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 |
|---|---|---|---|---|---|
syl20bnr/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/blocking_input.py | 69 | 12119 | """
This provides several classes used for blocking interaction with figure windows:
:class:`BlockingInput`
creates a callable object to retrieve events in a blocking way for interactive sessions
:class:`BlockingKeyMouseInput`
creates a callable object to retrieve key or mouse clicks in a blocking way for int... | gpl-3.0 |
jreback/pandas | pandas/tests/indexes/timedeltas/test_timedelta_range.py | 3 | 3258 | import numpy as np
import pytest
from pandas import Timedelta, timedelta_range, to_timedelta
import pandas._testing as tm
from pandas.tseries.offsets import Day, Second
class TestTimedeltas:
def test_timedelta_range(self):
expected = to_timedelta(np.arange(5), unit="D")
result = timedelta_range... | bsd-3-clause |
pypot/scikit-learn | examples/cluster/plot_cluster_comparison.py | 246 | 4684 | """
=========================================================
Comparing different clustering algorithms on toy datasets
=========================================================
This example aims at showing characteristics of different
clustering algorithms on datasets that are "interesting"
but still in 2D. The last ... | bsd-3-clause |
ritchyteam/odoo | addons/resource/faces/timescale.py | 170 | 3902 | ############################################################################
# Copyright (C) 2005 by Reithinger GmbH
# mreithinger@web.de
#
# This file is part of faces.
#
# faces is free software; you can redistribute it and/or modify
# ... | agpl-3.0 |
aoehmichen/eae-docker | jupyter-sparkless/jupyter_notebook_config.py | 1 | 21983 | # Configuration file for jupyter-notebook.
#------------------------------------------------------------------------------
# Application(SingletonConfigurable) configuration
#------------------------------------------------------------------------------
## This is an application.
## The date format used by logging f... | apache-2.0 |
rampasek/seizure-prediction | features/entropy.py | 1 | 10855 | from __future__ import division
import warnings
import os, sys
import numpy as np
import scipy.spatial
import scipy.weave
import scipy.stats.kde
import matplotlib.pyplot as pp
import bisect
# apparently scipy.weave is depricated. I really shouldn't have used it.
# the best thing would probably be to port entropy_nn()... | gpl-2.0 |
basnijholt/holoviews | setup.py | 1 | 9955 | #!/usr/bin/env python
import sys, os
import shutil
from collections import defaultdict
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
setup_args = {}
install_requires = ['param>=1.8.0,<2.0', 'numpy>=1.0', 'pyviz_comms>=0.7.0']
extras_require = {}
# Notebook dependenc... | bsd-3-clause |
antoinecarme/sklearn_explain | reason_codes/settings.py | 1 | 1038 | import pandas as pd
import numpy as np
class cScoreExplainerConfig:
def __init__(self):
self.mFeatureNames = None
self.mCategoricalFeatureNames = None
self.mScoreBins = 5 # score binning
self.mFeatureBins = 5 # feature binning
self.mCustomFeatureQuantiles = None
sel... | bsd-3-clause |
james-pack/ml | pack/ml/mnist/train_classifier.py | 1 | 7499 | import argparse
import concurrent.futures
from contextlib import ExitStack
import math
import numpy as np
from matplotlib import pyplot as plt
from pack.ml.datasets.validation_sets import ValidationSets
from pack.ml.mnist.mnist_data import MnistData
from pack.ml.nodes.file_set import FileSet
from pack.ml.nodes.loss_vi... | mit |
myt00seven/svrg | cifar/draw_3_BN.py | 4 | 9604 | # This is used to draw three comparisons for SGD+BN, SVRG+BN and Streaming SVRG +BN
import matplotlib
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import pylab
import numpy as np
import sys
# all the methods are with BN layers!!!
PATH_DATA_adagrad = "data_3_BN/"
PATH_DATA_SVRG = "data_3_BN/"
... | mit |
diegocavalca/Studies | phd-thesis/benchmarkings/Imaging-NILM-time-series/Imaging-time-series-to-improve-classification-and-imputation-master/serie2QMlib.py | 2 | 5921 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 11 13:45:33 2014
Modified on Wed Jan 27 15:36:00 2016
@author: Stephen Wang
"""
import pandas as pd
import numpy as np
import scipy.io as sio
import pickle
def output_arff(dataset, filename="data.arff"):
outfile = open(filename, 'w')
features = {}
labels = ... | cc0-1.0 |
nesterione/scikit-learn | examples/manifold/plot_compare_methods.py | 259 | 4031 | """
=========================================
Comparison of Manifold Learning methods
=========================================
An illustration of dimensionality reduction on the S-curve dataset
with various manifold learning methods.
