repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
nomadcube/scikit-learn | examples/mixture/plot_gmm_pdf.py | 284 | 1528 | """
=============================================
Density Estimation for a mixture of Gaussians
=============================================
Plot the density estimation of a mixture of two Gaussians. Data is
generated from two Gaussians with different centers and covariance
matrices.
"""
import numpy as np
import ma... | bsd-3-clause |
mikelseverson/Udacity-Deep_Learning-Nanodegree | weight-initialization/helper.py | 153 | 3649 | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def hist_dist(title, distribution_tensor, hist_range=(-4, 4)):
"""
Display histogram of a TF distribution
"""
with tf.Session() as sess:
values = sess.run(distribution_tensor)
plt.title(title)
plt.hist(values, ... | mit |
mhdella/scikit-learn | sklearn/neighbors/tests/test_kd_tree.py | 129 | 7848 | import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.kd_tree import (KDTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dist_metrics import Dista... | bsd-3-clause |
JPalmerio/GRB_population_code | catalogs/GBM_cat/GBM_Ep_constraint_testing.py | 1 | 1178 | import sys
import platform
if platform.system() == 'Linux':
sys.path.insert(0,'/nethome/palmerio/Dropbox/Plotting_GUI/Src')
elif platform.system() == 'Darwin':
sys.path.insert(0,'/Users/palmerio/Dropbox/Plotting_GUI/Src')
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import plotting_functions... | gpl-3.0 |
sjperkins/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | 88 | 31139 | # 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 |
eg-zhang/scikit-learn | sklearn/ensemble/weight_boosting.py | 71 | 40664 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
almarklein/scikit-image | doc/examples/plot_regionprops.py | 2 | 1300 | """
=========================
Measure region properties
=========================
This example shows how to measure properties of labelled image regions.
"""
import math
import matplotlib.pyplot as plt
import numpy as np
from skimage.draw import ellipse
from skimage.morphology import label
from skimage.measure impor... | bsd-3-clause |
ZenDevelopmentSystems/scikit-learn | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number o... | bsd-3-clause |
ifding/ifding.github.io | stylegan2-ada/metrics/clustering.py | 1 | 4125 | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and rel... | mit |
huzq/scikit-learn | examples/applications/plot_outlier_detection_wine.py | 17 | 5819 | """
====================================
Outlier detection on a real data set
====================================
This example illustrates the need for robust covariance estimation
on a real data set. It is useful both for outlier detection and for
a better understanding of the data structure.
We selected two sets o... | bsd-3-clause |
lamastex/scalable-data-science | dbcArchives/2021/000_6-sds-3-x-dl/055_DLbyABr_04-ConvolutionalNetworks.py | 1 | 22551 | # Databricks notebook source
# MAGIC %md
# MAGIC ScaDaMaLe Course [site](https://lamastex.github.io/scalable-data-science/sds/3/x/) and [book](https://lamastex.github.io/ScaDaMaLe/index.html)
# MAGIC
# MAGIC This is a 2019-2021 augmentation and update of [Adam Breindel](https://www.linkedin.com/in/adbreind)'s initial ... | unlicense |
3DGenomes/tadbit | _pytadbit/mapping/analyze.py | 1 | 67679 | """
18 Nov 2014
"""
from warnings import warn
from collections import OrderedDict
from pysam import AlignmentFile
from scipy.stats import norm as sc_norm, skew, kurtosis
from scipy.stats import pearsonr, spearmanr, linregres... | gpl-3.0 |
google/dl_bounds | dl_bounds/src/data.py | 1 | 5898 | # coding=utf-8
# Copyright 2018 Google LLC
#
# 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 t... | apache-2.0 |
yanchen036/tensorflow | tensorflow/examples/learn/text_classification_character_rnn.py | 38 | 4036 | # 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 |
timqian/sms-tools | lectures/5-Sinusoidal-model/plots-code/sineModelAnal-flute.py | 24 | 1179 | import numpy as np
import matplotlib.pyplot as plt
import sys, os, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import stft as STFT
import sineModel as SM
import utilFunctions as UF
(fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__fi... | agpl-3.0 |
nealbob/nealbob.github.io | _site/code/multicore_storage_sim.py | 2 | 2177 | import numpy as np
from matplotlib import pyplot as plt
import time
from multiprocessing import Process
from multiprocessing.queues import Queue
def retry_on_eintr(function, *args, **kw):
while True:
try:
return function(*args, **kw)
except IOError, e:
if e.errno... | mit |
echohenry2006/tvb-library | contrib/from_articles/region_deterministic_bnm_wc.py | 5 | 3642 | # -*- coding: utf-8 -*-
"""
What:
Reproduces Figures 23 and 24 of Sanz-Leon P., Knock, S. A., Spiegler, A. and Jirsa V.
