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 |
|---|---|---|---|---|---|
vtesin/sklearn_tutorial | examples/svm_gui.py | 8 | 11157 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
ThomasChauve/aita | AITAToolbox/.ipynb_checkpoints/aita-checkpoint.py | 2 | 77555 | # -*- coding: utf-8 -*-
'''
Created on 3 juil. 2015
Toolbox for data obtained using G50 Automatique Ice Texture Analyser (AITA) provide by :
Russell-Head, D.S., Wilson, C., 2001. Automated fabric analyser system for quartz and ice. J. Glaciol. 24, 117–130
@author: Thomas Chauve
@contact: thomas.chauve@univ-grenoble-al... | gpl-3.0 |
DynaLite/DynaLite_1.0 | Sources/SpeechProcessing/pyAudioAnalysis/audioFeatureExtraction.py | 3 | 31678 | import sys
import time
import os
import glob
import numpy
import mlpy
import cPickle
import aifc
import math
from numpy import NaN, Inf, arange, isscalar, array
from scipy.fftpack import rfft
from scipy.fftpack import fft
from scipy.fftpack.realtransforms import dct
from scipy.signal import fftconvolve
from matplotlib.... | mit |
olafhauk/mne-python | mne/inverse_sparse/mxne_optim.py | 6 | 57847 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Daniel Strohmeier <daniel.strohmeier@gmail.com>
# Mathurin Massias <mathurin.massias@gmail.com>
# License: Simplified BSD
from math import sqrt
import numpy as np
from scipy import linalg
from .mxne_debiasing import compute_bias
from ..util... | bsd-3-clause |
rsignell-usgs/notebook | pyugrid/notebook_examples/pyugrid_cartopy_test.py | 1 | 3029 |
# coding: utf-8
# # Test out standardized ADCIRC, SELFE and FVCOM datasets with pyugrid, IRIS and Cartopy
# The datasets being accessed here are NetCDF files from ADCIRC, SELFE and FVCOM, with attributes added or modified virtually using NcML to meet the [UGRID conventions standard for unstructured grid models](htt... | mit |
Haddy1/ClusterMDS | lib/libMDS.py | 1 | 1353 | #!/usr/bin/python
from sklearn.decomposition import PCA
import imp
#Use Theano only if available
try:
imp.find_module('theano')
use_theano = True
except ImportError:
use_theano = False
#use SMACOF from libSMACOF_theano when Theano avalailable
#from numpy implemention from libSMACOF when not
if use_theano:... | gpl-3.0 |
zhmz90/first_step_with_julia_kaggle.jl | King/input.py | 1 | 1976 | import string
import pandas as pd
import numpy as np
from numpy.random import shuffle
#import skimage.io import imread
from scipy.misc import imread
import tensorflow as tf
tf.app.flags.DEFINE_boolean("debug", True, "for debug models")
tf.app.flags.DEFINE_boolean("use_fp16", False, "data type")
FLAGS = tf.app.flags.FL... | mit |
zorojean/scikit-learn | sklearn/ensemble/gradient_boosting.py | 126 | 65552 | """Gradient Boosted Regression Trees
This module contains methods for fitting gradient boosted regression trees for
both classification and regression.
The module structure is the following:
- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regressio... | bsd-3-clause |
fredhusser/scikit-learn | sklearn/preprocessing/data.py | 113 | 56747 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# License: BSD 3 clause
from itertools import chain, combina... | bsd-3-clause |
lenovor/scikit-learn | sklearn/decomposition/base.py | 313 | 5647 | """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 <d.engemann@fz-juelich.de>
# Kyle Kastner <kastnerkyle@gmail.com>
#
# Licen... | bsd-3-clause |
cdegroc/scikit-learn | examples/linear_model/plot_sgd_penalties.py | 7 | 1500 | """
==============
SGD: Penalties
==============
Plot the contours of the three penalties supported by
`sklearn.linear_model.stochastic_gradient`.
