 | """
=====================================================================
Compute Power Spectral Density of inverse solution from single epochs
=====================================================================
Compute PSD of dSPM inverse solution on single trial epochs restricted
to a brain label. The PSD is compu... | bsd-3-clause |
jcli1023/NSERC-eng234 | sepideh_nn.py | 1 | 4560 | #!/usr/bin/env python3
import pandas as pd
import tensorflow as tf
import numpy as np
import sys
from datetime import datetime
# Constants
CLASSES = {"Half-Circle": 0, "Line": 1, "Sine": 2}
LEARNING_RATE = 0.01
NUMBER_OF_EPOCHS = 1000
REGULAR_DATASET = "0"
REGULAR_DELTA_DATASET = "1"
ORIENTATIONS_DATASET = "2"
ORIEN... | gpl-3.0 |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/sklearn/linear_model/tests/test_least_angle.py | 20 | 26139 | import warnings
import numpy as np
from scipy import linalg
from sklearn.model_selection import train_test_split
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from ... | mit |
ctralie/SlidingWindowVideoTDA | Penn_Action/getActions.py | 1 | 1097 | import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
import os
import subprocess
if __name__ == '__main__':
files = os.listdir('labels')
actions = {}
for f in files:
s = "labels/%s"%f
x = sio.loadmat(s)
a = x['action'][0]
if not a in actions:
... | apache-2.0 |
olebole/astrometry.net | sdss/dr9.py | 2 | 1887 | # This file is part of the Astrometry.net suite.
# Licensed under a 3-clause BSD style license - see LICENSE
from __future__ import print_function
from __future__ import absolute_import
from .common import *
from .dr8 import *
class DR9(DR8):
def __init__(self, **kwargs):
'''
Useful kwargs:
... | bsd-3-clause |
choderalab/openpathsampling | openpathsampling/analysis/tis/flux.py | 3 | 14062 | import collections
import openpathsampling as paths
from openpathsampling.netcdfplus import StorableNamedObject
import pandas as pd
import numpy as np
from .core import MultiEnsembleSamplingAnalyzer
def flux_matrix_pd(flux_matrix, sort_method="default"):
"""Convert dict form of flux to a pandas.Series
Parame... | lgpl-2.1 |
l8orre/XG8 | nxtPwt/nxtModels.py | 1 | 125431 |
#from PyQt4.QtCore import QObject ,QAbstractTableModel, pyqtSignal, pyqtSlot, SIGNAL, QModelIndex , Qt
from PyQt4.Qt import *
from PyQt4.QtGui import QColor, QPixmap, QIcon
import time
from PyQt4.QtCore import QObject , pyqtSignal, pyqtSlot, SIGNAL
from copy import copy
import numpy as np
from nxtPwt.nxtApiPr... | mit |
ominux/scikit-learn | doc/sphinxext/numpy_ext/docscrape_sphinx.py | 22 | 7924 | import re, inspect, textwrap, pydoc
import sphinx
from docscrape import NumpyDocString, FunctionDoc, ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots = config.get('use_plots', False)
NumpyDoc... | bsd-3-clause |
alvason/stochastic-evolution | code/stochastic_diffusion.py | 2 | 9368 |
# coding: utf-8
# # Stochastic infectious pulse
# https://github.com/alvason/stochastic-infectious-pulse
#
# ### Stochastic version for evolutionary insights
# In[24]:
'''
author: Alvason Zhenhua Li
date: 07/07/2015
'''
get_ipython().magic(u'matplotlib inline')
import numpy as np
import matplotlib.pyplot as plt... | gpl-2.0 |
r39132/airflow | tests/contrib/operators/test_hive_to_dynamodb_operator.py | 7 | 5053 | # -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
#... | apache-2.0 |
ASU-CompMethodsPhysics-PHY494/final-rendezvous-with-ramageddon | resources/mdlj.py | 2 | 11667 | #!/usr/bin/env python
# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
#
# Molecular Dynamics of the Lennard Jones Fluid
# Skeleton code --- incomplete.
#
# Written by Oliver Beckstein for ASU PHY494
# http://asu-compmethodsphysics-phy494.github.io/ASU-PH... | gpl-3.0 |
tomlof/scikit-learn | sklearn/neural_network/tests/test_rbm.py | 225 | 6278 | 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 |
toobaz/pandas | pandas/tests/indexes/timedeltas/test_partial_slicing.py | 2 | 3145 | import numpy as np
import pytest
import pandas as pd
from pandas import Series, Timedelta, timedelta_range
from pandas.util.testing import assert_series_equal
class TestSlicing:
def test_slice_keeps_name(self):
# GH4226
dr = pd.timedelta_range("1d", "5d", freq="H", name="timebucket")
asse... | bsd-3-clause |
ndaniels/Ammolite | scripts/overlap_conversion.py | 2 | 1724 | import matplotlib.pyplot as plt
from pylab import polyfit, show
import sys
def parse( filename):
f = open( filename)
lineNum = 0;
molOverlap = []
repOverlap = []
molTanimoto = []
repTanimoto = []
rawMolOverlap = []
rawRepOverlap = []
rawMolTanimoto = []
rawRepTanimoto = []
for line in f:
if lineNum >=... | gpl-2.0 |
raghavrv/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 |
rishikksh20/scikit-learn | examples/text/document_clustering.py | 32 | 8526 | """
=======================================
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 |
bsipocz/AstroHackWeek2015 | day3-machine-learning/plots/plot_interactive_tree.py | 13 | 2539 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.tree import DecisionTreeClassifier
from sklearn.externals.six import StringIO # doctest: +SKIP
from sklearn.tree import export_graphviz
from scipy.misc import imread
from scipy import ndimage
import os
GRAPHVIS_P... | gpl-2.0 |
OshynSong/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | 57 | 8062 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
assert_equal, assert_not_equal,
assert_raises)
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import mak... | bsd-3-clause |
xray/xray | doc/gallery/plot_rasterio.py | 4 | 1486 | """
.. _recipes.rasterio:
=================================
Parsing rasterio's geocoordinates
=================================
Converting a projection's cartesian coordinates into 2D longitudes and
latitudes.
These new coordinates might be handy for plotting and indexing, but it should
be kept in mind that a grid ... | apache-2.0 |
unnati-xyz/droidcon-twitter-analytics | geeksrus/analytics/wordcloud.py | 1 | 6714 | from datetime import datetime
import traceback
import re
from operator import itemgetter
import pandas as pd
import nltk
from nltk.corpus import stopwords
from geeksrus import LOGGER
from geeksrus.utils.dbconn import read_mongo, write_mongo, read_mongo_projection, find_and_sort_desc
from geeksrus import dbcon
from ge... | mit |
atechnicolorskye/Stratospheric-UAV-Simulator | gfs_data_simulator_local.py | 1 | 100831 | """
gfs_data_simulator_local.py
GFS Local Data Simulator
DESCRIPTION
-----------
GFS Data Local Simulator Module:
This module simulates the descent of a UAV through multiple atmopsheric layers. The properties of the layers are obtained from National
Oceanic and Atmospheric Administration's (NOAA) Global Forecast Sys... | gpl-2.0 |
beiko-lab/gengis | bin/Lib/site-packages/matplotlib/testing/jpl_units/__init__.py | 6 | 3064 | #=======================================================================
"""
This is a sample set of units for use with testing unit conversion
of matplotlib routines. These are used because they use very strict
enforcement of unitized data which will test the entire spectrum of how
unitized data might be used (it is... | gpl-3.0 |
rseubert/scikit-learn | benchmarks/bench_covertype.py | 154 | 7296 | """
===========================
Covertype dataset benchmark
===========================
Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART
(decision tree), RandomForest and Extra-Trees on the forest covertype dataset
of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It ... | bsd-3-clause |
pbenya/office-nfl-pool | extra_code/make_datasheet.py | 4 | 3996 | """
make_datasheet.py
~~~~~~~~~~~~~~~~~~
A one-sheet log for the 2015 season.