Mathematical framework for large-scale brain network modelling in The Virtual Brain.
Neuroimage, 2014, (in review)
Needs:
A working installation of tvb
Run:
python region_de... | gpl-2.0 |
HaebinShin/tensorflow | tensorflow/examples/skflow/hdf5_classification.py | 5 | 2006 | # 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 |
grantvk/aima-python | submissions/Sery/myNN.py | 13 | 3375 | from sklearn.neural_network import MLPClassifier
import traceback
from submissions.Sery import aids
class DataFrame:
data = []
feature_names = []
target = []
target_names = []
aidsECHP = DataFrame()
aidsECHP.data = []
target_data = []
list_of_report = aids.get_reports()
for record in list_of_report:
... | mit |
fyffyt/scikit-learn | examples/ensemble/plot_adaboost_regression.py | 311 | 1529 | """
======================================
Decision Tree Regression with AdaBoost
======================================
A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tr... | bsd-3-clause |
waynenilsen/statsmodels | statsmodels/tsa/base/tests/test_base.py | 27 | 2106 | import numpy as np
from pandas import Series
from pandas import date_range
from statsmodels.tsa.base.tsa_model import TimeSeriesModel
import numpy.testing as npt
from statsmodels.tools.testing import assert_equal
def test_pandas_nodates_index():
from statsmodels.datasets import sunspots
y = sunspots.load_panda... | bsd-3-clause |
RayMick/scikit-learn | examples/semi_supervised/plot_label_propagation_digits.py | 268 | 2723 | """
===================================================
Label Propagation digits: Demonstrating performance
===================================================
This example demonstrates the power of semisupervised learning by
training a Label Spreading model to classify handwritten digits
with sets of very few labels.... | bsd-3-clause |
Stonelinks/jsbsim | tests/CheckOutputRate.py | 2 | 6059 | # CheckOutputRate.py
#
# A regression test on the output features that allow to set the output rate
# including enabling/disabling the output.
#
# Copyright (c) 2015 Bertrand Coconnier
#
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU General Public License as publ... | lgpl-2.1 |
goldmedal/spark | python/pyspark/sql/tests/test_pandas_udf.py | 5 | 10122 | #
# 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 |
PatrickOReilly/scikit-learn | sklearn/feature_selection/__init__.py | 140 | 1302 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | bsd-3-clause |
turi-code/SFrame | oss_src/unity/python/sframe/test/util.py | 5 | 4772 | '''
Copyright (C) 2016 Turi
All rights reserved.
This software may be modified and distributed under the terms
of the BSD license. See the LICENSE file for details.
'''
import random
import tempfile
import shutil
import math
import string
import numpy as np
from pandas.util.testing import assert_frame_equal
from .. i... | bsd-3-clause |
ODM2/ODMToolsPython | odmtools/gui/pnlPlot.py | 1 | 7003 | #Boa:FramePanel:Panel1
import wx
from wx.lib.pubsub import pub as Publisher
try:
from agw import flatnotebook as fnb
except ImportError: # if it's not there locally, try the wxPython lib.
import wx.lib.agw.flatnotebook as fnb
import matplotlib
matplotlib.use('WXAgg')
import plotTimeSeries
import plotSummar... | bsd-3-clause |
karstenw/nodebox-pyobjc | examples/Extended Application/sklearn/examples/ensemble/plot_random_forest_regression_multioutput.py | 1 | 3492 | """
============================================================
Comparing random forests and the multi-output meta estimator
============================================================
An example to compare multi-output regression with random forest and
the :ref:`multioutput.MultiOutputRegressor <multiclass>` meta-e... | mit |
cbertinato/pandas | pandas/tests/test_downstream.py | 1 | 4179 | """
Testing that we work in the downstream packages
"""
import importlib
import subprocess
import sys
import numpy as np # noqa
import pytest
from pandas.compat import PY36
from pandas import DataFrame
from pandas.util import testing as tm
def import_module(name):
# we *only* want to skip if the module is tru... | bsd-3-clause |
Reagankm/KnockKnock | venv/lib/python3.4/site-packages/matplotlib/testing/image_util.py | 11 | 3765 | # This module contains some functionality from the Python Imaging