"""
from __future__ import division
print __doc__
import numpy as np
import pylab as pl
def l1(xs):
return np.array([np.sqrt((1 - np.sqrt(x ** 2.0)) ** 2.0) for x i... | bsd-3-clause |
ishanic/scikit-learn | sklearn/linear_model/__init__.py | 270 | 3096 | """
The :mod:`sklearn.linear_model` module implements generalized linear models. It
includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-le... | bsd-3-clause |
yonglehou/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 |
lidalei/DataMining | parameters_tunning/visualize_nn_train_process.py | 1 | 1386 | import json
import matplotlib.pylab as plt
import itertools
import seaborn
fig1, ax1 = plt.subplots(1, 1)
fig2, ax2 = plt.subplots(1, 1)
palette = itertools.cycle(seaborn.color_palette(n_colors = 10))
for hidden1 in [10, 50, 100, 150]:
with open('train_process_hidden1_' + str(hidden1) + '.json', 'r') as f:
... | mit |
rajul/mne-python | examples/inverse/plot_compute_mne_inverse_epochs_in_label.py | 19 | 4539 | """
==================================================
Compute MNE-dSPM inverse solution on single epochs
==================================================
Compute dSPM inverse solution on single trial epochs restricted
to a brain label.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# ... | bsd-3-clause |
lin-credible/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.spars... | bsd-3-clause |
RPGOne/Skynet | scikit-learn-0.18.1/sklearn/feature_selection/tests/test_base.py | 98 | 3681 | import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
from sklearn.utils import check_array
from sklearn.utils.testing import assert_raises, assert_equal
class StepSelector(Select... | bsd-3-clause |
tswast/google-cloud-python | automl/tests/unit/gapic/v1beta1/test_gcs_client_v1beta1.py | 2 | 6919 | # -*- coding: utf-8 -*-
#
# Copyright 2019 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | apache-2.0 |
rustyrazorblade/ironeagle | ironeagle/__init__.py | 1 | 1028 | from cassandra.concurrent import execute_concurrent_with_args
import pandas
def save_dataframe_to_cassandra(session, dataframe, table, types=None):
"""
:param session:
:type session: cassandra.cluster.Session
:param dataframe:
:type dataframe: pandas.DataFrame
:param table:
:return:
"... | bsd-2-clause |
harisbal/pandas | pandas/tests/indexes/test_frozen.py | 2 | 3493 | import warnings
import numpy as np
from pandas.compat import u
from pandas.core.indexes.frozen import FrozenList, FrozenNDArray
from pandas.tests.test_base import CheckImmutable, CheckStringMixin
from pandas.util import testing as tm
class TestFrozenList(CheckImmutable, CheckStringMixin):
mutable_methods = ('ext... | bsd-3-clause |
thientu/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 242 | 5885 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
Loisel/colorview2d | colorview2d/view.py | 1 | 27152 | # -*- coding: utf-8 -*-
"""
The view module hosts the View class, the central object of cv2d.
"""
import logging
import os
import sys
import six
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from matplotlib.widgets import Slider, Button
import yaml
from color... | bsd-2-clause |
ratnania/pigasus | python/plugin/adaptiveMesh.py | 1 | 6879 | # -*- coding: UTF-8 -*-
#! /usr/bin/python
from caid.cad_geometry import square
from caid.cad_geometry import circle
from caid.cad_geometry import quart_circle
from caid.cad_geometry import annulus
from matplotlib import pyplot as plt
import numpy as np
from time import time
import sys
import inspect
fi... | mit |
COMPSCI290-S2016/Group3_LaplacianMesh | LapGUI.py | 1 | 31160 | #Based off of http://wiki.wxpython.org/GLCanvas
#Lots of help from http://wiki.wxpython.org/Getting%20Started
import sys
sys.path.append("S3DGLPy")
from OpenGL.GL import *
from OpenGL.arrays import vbo
import wx
from wx import glcanvas
from Primitives3D import *
from PolyMesh import *
from LaplacianMesh import *
from ... | apache-2.0 |
FrederichRiver/neutrino | applications/venus/venus/stock_base.py | 1 | 10456 | #!/usr/bin/python3
import datetime
import numpy as np
import pandas as pd
import re
import requests
from lxml import etree
from dev_global.env import TIME_FMT
from polaris.mysql8 import (mysqlBase, mysqlHeader)
from jupiter.utils import trans
__version__ = '1.0.10'
class StockBase(object):
"""
param header... | bsd-3-clause |
billy-inn/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 256 | 2406 | """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 ... | bsd-3-clause |
ldirer/scikit-learn | sklearn/feature_extraction/text.py | 3 | 52600 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gmail.com>
#
# License: B... | bsd-3-clause |
chintak/face_detection | models.py | 1 | 15942 | import numpy as np
import os
import theano
import theano.tensor as T
import lasagne
from lasagne import layers