For a discussion and comparison of these algorithms, see the
:ref:`manifold module... | bsd-3-clause |
John-Jumper/Upside-MD | py/tensorflow_upside.py | 1 | 11516 | import sys
import time
import tables as tb
import numpy as np
import tempfile
import subprocess as sp
import os
import cPickle as cp
import collections
import uuid
import predict_chi1
import upside_engine as ue
from mpi4py import MPI
import mpi_collective_object as mco
import threading
gensym_salt = str(uuid.uuid4())... | gpl-2.0 |
zertan/PTR-Pipeline | setup.py | 2 | 4674 | """A bioinformatics pipeline for estimation of relative cell periods."""
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open
from os import environ
environ['LC_ALL']='en_US.UTF-8'
environ['LANG']='en_US.UTF-8'
try:
import py... | gpl-2.0 |
ChanderG/scikit-learn | examples/linear_model/plot_lasso_and_elasticnet.py | 249 | 1982 | """
========================================
Lasso and Elastic Net for Sparse Signals
========================================
Estimates Lasso and Elastic-Net regression models on a manually generated
sparse signal corrupted with an additive noise. Estimated coefficients are
compared with the ground-truth.
"""
print(... | bsd-3-clause |
binghongcha08/pyQMD | GWP/2D/1.1.0/plt.py | 14 | 1041 | ##!/usr/bin/python
import numpy as np
import pylab as plt
import seaborn as sns
sns.set_context('poster')
#with open("traj.dat") as f:
# data = f.read()
#
# data = data.split('\n')
#
# x = [row.split(' ')[0] for row in data]
# y = [row.split(' ')[1] for row in data]
#
# fig = plt.figure()
#
# ax1 ... | gpl-3.0 |
errcHuang/pyRM114 | pyrm114/interface.py | 1 | 15378 | from sklearn.metrics import *
from collections import namedtuple
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
import subprocess
import argparse
import random
import ntpath
#TO-DO: make training echo like classify is
#basically training and test set partitioning is outside scope of class,
#th... | mit |
navijo/FlOYBD | DataMining/weather/ml/linearRegression.py | 2 | 6641 | from pyspark import SparkContext, SparkConf
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.regression import LinearRegression
from pyspark.ml.tuning import ParamGridBuilder, TrainValidationSplit
from pyspark.ml.feature import VectorAssembler
from pyspark.sql import SQLContext, SparkSession
from... | mit |
Roibal/Geotechnical_Engineering_Python_Code | Ventilation-Mining-Engineering/Data-Analysis-Examples-Ventilation-Mining/Data_Display_TestUGMine.py | 1 | 4160 | import matplotlib.pyplot as plt
import csv
import datetime
"""
The Purpose of "Data_Display_TestUGMine.py" is to load data and display data
collected by the two data collection units (raspberry pi & sense Hat)
for the purpose of ventilation engineering.