# Library, that has been ported to use Numpy arrays rather than PIL
# Image objects.
# The Python Imaging Library is
# Copyright (c) 1997-2009 by Secret Labs AB
# Copyright (c) 1995-2009 by Fredrik Lundh
# By obtaining, using, and/or copying this sof... | gpl-2.0 |
wateraccounting/wa | Collect/MOD9/DataAccess.py | 1 | 12824 | # -*- coding: utf-8 -*-
"""
Authors: Tim Hessels
UNESCO-IHE 2016
Contact: t.hessels@unesco-ihe.org
Repository: https://github.com/wateraccounting/wa
Module: Collect/MOD9
"""
# import general python modules
import os
import numpy as np
import pandas as pd
import gdal
import urllib
import urllib2
from bs4 impor... | apache-2.0 |
Lab603/PicEncyclopedias | jni-build/jni-build/jni/include/tensorflow/contrib/factorization/python/ops/gmm_test.py | 4 | 6387 | # 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... | mit |
nblago/utils | src/model/BBFit.py | 1 | 66521 | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 22 10:57:34 2018
Class that enables to fit a black body function to a set of magntidues.
@author: nadiablago
@version: 0.22
"""
from __future__ import print_function
import matplotlib
from matplotlib import pylab as plt
import corner
from astropy import units as u
impo... | mit |
Jericho/deep-learning | image-classification/helper.py | 155 | 5631 | import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer
def _load_label_names():
"""
Load the label names from file
"""
return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def load_cfar10_batch(ci... | mit |
xiaoxiamii/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 251 | 2022 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
mwv/scikit-learn | sklearn/preprocessing/__init__.py | 268 | 1319 | """
The :mod:`sklearn.preprocessing` module includes scaling, centering,
normalization, binarization and imputation methods.
"""
from ._function_transformer import FunctionTransformer
from .data import Binarizer
from .data import KernelCenterer
from .data import MinMaxScaler
from .data import MaxAbsScaler
from .data ... | bsd-3-clause |
tdegeus/GooseEYE | docs/examples/clusters_dilate_periodic.py | 1 | 2926 | r'''
Plot and/or check.
Usage:
script [options]
Options:
-s, --save Save output for later check.
-c, --check Check against earlier results.
-p, --plot Plot.
-h, --help Show this help.
'''
# <snippet>
import numpy as np
import GooseEYE
# generate image
I = np.zeros((21, 21)... | gpl-3.0 |
aep124/TwitterAnalyticsTools | textonly.py | 1 | 2405 | # this is a script to retrieve and process text-only data for classification
# This process includes four main tasks
# 1) getting raw tweets
# 2) apply labels (this step can be conducted at any time)
# 2) filtering those tweets (e.g., according to CMU POS tagger)
# 3) deriving a set of features (a.k.a. wo... | mit |
felipemontefuscolo/bitme | get_bitmex_candles.py | 1 | 4122 | #!/usr/bin/env python
import sys
import time
import swagger_client
from swagger_client.rest import ApiException
from utils.utils import smart_open
import argparse
import pandas as pd
MAX_NUM_CANDLES_BITMEX = 500
def print_file(file_or_stdout, api_instance, bin_size, partial, symbol, reverse, start_time, end_time)... | mpl-2.0 |
ellio167/lammps | examples/SPIN/test_problems/validation_damped_precession/llg_precession.py | 9 | 1646 | #!/usr/bin/env python3
import numpy as np , pylab, tkinter
import math
import matplotlib.pyplot as plt
import mpmath as mp
mub=5.78901e-5 # Bohr magneton (eV/T)
hbar=0.658212 # Planck's constant (eV.fs/rad)
g=2.0 # Lande factor (adim)
gyro=g*mub/hbar # gyromag ratio (rad/f... | gpl-2.0 |
SanPen/GridCal | src/research/PTDF/ACPTDF_research2.py | 1 | 14022 | # This file is part of GridCal.