from lasagne.init import Orthogonal
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from nolearn.lasagne import BatchIterator
from lazy_batch_iterator import LazyBatchItera... | apache-2.0 |
davidbrandfonbrener/Project-Sisyphus | backend/visualizations.py | 1 | 1392 | #from backend.networks import Model
import matplotlib.pyplot as plt
import numpy as np
#from backend.simulation_tools import Simulator
# visualize network output on a trial, compared to desired output
def visualize_2_input_one_output_trial(model, sess, data):
preds = model.test(sess, data[0])[0]
length = data... | mit |
peraktong/Cannon-Experiment | 0218_plot_three_suspect_star.py | 1 | 56764 |
import numpy as np
from astropy.table import Table
from astropy.io import fits
import matplotlib.pyplot as plt
import matplotlib
import pickle
from TheCannon_2 import dataset,apogee
from TheCannon_2 import model
pkl_file = open('wl.pkl', 'rb')
wl = pickle.load(pkl_file)
pkl_file.close()
# load path
pkl_file = op... | mit |
adamgreenhall/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 |
sanjayankur31/nest-simulator | pynest/examples/tsodyks_depressing.py | 8 | 5619 | # -*- coding: utf-8 -*-
#
# tsodyks_depressing.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the Lice... | gpl-2.0 |
iancze/PSOAP | scripts/psoap_predict_ST3.py | 1 | 5233 | #!/usr/bin/env python
import argparse
parser = argparse.ArgumentParser(description="Measure statistics across multiple chains.")
parser.add_argument("--draws", type=int, default=0, help="In addition to plotting the mean GP, plot several draws of the GP to show the scatter in predicitions.")
args = parser.parse_args()... | mit |
Djabbz/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
johnyf/pyvectorized | pyvectorized/multidim_plot.py | 1 | 5949 | """
Common 2D and 3D plot, quiver, text functions
2013 (BSD-3) California Institute of Technology
"""
from __future__ import division
from warnings import warn
#import numpy as np
from matplotlib import pyplot as plt
from vectorized_meshgrid import vec2meshgrid
def dimension(ndarray):
"""dimension of ndarray
... | bsd-3-clause |
gfyoung/pandas | asv_bench/benchmarks/categoricals.py | 2 | 9788 | import string
import sys
import warnings
import numpy as np
import pandas as pd
from .pandas_vb_common import tm
try:
from pandas.api.types import union_categoricals
except ImportError:
try:
from pandas.types.concat import union_categoricals
except ImportError:
pass
class Constructor:
... | bsd-3-clause |
ddboline/kaggle_imdb_sentiment_model | train_word2vec_model.py | 1 | 2397 | #!/usr/bin/python
import os
import csv
import gzip
import multiprocessing
from collections import defaultdict
import pandas as pd
import numpy as np
import nltk
from gensim.models import Word2Vec
from sklearn.feature_extraction.text import CountVectorizer
from KaggleWord2VecUtility import review_to_wordlist, revie... | mit |
kylerbrown/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 |
puyokw/kaggle_digitRecognizer | Lasagne.py | 1 | 3906 | import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from lasagne.layers import DenseLayer
from lasagne.layers import InputLayer
from lasagne.layers import DropoutLayer
from lasagne.layers import *
from lasagne.nonlinearities import softm... | mit |
IssamLaradji/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
ryfeus/lambda-packs | Tensorflow_LightGBM_Scipy_nightly/source/scipy/interpolate/ndgriddata.py | 13 | 7473 | """
Convenience interface to N-D interpolation
.. versionadded:: 0.9
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from .interpnd import LinearNDInterpolator, NDInterpolatorBase, \
CloughTocher2DInterpolator, _ndim_coords_from_arrays
from scipy.spatial import cKDTree
_... | mit |
wbengine/SPMILM | egs/1-billion/run_trf_2.py | 1 | 6271 | import os
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.insert(0, os.getcwd() + '/../../tools/')
import wb
import trf
# revise this function to config the dataset used to train different model
def data(tskdir):
train = tskdir + 'data/train.txt'
valid = tskdir + 'data/valid.txt'
te... | apache-2.0 |
kevin-coder/tensorflow-fork | tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py | 25 | 7883 | # Copyright 2015 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 |
StagPython/StagPy | setup.py | 1 | 1411 | import os
from setuptools import setup
with open('README.rst') as rdm:
README = rdm.read()
DEPENDENCIES = [
'loam>=0.3.1',
'f90nml>=1.2',
'setuptools_scm>=4.1',
]
HEAVY = [
'numpy>=1.19',
'scipy>=1.5',
'pandas>=1.1',
'h5py>=3.0',
'matplotlib>=3.3',
]
ON_RTD = os.environ.get('READ... | apache-2.0 |
weissercn/MLTools | Dalitz_simplified/evaluation_of_optimised_classifiers/svm_sin/svm_Sin_evaluation_of_optimised_classifiers.py | 1 | 1573 | import numpy as np
import math
import sys
sys.path.insert(0,'../..')