The Data collected in this example was collected over a period... | mit |
marionleborgne/nupic.research | projects/sequence_prediction/mackey_glass/visualize_results.py | 13 | 1819 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | agpl-3.0 |
PMBio/limix | limix/deprecated/modules/lmmlasso.py | 2 | 8245 | '''
Created on Dec 19, 2013
@author: johannes stephan, barbara rakitsch
'''
from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
import sklearn.cross_validation as cross_validation
from .varianceDecomposition import VarianceDecomposition
from . import qtl
import scipy as SP
import nu... | apache-2.0 |
cdiazbas/LMpyMilne | Versions/LMmilne_2cycle.py | 1 | 10566 | '''
LMEI: Levenberg-Marquardt (with constrain) for a Milne-Eddignton atmosphere Inversion
'''
from mutils2 import *
from milne import *
from numpy import arange, pi, sqrt, array, ones, imag, real, sign, random, diag, load
from lmfit import minimize, Parameters, fit_report
def inversionStokes(p0,x,yc,param,Chitol,Maxi... | mit |
Luttik/mellowcakes_prototype | freya/machine_learning/test.py | 1 | 1655 | from sklearn.model_selection import cross_val_score
from ..email_analysis_module import get_encoded_matrix
import numpy as np
def test(x, y, model, cv=10):
results = np.array(list(cross_val_score(model, x, y, cv=cv)))
return results.mean(), results.std()
def dict_chain(dict):
output = {}
for label_1... | mit |
vdrhtc/Measurement-automation | lib2/fulaut/ResonatorOracle.py | 1 | 4919 | from loggingserver import LoggingServer
from scipy import *
import pickle
from scipy.signal import argrelextrema
from matplotlib import gridspec, pyplot as plt
from lib2.ExperimentParameters import ResonatorOracleParameters, GlobalParameters
from lib2.ResonatorDetector import ResonatorDetector
import os
class Resonato... | gpl-3.0 |
niketanpansare/incubator-systemml | src/main/python/systemml/defmatrix.py | 7 | 48505 | # -------------------------------------------------------------
#
# 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 unde... | apache-2.0 |
BorisJeremic/Real-ESSI-Examples | analytic_solution/test_cases/Contact/Interface_Mesh_Types/Interface_1/SoftContact_ElPPlShear/Interface_Test_Shear_Plot.py | 1 | 3515 | #!/usr/bin/python
import h5py
import matplotlib.pylab as plt
import matplotlib as mpl
import sys
import numpy as np;
plt.rcParams.update({'font.size': 28})
# set tick width
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 5
mpl.rcParams['xtick.minor.size'] = 10
mpl.rcParams['xtick.minor.width... | cc0-1.0 |
cpcloud/blaze | blaze/server/server.py | 2 | 13284 | from __future__ import absolute_import, division, print_function
import socket
import functools
import re
from warnings import warn
import collections
import flask
from flask import Blueprint, Flask, request, Response
try:
from bokeh.server.crossdomain import crossdomain
except ImportError:
def crossdomain(*... | bsd-3-clause |
Karel-van-de-Plassche/QLKNN-develop | qlknn/dataset/prep_datasets.py | 1 | 7789 | import os
import time
import xarray as xr
import numpy as np
from qlknn.dataset.hypercube_to_pandas import *
@profile
def load_megarun1_ds(rootdir='.'):
""" Load the 'megarun1' data as xarray/dask dataset
For the megarun1 dataset, the data is split in the 'total fluxes + growth rates'
and 'TEM/ITG/ETG fl... | mit |
jblackburne/scikit-learn | sklearn/metrics/base.py | 46 | 4627 | """
Common code for all metrics
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Arnaud Joly <a.joly@ulg.ac.be>
# Jochen Wersdorfer <jochen@wersdoerfer.de>
# Lars Buitinck
... | bsd-3-clause |
AstroHackWeek/AstroHackWeek2015 | day4-day5-inference/straightline_utils.py | 8 | 3701 | # numpy: numerical library
import numpy as np
# avoid broken installs by forcing Agg backend...
#import matplotlib
#matplotlib.use('Agg')
# pylab: matplotlib's matlab-like interface
import pylab as plt
# The data we will fit, sometimes:
# x, y, sigma_y
data1 = np.array([[201,592,61],[244,401,25],[47,583,38],[287,402,... | gpl-2.0 |
csferrie/python-qinfer | doc/source/conf.py | 3 | 12884 | # -*- coding: utf-8 -*-
#
# QInfer documentation build configuration file, created by
# sphinx-quickstart on Tue Aug 14 21:12:57 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All ... | agpl-3.0 |
fzalkow/scikit-learn | examples/svm/plot_oneclass.py | 249 | 2302 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
... | bsd-3-clause |
khkaminska/scikit-learn | examples/decomposition/plot_pca_iris.py | 253 | 1801 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
PCA example with Iris Data-set
=========================================================
Principal Component Analysis applied to the Iris dataset.