#
# GridCal 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 version.
#
# GridCal is distributed in the hope that... | gpl-3.0 |
depet/scikit-learn | sklearn/neighbors/tests/test_dist_metrics.py | 48 | 4949 | import itertools
import numpy as np
from numpy.testing import assert_array_almost_equal
import scipy
from scipy.spatial.distance import cdist
from sklearn.neighbors.dist_metrics import DistanceMetric
from nose import SkipTest
def cmp_version(version1, version2):
version1 = tuple(map(int, version1.split('.')[:2]... | bsd-3-clause |
nmartensen/pandas | pandas/tests/indexing/test_callable.py | 14 | 8721 | # -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import numpy as np
import pandas as pd
import pandas.util.testing as tm
class TestIndexingCallable(object):
def test_frame_loc_ix_callable(self):
# GH 11485
df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': list('aabb'),
... | bsd-3-clause |
sinhrks/scikit-learn | examples/hetero_feature_union.py | 288 | 6236 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of hetero... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/sklearn/model_selection/_validation.py | 4 | 53401 | """
The :mod:`sklearn.model_selection._validation` module includes classes and
functions to validate the model.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Raghav RV <rvraghav93@gma... | mit |
quheng/scikit-learn | examples/decomposition/plot_image_denoising.py | 181 | 5819 | """
=========================================
Image denoising using dictionary learning
=========================================
An example comparing the effect of reconstructing noisy fragments
of the Lena image using firstly online :ref:`DictionaryLearning` and
various transform methods.
The dictionary is fitted o... | bsd-3-clause |
gclenaghan/scikit-learn | examples/preprocessing/plot_function_transformer.py | 158 | 1993 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you ca... | bsd-3-clause |
akraft196/pyASC | examples/mplot1.py | 1 | 7267 | #! /usr/bin/env python
#
# quick and dirty processing of the MD All Sky images
from astropy.io import fits
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy.misc import imsave
import numpy as np
import aplpy
import argparse as ap
import os.path
import logging
import time
def d(ff,box=[]):... | mit |
alexsavio/scikit-learn | sklearn/metrics/cluster/bicluster.py | 359 | 2797 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_consistent_length, check_array
__all__ = ["consensus_score"]
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shap... | bsd-3-clause |
plissonf/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
ifuding/Kaggle | PMRCN/Code/siamese_net.py | 1 | 22230 |
from sklearn import *
import sklearn
import pandas as pd
import numpy as np
import xgboost as xgb
import lightgbm as lgb
from time import gmtime, strftime
import numpy.random as rng
from multiprocessing.dummy import Pool
import h5py
import concurrent.futures
import tensorflow as tf
import multiprocessing as mp
from s... | apache-2.0 |
lhilt/scipy | scipy/stats/tests/test_morestats.py | 4 | 70469 | # Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.random import RandomState
from numpy.testing import (assert_array_equal,
assert_almost_equal, ass... | bsd-3-clause |
wangsharp/trading-with-python | spreadApp/makeDist.py | 77 | 1720 | from distutils.core import setup
import py2exe
manifest_template = '''
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
<assemblyIdentity
version="5.0.0.0"
processorArchitecture="x86"
name="%(prog)s"
type="win32"... | bsd-3-clause |
gfyoung/pandas | pandas/tests/groupby/test_bin_groupby.py | 2 | 3023 | import numpy as np
import pytest
from pandas._libs import lib, reduction as libreduction
import pandas as pd
from pandas import Series
import pandas._testing as tm
def test_series_grouper():
obj = Series(np.random.randn(10))
dummy = obj.iloc[:0]
labels = np.array([-1, -1, -1, 0, 0, 0, 1, 1, 1, 1], dtyp... | bsd-3-clause |
fabioticconi/scikit-learn | examples/linear_model/plot_lasso_coordinate_descent_path.py | 42 | 2944 | """
=====================
Lasso and Elastic Net
=====================
Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.
The coefficients can be forced to be positive.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from itert... | bsd-3-clause |
Obus/scikit-learn | doc/datasets/mldata_fixture.py | 367 | 1183 | """Fixture module to skip the datasets loading when offline
Mock urllib2 access to mldata.org and create a temporary data folder.
"""
from os import makedirs
from os.path import join
import numpy as np
import tempfile
import shutil
from sklearn import datasets
from sklearn.utils.testing import install_mldata_mock
fr... | bsd-3-clause |
markcheno/trading-with-python | lib/classes.py | 76 | 7847 | """
worker classes
@author: Jev Kuznetsov
Licence: GPL v2
"""
__docformat__ = 'restructuredtext'
import os
import logger as logger
import yahooFinance as yahoo
from functions import returns, rank
from datetime import date
from pandas import DataFrame, Series
import numpy as np
import pandas as pd
im... | bsd-3-clause |
jjx02230808/project0223 | sklearn/ensemble/partial_dependence.py | 251 | 15097 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals im... | bsd-3-clause |
vsoch/expfactory-docker | expdj/settings.py | 2 | 5848 | """
Django settings for expdj project.