import os
import classifier_eval_simplified
from sklearn import tree
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
for dim in range(2,11):
comp_file_list=[]
######################################################... | mit |
vshtanko/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
astrofrog/glue-3d-viewer | glue_vispy_viewers/volume/layer_artist.py | 2 | 8312 | from __future__ import absolute_import, division, print_function
import uuid
import weakref
from matplotlib.colors import ColorConverter
from glue.core.data import Subset, Data
from glue.core.exceptions import IncompatibleAttribute
from glue.utils import broadcast_to
from glue.core.fixed_resolution_buffer import ARR... | bsd-2-clause |
jskDr/jamespy_py3 | kkeras_util.py | 2 | 2816 | #from keras.models import Sequential
from keras.layers import Dense, Input
from keras.models import Model
from keras.regularizers import l1
import matplotlib.pyplot as plt
def plot_model_history( history):
"""
accuracy and loss are depicted.
"""
plt.plot(history.history['acc'])
#plt.plot(history.history['val_acc... | mit |
MicrosoftGenomics/PySnpTools | pysnptools/snpreader/snpreader.py | 1 | 40556 | import numpy as np
import subprocess, sys
import os.path
from itertools import *
import pandas as pd
import logging
import time
import pysnptools.util as pstutil
from pysnptools.pstreader import PstReader
import warnings
import pysnptools.standardizer as stdizer
try:
from builtins import range
except:
pass
#!!... | apache-2.0 |
phobson/statsmodels | statsmodels/emplike/descriptive.py | 6 | 39010 | """
Empirical likelihood inference on descriptive statistics
This module conducts hypothesis tests and constructs confidence
intervals for the mean, variance, skewness, kurtosis and correlation.
If matplotlib is installed, this module can also generate multivariate
confidence region plots as well as mean-variance con... | bsd-3-clause |
fredhusser/scikit-learn | examples/cluster/plot_color_quantization.py | 297 | 3443 | # -*- coding: utf-8 -*-
"""
==================================
Color Quantization using K-Means
==================================
Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace
(China), reducing the number of colors required to show the image from 96,615
unique colors to 64, while pre... | bsd-3-clause |
Barmaley-exe/scikit-learn | examples/svm/plot_rbf_parameters.py | 35 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radius 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 |
nisse3000/pymatgen | dev_scripts/chemenv/strategies/multi_weights_strategy_parameters.py | 14 | 15863 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import division, unicode_literals
"""
Script to visualize the model coordination environments
"""
__author__ = "David Waroquiers"
__copyright__ = "Copyright 2012, The Materials Project"
__vers... | mit |
drodarie/nest-simulator | topology/pynest/hl_api.py | 8 | 71159 | # -*- coding: utf-8 -*-
#
# hl_api.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (a... | gpl-2.0 |
timothydmorton/bokeh | bokeh/charts/builder/boxplot_builder.py | 41 | 11882 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the BoxPlot class which lets you build your BoxPlot plots just passing
the arguments to the Chart class and calling the proper functions.
It also add a new chained stacked method.