Read in the CSV in '../data/nfl_season2015.csv' and
write out to '../excel_files/season2015_datasheet.xlsx'
The input file '../data/nfl_season2015.csv' looks like:
Season,Category,Week,Team,Opponent,AtHome,Points,PointsAllowed,Date,Stadiu... | mit |
cauchycui/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 261 | 2836 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from nose.tools import assert_equa... | bsd-3-clause |
pianomania/scikit-learn | examples/linear_model/plot_logistic_l1_l2_sparsity.py | 384 | 2601 | """
==============================================
L1 Penalty and Sparsity in Logistic Regression
==============================================
Comparison of the sparsity (percentage of zero coefficients) of solutions when
L1 and L2 penalty are used for different values of C. We can see that large
values of C give mo... | bsd-3-clause |
RaviSoji/probabilistic_LDA | tests/test_model/test_model_integration.py | 1 | 6430 | # Copyright 2017 Ravi Sojitra. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | apache-2.0 |
Mic92/tthread | src/inspector/graph.py | 2 | 12231 | import sys
import json
from collections import defaultdict
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec, ticker
import matplotlib
FIELDS = [
"times",
# "log_sizes",
# "user_time",
# "alignment-faults",
# 'branch-instru... | gpl-2.0 |
shikhardb/scikit-learn | sklearn/datasets/species_distributions.py | 24 | 7871 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
COSMOGRAIL/PyCS | pycs/tdc/metrics.py | 1 | 29385 | """
Functions related to the TDC metrics
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
############## Here, we work with estimates objects ##############
def fN(estimates):
"""
@param estimates: list of Estimate objects
@return: length of the estimates list
"""
... | gpl-3.0 |
jimcunderwood/MissionPlanner | Lib/site-packages/numpy/core/function_base.py | 82 | 5474 | __all__ = ['logspace', 'linspace']
import numeric as _nx
from numeric import array
def linspace(start, stop, num=50, endpoint=True, retstep=False):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop` ].
Th... | gpl-3.0 |
GitYiheng/reinforcement_learning_test | test03_monte_carlo/t27_rlvps04_hn24_clr0p01.py | 1 | 7659 | import tensorflow as tf # neural network for function approximation
import gym # environment
import numpy as np # matrix operation and math functions
from gym import wrappers
import gym_morph # customized environment for cart-pole
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import time
star... | mit |
pratapvardhan/pandas | pandas/io/parsers.py | 3 | 123349 | """
Module contains tools for processing files into DataFrames or other objects
"""
from __future__ import print_function
from collections import defaultdict
import re
import csv
import sys
import warnings
import datetime
from textwrap import fill
import numpy as np
from pandas import compat
from pandas.compat import... | bsd-3-clause |
DTOcean/dtocean-core | dtocean_core/utils/moorings.py | 1 | 21601 | # -*- coding: utf-8 -*-
# Copyright (C) 2016-2018 Mathew Topper
#
# 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 3 of the License, or
# (at your option) any later... | gpl-3.0 |
miloharper/neural-network-animation | matplotlib/projections/polar.py | 11 | 27444 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import math
import warnings
import numpy as np
import matplotlib
rcParams = matplotlib.rcParams
from matplotlib.axes import Axes
import matplotlib.axis as maxis
from matplotlib import cbook
from m... | mit |
hovo1990/deviser | generator/util/generateCode.py | 1 | 21616 | #!/usr/bin/env python