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
import sys
import tempfile
from datetime import timedelta
import matplotlib
from celery import Celery
from kombu import Exchange, Queue
matplotlib.use('Agg')
BASE_DIR = os.path.dirname(os.pat... | mit |
datacommonsorg/data | scripts/us_bjs/nps/preprocess_data.py | 1 | 19122 | import pandas as pd
from absl import flags
from absl import app
FLAGS = flags.FLAGS
flags.DEFINE_string('preprocess_file',
'NPS_1978-2018_Data.tsv',
'file path to tsv file with data to proess',
short_name='p')
def convert_nan_for_calculation(value):
if ... | apache-2.0 |
apache/incubator-mxnet | python/mxnet/numpy/random.py | 4 | 40839 | # 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 u... | apache-2.0 |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/tests/frame/test_query_eval.py | 11 | 42389 | # -*- coding: utf-8 -*-
from __future__ import print_function
import operator
import pytest
from pandas.compat import (zip, range, lrange, StringIO)
from pandas import DataFrame, Series, Index, MultiIndex, date_range
import pandas as pd
import numpy as np
from numpy.random import randn
from pandas.util.testing imp... | mit |
jniediek/mne-python | examples/visualization/plot_evoked_topomap.py | 13 | 1606 | """
========================================
Plotting topographic maps of evoked data
========================================
Load evoked data and plot topomaps for selected time points.
"""
# Authors: Christian Brodbeck <christianbrodbeck@nyu.edu>
# Tal Linzen <linzen@nyu.edu>
# Denis A. Engeman <... | bsd-3-clause |
RPGOne/Skynet | 5230d93ccc9fa5329b0a02a351b02939-459eebff35e625675d2f6ff5633c7051c1d64a0e/gistfile1.py | 1 | 3974 | """
python speedup_kmeans.py --profile
python speedup_kmeans.py
git worktree add workdir_master master
rob sedr "\<sklearn\>" sklearn_master True
git mv sklearn sklearn_master
python setup develop
python -c "import sklearn_master; print(sklearn_master.__file__)"
python -c "import sklearn; print(sklearn.__file__)"
"""
... | bsd-3-clause |
JoeBartelmo/PyDetect | gui/img_proc/GlobalSurveyor.py | 2 | 10667 | # Copyright (c) 2016, Jeffrey Maggio and Joseph Bartelmo
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
# associated documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, ... | mit |
michigraber/scikit-learn | sklearn/decomposition/truncated_svd.py | 199 | 7744 | """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 |
wataash/Instr | instr/ke2636a.py | 1 | 3794 | import numpy as np
import unittest2
from instr.base import SourceMeter
class Keithley2636A(SourceMeter):
def __init__(self, rsrc=None, timeout_sec=600, reset=True):
self._smu = 'a'
idn = 'Keithley Instruments Inc., Model 2636A'
super().__init__(rsrc, idn, timeout_sec, reset)
@proper... | mit |
arank/mxnet | example/reinforcement-learning/ddpg/strategies.py | 15 | 1705 | import numpy as np
class BaseStrategy(object):
"""
Base class of exploration strategy.