See `here <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ fo... | bsd-3-clause |
ioam/holoviews | holoviews/plotting/mpl/plot.py | 2 | 48619 | from __future__ import absolute_import, division, unicode_literals
from itertools import chain
from contextlib import contextmanager
import param
import numpy as np
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D # noqa (For 3D plots)
from matplotlib import pyplot as plt
from matplotlib import grid... | bsd-3-clause |
AWI-Paleodyn/Python_Helpers | plot_tools/custom_cmap.py | 2 | 3143 | '''
NAME
Custom Colormaps for Matplotlib
PURPOSE
This program shows how to implement make_cmap which is a function that
generates a colorbar. If you want to look at different color schemes,
check out https://kuler.adobe.com/create.
PROGRAMMER(S)
Chris Slocum
REVISION HISTORY
20130411 -- Initial... | gpl-2.0 |
umuzungu/zipline | zipline/sources/data_frame_source.py | 5 | 5146 | #
# Copyright 2015 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 |
nortti/trump-tweet-reception-predictor | word_count.py | 1 | 6296 | #!/usr/bin/env python
import pandas as pd
import string
import plotly as py
import plotly.graph_objs as go
from nltk.stem.porter import PorterStemmer
from collections import Counter
import operator
def generate(json_data, out_dir, num_top_words = 25):
stopword_file = "stop-word-list.csv"
# Data IN
df = pd... | bsd-3-clause |
btabibian/scikit-learn | sklearn/decomposition/base.py | 5 | 5613 | """Principal Component Analysis Base Classes"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <denis-alexander.engemann@inria.fr>
# Kyle Kastner <kastnerkyle@gmail.com>
... | bsd-3-clause |
ankurankan/pgmpy | pgmpy/tests/test_estimators/test_CITests.py | 2 | 8176 | import unittest
import math
import numpy as np
import pandas as pd
from numpy import testing as np_test
from pgmpy.estimators.CITests import *
np.random.seed(42)
class TestPearsonr(unittest.TestCase):
def setUp(self):
self.df_ind = pd.DataFrame(np.random.randn(10000, 3), columns=["X", "Y", "Z"])
... | mit |
mattilyra/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | bsd-3-clause |
nolanliou/tensorflow | tensorflow/contrib/timeseries/examples/multivariate.py | 67 | 5155 | # 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 |
olinguyen/self-driving-cars | p3-behavioral-cloning/model.py | 1 | 4941 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cv2
import math
from keras.layers.core import Dense, Activation, Flatten, Dropout, Reshape, Lambda