#
# @file generateCode.py
# @brief function for generating all code files
# @author Frank Bergmann
# @author Sarah Keating
#
# <!--------------------------------------------------------------------------
#
# Copyright (c) 2013-2015 by the California Institute of Technology
# (California, US... | lgpl-2.1 |
apmoore1/semeval | svrs/feature_extractors/Tokeniser.py | 1 | 1328 | from semeval import helper as helper
from sklearn.base import TransformerMixin
from sklearn.base import BaseEstimator
class Tokeniser(BaseEstimator, TransformerMixin):
def __init__(self, ngram_range=(1,1), tokeniser_func=helper.unitok_tokens):
self.ngram_range = ngram_range
self.tokeniser_func ... | gpl-3.0 |
Lawrence-Liu/scikit-learn | sklearn/metrics/tests/test_classification.py | 83 | 49782 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import la... | bsd-3-clause |
gVallverdu/myScripts | VASP/moduleDOS.py | 1 | 10347 | #!/usr/bin/env python3
# -*-coding:utf-8 -*-
"""
Apply scofield cross sections to a DOS
Journal of Electron Spectroscopy and Related Phenomena, 8 (1976) 129-137)
"""
import numpy as np
import matplotlib.pyplot as plt
from pymatgen.electronic_structure.core import Spin, OrbitalType
from pymatgen.io.vasp.outputs import... | gpl-2.0 |
midnightradio/gensim | docs/src/gallery/tutorials/run_word2vec.py | 6 | 25525 | r"""
Word2Vec Model
==============
Introduces Gensim's Word2Vec model and demonstrates its use on the `Lee Evaluation Corpus
<https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>`_.
"""
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging... | gpl-3.0 |
OpenPIV/openpiv-python | openpiv/test/test_windef.py | 2 | 5243 | # -*- coding: utf-8 -*-
"""
Created on Fri Oct 4 14:33:21 2019
@author: Theo
"""
import numpy as np
from openpiv import windef
from openpiv.test import test_process
from openpiv import preprocess
import pathlib
import os
import matplotlib.pyplot as plt
frame_a, frame_b = test_process.create_pair(image_size=256)
... | gpl-3.0 |
PythonCharmers/bokeh | examples/interactions/interactive_bubble/gapminder.py | 20 | 4375 | import pandas as pd
from jinja2 import Template
from bokeh.browserlib import view
from bokeh.models import (
ColumnDataSource, Plot, Circle, Range1d,
LinearAxis, HoverTool, Text,
SingleIntervalTicker,
)
from bokeh.models.actions import Callback
from bokeh.models.widgets import Slider
from bokeh.palettes i... | bsd-3-clause |
nextgenusfs/amptk | amptk/filter.py | 2 | 31579 | #!/usr/bin/env python
from __future__ import (absolute_import, division,
print_function, unicode_literals)
import sys
import os
import argparse
import math
from Bio import SeqIO
from natsort import natsorted
import pandas as pd
import numpy as np
import amptk.amptklib as lib
class colr(object... | bsd-2-clause |
HyperloopTeam/FullOpenMDAO | lib/python2.7/site-packages/matplotlib/pyplot.py | 10 | 120496 | # Note: The first part of this file can be modified in place, but the latter
# part is autogenerated by the boilerplate.py script.
"""
Provides a MATLAB-like plotting framework.
:mod:`~matplotlib.pylab` combines pyplot with numpy into a single namespace.
This is convenient for interactive work, but for programming it
... | gpl-2.0 |
hongchhe/myhadoop | spark/scripts/mllibTest.py | 1 | 5324 | import json
import sys
import traceback
def getDataFrameFromSource(jsonData, hdfsHost="spark-master0", hdfsPort="9000", rootFolder="users"):
"""
get spark DataFrame once the input data source is valid.
Notes:
hdfsHost, hdfsPort, rootFolder are available if jsonData["sourceType"] is hdfs and the hdfsUr... | apache-2.0 |
sgagnon/lyman-tools | roi/extract_local_max.py | 1 | 4059 | #! /usr/bin/env python
"""
This script finds clusters in group FFX (MNI space, smoothed), and then outputs
the peaks within a specified ROI as a csv file.