"""
def get_action(self, obs, policy):
raise NotImplementedError
def reset(self):
pass
class OUStrategy(BaseStrategy):
"""
Ornstein-Uhlenbeck process: dxt = theta * (mu - xt) * dt +... | apache-2.0 |
frank-tancf/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 |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/io/json/table_schema.py | 12 | 5184 | """
Table Schema builders
http://specs.frictionlessdata.io/json-table-schema/
"""
from pandas.core.dtypes.common import (
is_integer_dtype, is_timedelta64_dtype, is_numeric_dtype,
is_bool_dtype, is_datetime64_dtype, is_datetime64tz_dtype,
is_categorical_dtype, is_period_dtype, is_string_dtype
)
def as_js... | mit |
xavierwu/scikit-learn | sklearn/cluster/tests/test_k_means.py | 63 | 26190 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
JensTimmerman/radical.pilot | src/radical/pilot/utils/analysis.py | 1 | 12671 |
import os
# ------------------------------------------------------------------------------
#
def get_experiment_frames(experiments, datadir=None):
"""
read profiles for all sessions in the given 'experiments' dict. That dict
is expected to be like this:
{ 'test 1' : [ [ 'rp.session.thinkie.merzky.01... | mit |
alexandrebarachant/mne-python | mne/decoding/tests/test_ems.py | 1 | 3384 | # Author: Denis A. Engemann <d.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_equal, assert_raises
from mne import io, Epochs, read_events, pick_types
from mne.utils import requires_sklearn, chec... | bsd-3-clause |
muku42/bokeh | bokeh/charts/builder/tests/test_dot_builder.py | 4 | 3924 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
sbg2133/miscellaneous_projects | carina/ItoNH.py | 1 | 1115 | import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import aplpy
from astropy.wcs import WCS
import sys, os
from getIQU import IQU
from astropy import coordinates as coord
from astropy.coordinates import SkyCoord
from astropy import units as u
from scipy.interpolate import griddata
plt.ion()
... | gpl-3.0 |
gfyoung/pandas | pandas/tests/indexes/timedeltas/test_join.py | 4 | 1497 | import numpy as np
from pandas import Index, Timedelta, timedelta_range
import pandas._testing as tm
class TestJoin:
def test_append_join_nondatetimeindex(self):
rng = timedelta_range("1 days", periods=10)
idx = Index(["a", "b", "c", "d"])
result = rng.append(idx)
assert isinstan... | bsd-3-clause |
TheChymera/LabbookDB | labbookdb/tests/test_report.py | 1 | 3726 | import pytest
from os import path
DB_PATH = '~/.demolog/meta.db'
DATA_DIR = path.join(path.dirname(path.realpath(__file__)),'../../example_data/')
def test_implant_angle_filter():
from labbookdb.report.selection import animal_id, animal_treatments, animal_operations
import numpy as np
db_path=DB_PATH
df = animal... | bsd-3-clause |
blink1073/scikit-image | doc/examples/edges/plot_active_contours.py | 4 | 3317 | """
====================
Active Contour Model
====================
The active contour model is a method to fit open or closed splines to lines or
edges in an image. It works by minimising an energy that is in part defined by
the image and part by the spline's shape: length and smoothness. The
minimization is done impl... | bsd-3-clause |
KitwareMedical/ITKTubeTK | examples/archive/SegmentVesselsUsingNeuralNetworks/scripts/PreProcessing.py | 4 | 6399 | #!/usr/bin/python
###########################################################################
# PreProcessing.py :
#
# Iterate through the expert labelmap and create 65x65 patches around the
# central pixel. All positive pixels are used as positives input cases.
# The same amount of negatives is randomly picked. For e... | apache-2.0 |
mrshu/scikit-learn | examples/plot_permutation_test_for_classification.py | 1 | 2236 | """
=================================================================
Test with permutations the significance of a classification score
=================================================================
In order to test if a classification score is significative a technique
in repeating the classification procedure aft... | bsd-3-clause |
aruneral01/auto-sklearn | autosklearn/estimators.py | 5 | 4834 | import os
import random
import shutil
import numpy as np
import autosklearn.automl
from autosklearn.constants import *
class AutoSklearnClassifier(autosklearn.automl.AutoML):
"""This class implements the classification task. It must not be pickled!
Parameters
----------
time_left_for_this_task : in... | bsd-3-clause |
aabadie/scikit-learn | examples/mixture/plot_concentration_prior.py | 25 | 5631 | """
========================================================================
Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture
========================================================================
This example plots the ellipsoids obtained from a toy dataset (mixture of three
Gaussians) fitte... | bsd-3-clause |
bnoi/scikit-tracker | sktracker/tracker/cost_function/tests/test_abstract_cost_functions.py | 1 | 1500 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from nose.tools import assert_raises
import sys
import pandas as pd
import numpy as np
from sktracker.tracker.cost_function import AbstractCostF... | bsd-3-clause |
viisar/brew | brew/selection/dynamic/dsknn.py | 3 | 3563 | import numpy as np
from brew.base import Ensemble
from brew.metrics.diversity.paired import kuncheva_double_fault_measure
from .base import DCS
class DSKNN(DCS):
"""DS-KNN
The DS-KNN selects an ensemble of classifiers based on
their accuracy and diversity in the neighborhood of the
test sample.