"""
#------------------... | bsd-3-clause |
ronalcc/zipline | tests/test_sources.py | 17 | 7041 | #
# 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 |
jorik041/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | 228 | 11221 | from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import os
import shutil
from tempfile import NamedTemporaryFile
from sklearn.externals.six import b
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert... | bsd-3-clause |
berkeley-stat159/project-zeta | code/tsa_s3.py | 3 | 11180 | from __future__ import print_function, division
import numpy as np
import numpy.linalg as npl
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import colors
from matplotlib import gridspec
import os
import re
import json
import nibabel as nib
from utils import subject_class as sc
from utils import outl... | bsd-3-clause |
bjornsturmberg/NumBAT | lit_examples/simo-lit_02-Laude-AIPAdv_2013-silicon.py | 1 | 3208 | """ Replicating the results of
Generation of phonons from electrostriction in
small-core optical waveguides
Laude et al.
http://dx.doi.org/10.1063/1.4801936
Replicating silicon example.
Note requirement for lots of modes and therefore lots of memory.
"""
import time
import datetime
import nu... | gpl-3.0 |
CforED/Machine-Learning | sklearn/linear_model/tests/test_omp.py | 272 | 7752 | # Author: Vlad Niculae
# Licence: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equa... | bsd-3-clause |
Morgan-Stanley/treadmill | lib/python/treadmill/cli/scheduler/__init__.py | 2 | 2655 | """Top level command for Treadmill reports.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import json
import click
import pandas as pd
import tabulate
from six.moves import urllib_parse
from treadmill import ... | apache-2.0 |
theislab/scanpy | scanpy/external/tl/_palantir.py | 1 | 9726 | """\
Run Diffusion maps using the adaptive anisotropic kernel
"""
from typing import Optional, List
import pandas as pd
from anndata import AnnData
from ... import logging as logg
def palantir(
adata: AnnData,
n_components: int = 10,
knn: int = 30,
alpha: float = 0,
use_adjacency_matrix: bool = ... | bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/lib/mpl_examples/mplot3d/mixed_subplots_demo.py | 12 | 1032 | """
Demonstrate the mixing of 2d and 3d subplots
"""
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
def f(t):
s1 = np.cos(2*np.pi*t)
e1 = np.exp(-t)
return np.multiply(s1,e1)
################
# First subplot
################
t1 = np.arange(0.0, 5.0, 0.1)
t2 = n... | gpl-2.0 |
kernc/scikit-learn | sklearn/metrics/ranking.py | 7 | 27694 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
joyeshmishra/spark-tk | regression-tests/sparktkregtests/testcases/graph/single_source_shortest_path_test.py | 9 | 9396 | # vim: set encoding=utf-8
# Copyright (c) 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | apache-2.0 |
huzq/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 78 | 2702 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
jmcarpenter2/swifter | swifter/parallel_accessor.py | 1 | 5445 | import numpy as np
import warnings
from .base import _SwifterBaseObject, ERRORS_TO_HANDLE, suppress_stdout_stderr_logging
class _SwifterParallelBaseObject(_SwifterBaseObject):
def set_dask_threshold(self, dask_threshold=1):
"""
Set the threshold (seconds) for maximum allowed estimated duration of ... | mit |
NunoEdgarGub1/scikit-learn | examples/cluster/plot_agglomerative_clustering.py | 343 | 2931 | """
Agglomerative clustering with and without structure
===================================================
This example shows the effect of imposing a connectivity graph to capture
local structure in the data. The graph is simply the graph of 20 nearest
neighbors.
Two consequences of imposing a connectivity can be s... | bsd-3-clause |
chapmanb/bcbio-nextgen | tests/integration/rnaseq/test_ericscript.py | 2 | 4719 | from copy import deepcopy
import functools
import os
import pytest
import pandas as pd
from bcbio.rnaseq import ericscript
from bcbio.pipeline import config_utils, run_info
from bcbio.log import setup_script_logging
def create_sample_config(data_dir, work_dir, disambiguate=False):
system_config, system_file = c... | mit |
manashmndl/scikit-learn | examples/ensemble/plot_ensemble_oob.py | 259 | 3265 | """
=============================
OOB Errors for Random Forests
=============================
The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where
each new tree is fit from a bootstrap sample of the training observations
:math:`z_i = (x_i, y_i)`. The *out-of-bag* (OOB) error is the average er... | bsd-3-clause |
louispotok/pandas | pandas/tests/test_multilevel.py | 1 | 107731 | # -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101,W0141
from warnings import catch_warnings
import datetime
import itertools
import pytest
import pytz
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notna, isna, Tim... | bsd-3-clause |
jbloomlab/phydms | setup.py | 1 | 3584 | """Setup script for ``phydms``.