from keras.activations import relu, softmax
from keras.layers.pooling import MaxPooling2D
from keras.layers.convolutional import Convolution2D... | mit |
pwcazenave/PyFVCOM | examples/pyfvcom_plot_example.py | 1 | 3605 |
# coding: utf-8
# ### PyFVCOM plotting tools examples
#
# Here, we demonstrate plotting in three different dimensions: horizontal space, vertical space and time.
#
# First we load some model output into an object which can be passed to a number of plotting objects. These objects have methods for plotting different ... | mit |
h2educ/scikit-learn | sklearn/tests/test_naive_bayes.py | 70 | 17509 | import pickle
from io import BytesIO
import numpy as np
import scipy.sparse
from sklearn.datasets import load_digits, load_iris
from sklearn.cross_validation import cross_val_score, train_test_split
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.te... | bsd-3-clause |
SimonBiggs/electronfactors | Spline modelling electron insert factors standalone example.py | 1 | 20465 | import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')
from bokeh.io import output_file
from electronfactors import generic_shape_convert, parameterise, create_report
input_dict = dict()
applicator = 10
energy = 12
ssd = 100
# In[3]:
key = '3cm circle'
input_dict[key] = dict()
input_dict[key]['f... | agpl-3.0 |
harisbal/pandas | pandas/tests/indexing/interval/test_interval.py | 4 | 7948 | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Interval, IntervalIndex, Series
import pandas.util.testing as tm
class TestIntervalIndex(object):
def setup_method(self, method):
self.s = Series(np.arange(5), IntervalIndex.from_breaks(np.arange(6)))
# To be removed... | bsd-3-clause |
alexmojaki/blaze | blaze/compute/tests/test_pytables_compute.py | 14 | 7657 | from __future__ import absolute_import, division, print_function
import os
import pytest
import pandas as pd
tb = pytest.importorskip('tables')
try:
f = pd.HDFStore('foo')
except (RuntimeError, ImportError) as e:
pytest.skip('skipping test_hdfstore.py %s' % e)
else:
f.close()
os.remove('foo')
from ... | bsd-3-clause |
WMD-group/MacroDensity | examples/PlanarAverage.py | 1 | 1084 | #! /usr/bin/env python
import macrodensity as md
import math
import numpy as np
import matplotlib.pyplot as plt
input_file = 'LOCPOT'
lattice_vector = 4.75
output_file = 'planar.dat'
# No need to alter anything after here
#------------------------------------------------------------------
# Get the potential
# This se... | mit |
mikeireland/chronostar | projects/scocen/galaxy_black.py | 1 | 9585 | """
Plot ScoCen in (l, b) coordinates, stars for each component with different
colours.
"""
import numpy as np
from astropy.table import Table
from astropy import units as u
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
plt.ion()
# Pretty plots
from fig_settings import *
########################... | mit |
aewhatley/scikit-learn | sklearn/datasets/lfw.py | 50 | 19048 | """Loader for the Labeled Faces in the Wild (LFW) dataset
This dataset is a collection of JPEG pictures of famous people collected
over the internet, all details are available on the official website:
http://vis-www.cs.umass.edu/lfw/
Each picture is centered on a single face. The typical task is called
Face Veri... | bsd-3-clause |
mohazahran/Detecting-anomalies-in-user-trajectories | scripts/bipartite-it.py | 2 | 1698 | #-*- coding: utf8
from __future__ import division, print_function
from statsmodels.distributions.empirical_distribution import ECDF
import matplotlib
#matplotlib.use('Agg')
import sys
import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def main():
parser = argparse.ArgumentPar... | bsd-3-clause |
serendio-labs-stage/diskoveror-datapreprocessing-python | premodelling routines/deviation/deviate.py | 3 | 1227 | '''
Copyright 2015 Serendio Inc.
Author - kshitij soni
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 wri... | apache-2.0 |
nvoron23/scikit-learn | sklearn/ensemble/gradient_boosting.py | 50 | 67625 | """Gradient Boosted Regression Trees
This module contains methods for fitting gradient boosted regression trees for
both classification and regression.
The module structure is the following:
- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regressio... | bsd-3-clause |
yuanchao/pyHiggsML | sklearn/sk_atlas.py | 1 | 2867 | '''
Example code for processing Atlas Machine-Learning Challenge
Based on Darin Baumgartel's reference code.
2014/06/23 Yuan CHAO
'''
print(__doc__)
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier as GBC
from sklearn.cross_validation import train_test_split
import math
# Load training... | gpl-2.0 |
plissonf/scikit-learn | examples/svm/plot_rbf_parameters.py | 132 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radial Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
childresslab/MicrocavityExp1 | logic/jupyterkernel/mpl/backend_inline.py | 7 | 7669 | """A matplotlib backend for publishing figures via display_data
Qudi 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.