... | mit |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/skimage/viewer/canvastools/linetool.py | 43 | 6911 | import numpy as np
from matplotlib import lines
from ...viewer.canvastools.base import CanvasToolBase, ToolHandles
__all__ = ['LineTool', 'ThickLineTool']
class LineTool(CanvasToolBase):
"""Widget for line selection in a plot.
Parameters
----------
manager : Viewer or PlotPlugin.
Skimage vi... | mit |
CopyChat/Plotting | Python/PythonNetCDF.py | 1 | 10821 | '''
NAME
NetCDF with Python
PURPOSE
To demonstrate how to read and write data with NetCDF files using
a NetCDF file from the NCEP/NCAR Reanalysis.
Plotting using Matplotlib and Basemap is also shown.
PROGRAMMER(S)
Chris Slocum
REVISION HISTORY
20140320 -- Initial version created and posted onlin... | gpl-3.0 |
ephes/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
equialgo/scikit-learn | examples/hetero_feature_union.py | 81 | 6241 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of hetero... | bsd-3-clause |
mirestrepo/voxels-at-lems | registration_eval/results/compute_trans_geo_accuracy.py | 1 | 13935 | #!/usr/bin/env python
# encoding: utf-8
"""
compute_transformation_error.py
Created by Maria Isabel Restrepo on 2012-09-24.
Copyright (c) 2012 . All rights reserved.
This script computes the distances betweeen an estimated similarity transformation and its ground truth
The transformation is used to transform a "source... | bsd-2-clause |
MechCoder/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
KDD-OpenSource/geox-young-academy | day-3/Kalman-filter_Mark.py | 1 | 1494 | # -*- coding: utf-8 -*-
"""
Created on Wed Oct 11 10:10:24 2017
@author: Mark
"""
import numpy as np
import matplotlib.pyplot as plt
#Define functions
def model(state_0,A,B):
state_1 = A*state_0 + np.random.normal(0,B)
return state_1
state_null=np.random.normal(0,0.4)
def observation_functi... | mit |
mskwark/PconsC3 | extra/arne/MSA/find-intradom.py | 1 | 1381 | #!/usr/bin/env perl
# Find all contacts beween domains..
import sys, os, re, string
import argparse
from os.path import expanduser
home = expanduser("~")
sys.path.append(home + '/bioinfo-toolbox/parsing')
sys.path.append(home + '/git/bioinfo-toolbox/parsing')
import parse_contacts
import numpy as np
import matplotl... | gpl-2.0 |
goulu/Goulib | Goulib/plot.py | 1 | 4898 | """
plotable rich object display on IPython/Jupyter notebooks
"""
__author__ = "Philippe Guglielmetti"
__copyright__ = "Copyright 2015, Philippe Guglielmetti"
__credits__ = []
__license__ = "LGPL"
# import matplotlib and set backend once for all
from . import itertools2
import os
import io
import sys
i... | lgpl-3.0 |
elingg/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | 88 | 31139 | # 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 |
yavalvas/yav_com | build/matplotlib/lib/mpl_toolkits/axes_grid1/mpl_axes.py | 8 | 4971 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
import matplotlib.axes as maxes
from matplotlib.artist import Artist
from matplotlib.axis import XAxis, YAxis
class SimpleChainedObjects(object):
def __init__(self, objects):
... | mit |
wavelets/zipline | tests/test_batchtransform.py | 3 | 9739 | #
# 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 |
Srisai85/scikit-learn | sklearn/decomposition/tests/test_truncated_svd.py | 240 | 6055 | """Test truncated SVD transformer."""
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_raises, assert_greater,
... | bsd-3-clause |
rsignell-usgs/PySeidon | pyseidon/tidegaugeClass/plotsTidegauge.py | 2 | 1096 | #!/usr/bin/python2.7
# encoding: utf-8
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as Tri
import matplotlib.ticker as ticker
import seaborn
class PlotsTidegauge:
"""'Plots' subset of Tidegauge class gathers plotting functions"""
def __init__(self, v... | agpl-3.0 |
chrismattmann/tika-similarity | sk_kmeans.py | 2 | 4409 | #!/usr/bin/env python2.7
#
# 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 |
dtusar/coco | code-postprocessing/bbob_pproc/compall/pprldmany.py | 3 | 29654 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
"""Generates figure of the bootstrap distribution of ERT.
The main method in this module generates figures of Empirical
Cumulative Distribution Functions of the bootstrap distribution of
the Expected Running Time (ERT) divided by the dimension for many
algorithms.
T... | bsd-3-clause |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.