Written by Jesse Bloom.
"""
import sys
import os
import re
import glob
try:
from setuptools import setup
from setuptools import Extension
except ImportError:
raise ImportError("You must install setuptools")
if not (sys.version_info[0] == 3 and sys.version_info[1] >= 5):
... | gpl-3.0 |
tmills/uda | scripts/eval_bootstrap.py | 1 | 9103 | #!/usr/bin/env python
import numpy as np
import numpy.random
import os
from os.path import join,exists,dirname
from sklearn import svm
import sys
from sklearn.datasets import load_svmlight_file, dump_svmlight_file
from sklearn.metrics import f1_score
from uda_common import evaluate_and_print_scores, align_test_X_train... | apache-2.0 |
tkaitchuck/nupic | external/linux64/lib/python2.6/site-packages/matplotlib/dates.py | 54 | 33991 | #!/usr/bin/env python
"""
Matplotlib provides sophisticated date plotting capabilities, standing
on the shoulders of python :mod:`datetime`, the add-on modules
:mod:`pytz` and :mod:`dateutils`. :class:`datetime` objects are
converted to floating point numbers which represent the number of days
since 0001-01-01 UTC. T... | gpl-3.0 |
themrmax/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
rosswhitfield/mantid | Framework/PythonInterface/test/python/mantid/plots/axesfunctions3DTest.py | 3 | 5493 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
impo... | gpl-3.0 |
Adai0808/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 |
goerz/mgplottools | mgplottools/mpl.py | 1 | 16768 | """
Support routines for matplotlib plotting.
The module also contains a standard palette of colors (`colors` module
dictionary) and line styles (`ls` module dictionary).
For the colors, it is recommended to set up the color cycle by hand in your
matplotlibrc file. Alternatively, you can call
>>> mgplottools.mpl... | gpl-3.0 |
glouppe/scikit-learn | sklearn/metrics/cluster/supervised.py | 22 | 30444 | """Utilities to evaluate the clustering performance of models
Functions named as *_score return a scalar value to maximize: the higher the
better.
"""
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Wei LI <kuantkid@gmail.com>
# Diego Molla <dmolla-aliod@gmail.com>
# License: BSD 3 clause
fr... | bsd-3-clause |
MartinSavc/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 243 | 7461 | """
============================================================
Sparse recovery: feature selection for sparse linear models
============================================================
Given a small number of observations, we want to recover which features
of X are relevant to explain y. For this :ref:`sparse linear ... | bsd-3-clause |
nikitasingh981/scikit-learn | sklearn/linear_model/sag.py | 30 | 12959 | """Solvers for Ridge and LogisticRegression using SAG algorithm"""
# Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import make_dataset
from .sag_fast import sag
from ..exceptions import ConvergenceWarning
from ..utils import check_arra... | bsd-3-clause |
duthchao/kaggle-galaxies | predict_augmented_npy_maxout2048_extradense_pysex.py | 7 | 9720 | """
Load an analysis file and redo the predictions on the validation set / test set,
this time with augmented data and averaging. Store them as numpy files.