"""
import numpy as np
import glob
import os
import os.path as op
from scipy import stats
import nibabel as nib
import pandas as pd
from moss import locator
fro... | bsd-2-clause |
0xSteve/detection_learning | P_model/Visualizations/UUAV_depth_finding/analytics.py | 1 | 4407 | '''A script that gathers analytical data regarding the automata.'''
from lrp import Linear_Reward_Penalty as LRP
from mse import MSE
from environment import Environment
from pinger import Pinger
import numpy as np
# import matplotlib.pyplot as plt
import plotly.plotly as py
import plotly.graph_objs as go
# import tune_... | apache-2.0 |
anmolshkl/oppia-ml | scikit_stress_test.py | 1 | 1319 | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn import metrics
from sklearn import svm
import numpy as np
import os
import random
import timeit
import yaml
import load_data
import utilities
def main():
# Load data
X_train, ... | apache-2.0 |
anirudhjayaraman/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | 202 | 3757 | import scipy.sparse as sp
import numpy as np
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.testing import assert_raises_regex, assert_true
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.... | bsd-3-clause |
eggplantbren/DNest4 | code/Templates/Builder/coal.py | 1 | 2682 | import numpy as np
import matplotlib.pyplot as plt
import dnest4.builder as bd
# The data, as a dictionary
data = {}
data["t"] = np.array([1851, 1852, 1853, 1854, 1855, 1856, 1857,
1858, 1859, 1860, 1861, 1862, 1863, 1864,
1865, 1866, 1867, 1868, 1869, 1870, 1871,
... | mit |
gmnamra/python-image-utils | gabor_spaces.py | 1 | 2017 | #!/usr/bin/env python
'''
gabor_threads.py
=========
Sample demonstrates:
- use of multiple Gabor filter convolutions to get Fractalius-like image effect (http://www.redfieldplugins.com/filterFractalius.htm)
- use of python threading to accelerate the computation
Usage
-----
gabor_threads.py [image filename]
'''
... | mit |
chatcannon/numpy | numpy/core/function_base.py | 30 | 12092 | from __future__ import division, absolute_import, print_function
import warnings
import operator
from . import numeric as _nx
from .numeric import (result_type, NaN, shares_memory, MAY_SHARE_BOUNDS,
TooHardError,asanyarray)
__all__ = ['logspace', 'linspace', 'geomspace']
def _index_deprecate(... | bsd-3-clause |
schoolie/bokeh | bokeh/plotting/tests/test_figure.py | 4 | 11797 | from __future__ import absolute_import
import unittest
import pytest
import pandas as pd
from bokeh.core.properties import value
from bokeh.models import (
BoxZoomTool,
ColumnDataSource,
LassoSelectTool,
Legend,
LinearAxis,
PanTool,
ResetTool,
ResizeTool,
Title,
)
import bokeh.plot... | bsd-3-clause |
zooniverse/aggregation | active_weather/old/gold_standard.py | 1 | 1654 | # from __future__ import print_function
# from active_weather import ActiveWeather
import matplotlib.pyplot as plt
import cv2
import numpy as np
import sobel_transform
import csv
directory = "/home/ggdhines/Databases/old_weather/aligned_images/Bear/1940/"
# min_x,max_x,min_y,max_y
region_bounds = (559,3282,1276,2097)... | apache-2.0 |
mcdaniel67/sympy | sympy/plotting/tests/test_plot.py | 43 | 8577 | from sympy import (pi, sin, cos, Symbol, Integral, summation, sqrt, log,
oo, LambertW, I, meijerg, exp_polar, Max, Piecewise)
from sympy.plotting import (plot, plot_parametric, plot3d_parametric_line,
plot3d, plot3d_parametric_surface)
from sympy.plotting.plot import unset... | bsd-3-clause |
mhdella/scikit-learn | examples/cluster/plot_kmeans_assumptions.py | 270 | 2040 | """
====================================
Demonstration of k-means assumptions
====================================
This example is meant to illustrate situations where k-means will produce
unintuitive and possibly unexpected clusters. In the first three plots, the
input data does not conform to some implicit assumptio... | bsd-3-clause |
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