Qudi is distributed in... | gpl-3.0 |
massmutual/scikit-learn | sklearn/semi_supervised/tests/test_label_propagation.py | 307 | 1974 | """ test the label propagation module """
import nose
import numpy as np
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {'kernel': 'rbf'}),
(label_propa... | bsd-3-clause |
joernhees/scikit-learn | sklearn/metrics/cluster/__init__.py | 91 | 1468 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
MatthewDaggitt/PathVision | modules/pathDisplayModule.py | 1 | 3864 | import tkinter
from itertools import groupby
from collections import defaultdict
import matplotlib.pyplot as plt
from matplotlib.colors import to_hex
import networkx as nx
import settings
from modules.shared.graphFrame import GraphFrame
from modules.shared.graphInteraction import DrawData
################
## Control... | mit |
hsiaoyi0504/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 |
JustusSchwan/MasterThesis | trash/cluster_features.py | 1 | 6740 | import csv
import os
import shutil
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn.cluster import KMeans
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.neighbors import NearestNeighbors
from scipy.spatial import Conve... | mit |
poryfly/scikit-learn | examples/semi_supervised/plot_label_propagation_structure.py | 247 | 2432 | """
==============================================
Label Propagation learning a complex structure
==============================================
Example of LabelPropagation learning a complex internal structure
to demonstrate "manifold learning". The outer circle should be
labeled "red" and the inner circle "blue". Be... | bsd-3-clause |
bmcfee/librosa | docs/examples/plot_pcen_stream.py | 2 | 3939 | # coding: utf-8
# Code source: Brian McFee
# License: ISC
"""
==============
PCEN Streaming
==============
This notebook demonstrates how to use streaming IO with `librosa.pcen`
to do dynamic per-channel energy normalization on a spectrogram incrementally.
This is useful when processing long audio files that are too ... | isc |
chenyyx/scikit-learn-doc-zh | doc/zh/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 104 | 3139 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | gpl-3.0 |
Messaoud-Boudjada/dipy | doc/sphinxext/docscrape_sphinx.py | 154 | 7759 | import re, inspect, textwrap, pydoc
import sphinx
from docscrape import NumpyDocString, FunctionDoc, ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config={}):
self.use_plots = config.get('use_plots', False)
NumpyDocString.__init__(self, docstring, config=config)
... | bsd-3-clause |
fabioticconi/scikit-learn | sklearn/tests/test_calibration.py | 62 | 12288 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
... | bsd-3-clause |
kmclaugh/fastai_courses | deeplearning2/utils2.py | 7 | 4587 | import math, keras, datetime, pandas as pd, numpy as np, keras.backend as K, threading, json, re, collections
import tarfile, tensorflow as tf, matplotlib.pyplot as plt, xgboost, operator, random, pickle, glob, os, bcolz
import shutil, sklearn, functools, itertools, scipy
from PIL import Image
from concurrent.futures i... | apache-2.0 |
CPFL/gdev | compiler/as/ptx2sass/hydrazine/python/Plot.py | 3 | 8644 | ################################################################################
## \file RunRegression.py
## \author Gregory Diamos
## \date July 19, 2008
## \brief A class and script for parsing a list of data elements and
## identifiers, and plotting them using Matplotlib
###########################################... | mit |
bondenitrr2015/Machine-Learning | titanic_quick_1.py | 1 | 3407 | # This is the titanic data-set problem
# Current Architecture is 3-5-3-1
import pandas as pd
import tensorflow as tf
# Step 1 - Load and parse the data
# Importing of data
titanic_train = pd.read_csv('/Users/path/to/train.csv')
titanic_test = pd.read_csv('/Users/path/to/test.csv')
titanic_test_result = pd.read_csv('/... | mit |
colinbrislawn/scikit-bio | skbio/stats/distance/_bioenv.py | 12 | 9577 | # ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | bsd-3-clause |
pprett/scikit-learn | examples/linear_model/plot_sgd_weighted_samples.py | 344 | 1458 | """
=====================
SGD: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
# we create 20 points
np.random.seed(0)
X ... | bsd-3-clause |
mahak/spark | python/pyspark/pandas/tests/data_type_ops/test_null_ops.py | 7 | 5691 | #
# 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 |
Kirubaharan/hydrology | stats/distribution_check.py | 1 | 11192 | #!/usr/bin/env python
#title :distribution_checkX.py
#description :Checks a sample against 80 distributions by applying the Kolmogorov-Smirnov test.