"""
import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime... | bsd-3-clause |
scattering/ipeek | server/plot_dcs.py | 1 | 3478 | # -*- coding: utf-8 -*-
import h5py
import simplejson
import os
import numpy as np
#import matplotlib.pyplot as plt
from time import time
def Elam(lam):
"""
convert wavelength in angstroms to energy in meV
"""
return 81.81/lam**2
def Ek(k):
"""
convert wave-vector in inver angstroms to energy ... | unlicense |
mrgloom/h2o-3 | h2o-py/h2o/h2o.py | 1 | 75759 | import warnings
warnings.simplefilter('always', DeprecationWarning)
import os
import functools
import os.path
import re
import urllib
import urllib2
import json
import imp
import random
import tabulate
from connection import H2OConnection
from job import H2OJob
from expr import ExprNode
from frame import H2OFrame, _py_... | apache-2.0 |
themrmax/scikit-learn | examples/gaussian_process/plot_gpc.py | 103 | 3927 | """
====================================================================
Probabilistic predictions with Gaussian process classification (GPC)
====================================================================
This example illustrates the predicted probability of GPC for an RBF kernel
with different choices of the hy... | bsd-3-clause |
DANA-Laboratory/CoolProp | dev/scripts/fit_rational_functions.py | 3 | 9253 | from __future__ import division, print_function
import json
import matplotlib
matplotlib.use('TKAgg')
import matplotlib.pyplot as plt
import CoolProp.CoolProp as CP
import CoolProp
import numpy as np
import scipy.optimize
import xalglib
import os,sys
def fit_rational_polynomial(x, y, xfine, n, d):
def obj(x,... | mit |
hainm/scikit-learn | doc/sphinxext/gen_rst.py | 142 | 40026 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
i... | bsd-3-clause |
Small-Bodies-Node/pds4-python-examples | examples/birc_example_display.py | 2 | 10055 | """
Example PDS4 Array_2D_Image display for BOPPS/BIRC data
=======================================================
This document describes an example Python module that can read an
image from a PDS4 data product. The code will read the data based on
the label keywords, but does not otherwise validate the label. If ... | bsd-3-clause |
phobson/statsmodels | examples/python/robust_models_0.py | 33 | 2992 |
## Robust Linear Models
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
# ## Estimation
#
# Load data:
data = sm.datasets.stackloss.load()
data.exog = sm.add_constant(data.ex... | bsd-3-clause |
mbr0wn/gnuradio | gr-digital/examples/example_fll.py | 6 | 4947 | #!/usr/bin/env python
#
# Copyright 2011-2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
from gnuradio import gr, digital, filter
from gnuradio import blocks
from gnuradio import channels
from gnuradio import eng_notation
from gnuradio.eng_arg i... | gpl-3.0 |
ronojoy/BDA_py_demos | demos_ch6/demo6_2.py | 19 | 1366 | """Bayesian Data Analysis, 3rd ed
Chapter 6, demo 2
Posterior predictive checking
Binomial example - Testing sequential dependence example
"""
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
# edit default plot settings (colours from colorbrewer2.org)
plt.rc('font', size=14)
plt.r... | gpl-3.0 |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/sklearn/tests/test_calibration.py | 213 | 12219 | # 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,
... | mit |
asadziach/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py | 82 | 6157 | # 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 |
licode/scikit-beam | skbeam/testing/decorators.py | 7 | 4462 | ########################################################################
# Copyright (c) 2014, Brookhaven Science Associates, Brookhaven #
# National Laboratory. All rights reserved. #
# #
# Redistribution and use in ... | bsd-3-clause |
VU-Cog-Sci/PRF_experiment | exp_tools/Trial.py | 1 | 3101 | #!/usr/bin/env python
# encoding: utf-8
"""
Session.py
Created by Tomas HJ Knapen on 2009-11-26.
Copyright (c) 2009 TK. All rights reserved.
"""
import os, sys, datetime
import subprocess, logging
import pickle, datetime
import time as time_module
import scipy as sp
import numpy as np
# import matplotlib.pylab as p... | mit |
ucloud/uai-sdk | examples/mxnet/insightface/train/code/train_softmax_dist.py | 1 | 26097 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import random
import logging
import pickle
import numpy as np
from image_iter import FaceImageIter
from image_iter import FaceImageIterList
import mxnet as mx
from mxnet import ... | apache-2.0 |
hainm/statsmodels | tools/backport_pr.py | 30 | 5263 | #!/usr/bin/env python
"""
Backport pull requests to a particular branch.
Usage: backport_pr.py branch [PR]
e.g.:
python tools/backport_pr.py 0.13.1 123
to backport PR #123 onto branch 0.13.1
or
python tools/backport_pr.py 1.x
to see what PRs are marked for backport that have yet to be applied.
Copied fr... | bsd-3-clause |
mjudsp/Tsallis | examples/applications/topics_extraction_with_nmf_lda.py | 38 | 3869 | """
=======================================================================================
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
=======================================================================================
This is an example of applying Non-negative Matrix ... | bsd-3-clause |
imaculate/scikit-learn | sklearn/metrics/ranking.py | 17 | 27697 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
tarasane/h2o-3 | h2o-py/h2o/h2o.py | 1 | 69816 | import warnings
warnings.simplefilter('always', DeprecationWarning)
import os
import functools
import os.path
import re
import urllib
import urllib2
import imp
import tabulate
from connection import H2OConnection
from job import H2OJob
from expr import ExprNode
from frame import H2OFrame, _py_tmp_key
from model import ... | apache-2.0 |
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