#author :Andre Dietrich
#email :dietrich@ivs.cs.uni-magdeburg.de
#date :07.10.2014
#version :0.1
#usage :pyth... | gpl-3.0 |
wzbozon/scikit-learn | examples/model_selection/plot_precision_recall.py | 249 | 6150 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both ... | bsd-3-clause |
ScreamingUdder/mantid | scripts/HFIR_4Circle_Reduction/mplgraphicsview.py | 3 | 54538 | #pylint: disable=invalid-name,too-many-public-methods,too-many-arguments,non-parent-init-called,R0902,too-many-branches,C0302
from __future__ import (absolute_import, division, print_function)
from six.moves import range
import os
import numpy as np
from PyQt4 import QtGui
from PyQt4.QtCore import pyqtSignal
from mat... | gpl-3.0 |
econ-ark/HARK | examples/Gentle-Intro/Gentle-Intro-To-HARK.py | 1 | 20111 | # ---
# jupyter:
# jupytext:
# cell_metadata_filter: collapsed,code_folding
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.2'
# jupytext_version: 1.2.4
# kernelspec:
# display_name: econ-ark-3.8
# language: ... | apache-2.0 |
stevenzhang18/Indeed-Flask | lib/pandas/io/tests/test_json/test_pandas.py | 9 | 34431 | # pylint: disable-msg=W0612,E1101
from pandas.compat import range, lrange, StringIO, OrderedDict
import os
import numpy as np
from pandas import (Series, DataFrame, DatetimeIndex, Timestamp, CategoricalIndex,
read_json, compat)
from datetime import timedelta
import pandas as pd
from pandas.util.te... | apache-2.0 |
bendudson/freegs | freegs/shaped_coil.py | 1 | 7126 | """
Define a class of coil which contains a uniform current density
over a shaped region.
License
-------
Copyright 2019 Ben Dudson, University of York. Email: benjamin.dudson@york.ac.uk
This file is part of FreeGS.
FreeGS is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser ... | lgpl-3.0 |
rexshihaoren/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 |
WendyLiuLab/elisascripts | elisa/id_singlets.py | 2 | 7486 | import argparse
import numpy as np
import pandas as pd
import scipy.ndimage as img
import scipy.spatial as spatial
from tifffile import tifffile as tf
from elisa.annotate import surface_from_array, annotate_cells, PIL_from_surface
from typing import Callable, Union # noqa:F401
from typing.io import BinaryIO # noqa... | bsd-3-clause |
vybstat/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
nicproulx/mne-python | tutorials/plot_artifacts_correction_ica.py | 3 | 11598 | """
.. _tut_artifacts_correct_ica:
Artifact Correction with ICA
============================
ICA finds directions in the feature space
corresponding to projections with high non-Gaussianity. We thus obtain
a decomposition into independent components, and the artifact's contribution
is localized in only a small numbe... | bsd-3-clause |
cvdlab/lar-running-demo | py/computation/pngstack2array3d.py | 3 | 4009 | """
To import a stack of PNG images into a 3D array, with denoising and color quatization.
Return a scipy ndarray.
"""
import sys
import numpy as np
from numpy import reshape,array
from scipy.cluster.vq import kmeans,vq
import png
import matplotlib.pyplot as plt
from scipy import ndimage
import struct
import os
# Def... | mit |
themrmax/scikit-learn | examples/linear_model/plot_lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
RPGOne/Skynet | scikit-learn-c604ac39ad0e5b066d964df3e8f31ba7ebda1e0e/sklearn/utils/setup.py | 296 | 2884 | import os
from os.path import join
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
import numpy
from numpy.distutils.misc_util import Configuration
config = Configuration('utils', parent_package, top_path)
config.add_subpackage('sparsetools')
... | bsd-3-clause |
dsm054/pandas | pandas/io/formats/excel.py | 1 | 24633 | """Utilities for conversion to writer-agnostic Excel representation
"""
import itertools
import re
import warnings
import numpy as np
from pandas.compat import reduce
from pandas.core.dtypes import missing
from pandas.core.dtypes.common import is_float, is_scalar
from pandas.core.dtypes.generic import ABCMultiIndex... | bsd-3-clause |
toros-astro/ProperImage | drafts/test_propersubtract.py | 1 | 3915 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# test_propersubtract.py
#
# Copyright 2016 Bruno S <bruno@oac.unc.edu.ar>
#
# This file is part of ProperImage (https://github.com/toros-astro/ProperImage)
# License: BSD-3-Clause
# Full Text: https://github.com/toros-astro/ProperImage/blob/master/LICENSE.txt
#
import ... | bsd-3-clause |
arabenjamin/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more impor... | bsd-3-clause |
kkk669/mxnet | example/rcnn/rcnn/core/tester.py | 25 | 10193 | # 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 |
bioinfo-core-BGU/neatseq-flow_modules | neatseq_flow_modules/Liron/Snippy_module/MLST_parser.py | 3 | 8400 | import os, re
import argparse
import pandas as pd
parser = argparse.ArgumentParser(description='Pars MLST')
parser.add_argument('-M', type=str,
help='MetaData file')
parser.add_argument('-F', type=str,
help='Merged MLST typing file')
parser.add_argument('-O' , type=str, defaul... | gpl-3.0 |
roim/PyTranscribe | plotting/hps.py | 1 | 3155 | # Copyright 2015 Rodrigo Roim Ferreira
#
# 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 version.
#
# This program is distributed... | gpl-3.0 |
carrillo/scikit-learn | examples/covariance/plot_outlier_detection.py | 235 | 3891 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates two
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assumin... | bsd-3-clause |
Reagankm/KnockKnock | venv/lib/python3.4/site-packages/matplotlib/tests/test_axes.py | 9 | 108913 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange
from nose.tools import assert_equal, assert_raises, assert_false, assert_true
import datetime
import numpy as np
from numpy import ma
import matplotlib
from matplotlib... | gpl-2.0 |
toastedcornflakes/scikit-learn | sklearn/metrics/pairwise.py | 4 | 46488 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Philippe Gervais <philippe.gervais@inria.fr>
# Lars Buitinck
... | bsd-3-clause |
manderelee/csc2521_final | scripts/tsne_3viz.py | 1 | 1227 | import matplotlib.pyplot as plt
import sys
from mpl_toolkits.mplot3d import Axes3D
from utils import *
from sklearn.manifold import TSNE
if __name__ == "__main__":
if len(sys.argv) != 3:
print ("python pca_viz.py /path/to/pos/ex /path/to/neg/ex")
else:
# Truncate data to equalize number of exam... | mpl-2.0 |
vshtanko/scikit-learn | examples/svm/plot_svm_scale_c.py | 223 | 5375 | """
==============================================
Scaling the regularization parameter for SVCs
==============================================
The following example illustrates the effect of scaling the
regularization parameter when using :ref:`svm` for
:ref:`classification <svm_classification>`.
For SVC classificati... | bsd-3-clause |
GbalsaC/bitnamiP | venv/lib/python2.7/site-packages/sympy/physics/quantum/state.py | 4 | 14797 | """Dirac notation for states."""
from sympy import Expr
from sympy.printing.pretty.stringpict import prettyForm
from sympy.physics.quantum.qexpr import (
QExpr, dispatch_method
)
__all__ = [
'KetBase',
'BraBase',
'StateBase',
'State',
'Ket',
'Bra',
'TimeDepState',
'TimeDepBra',
... | agpl-3.0 |
chianwei123/visualizer | context_switch.py | 1 | 2487 | #!/usr/bin/env python
import matplotlib.pyplot as plt
log = open('log', 'r')
lines = log.readlines()
# prepare for plotting
fig, ax = plt.subplots()
bar = 5
label = []
label_axes = []
context_switch = []
tasks = {}
for line in lines:
line = line.strip()
inst, args = line.split(' ', 1)
if inst == 'task':
id,... | bsd-2-clause |
awni/tensorflow | tensorflow/contrib/skflow/python/skflow/tests/test_base.py | 1 | 5208 | # Copyright 2015-present The Scikit Flow 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 require... | apache-2.0 |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/spyderlib/widgets/externalshell/start_ipython_kernel.py | 2 | 6504 | # -*- coding: utf-8 -*-
#
# Copyright © 2009-2012 Pierre Raybaut
# Licensed under the terms of the MIT License
# (see spyderlib/__init__.py for details)
"""Startup file used by ExternalPythonShell exclusively for IPython kernels
(see spyderlib/widgets/externalshell/pythonshell.py)"""
import sys
import os.path as osp
... | gpl-3.0 |
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