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 |
|---|---|---|---|---|---|
ljchang/neurolearn | examples/01_DataOperations/plot_download.py | 3 | 5125 | """
Basic Data Operations
=====================
A simple example showing how to download a dataset from neurovault and perform
basic data operations. The bulk of the nltools toolbox is built around the
Brain_Data() class. This class represents imaging data as a vectorized
features by observations matrix. Each image... | mit |
glennq/scikit-learn | examples/gaussian_process/plot_gpr_noisy.py | 104 | 3778 | """
=============================================================
Gaussian process regression (GPR) with noise-level estimation
=============================================================
This example illustrates that GPR with a sum-kernel including a WhiteKernel can
estimate the noise level of data. An illustration... | bsd-3-clause |
scipy/scipy | scipy/stats/_binned_statistic.py | 12 | 30918 | import builtins
import numpy as np
from numpy.testing import suppress_warnings
from operator import index
from collections import namedtuple
__all__ = ['binned_statistic',
'binned_statistic_2d',
'binned_statistic_dd']
BinnedStatisticResult = namedtuple('BinnedStatisticResult',
... | bsd-3-clause |
jreback/pandas | pandas/tests/indexes/test_frozen.py | 8 | 3069 | import re
import pytest
from pandas.core.indexes.frozen import FrozenList
class TestFrozenList:
unicode_container = FrozenList(["\u05d0", "\u05d1", "c"])
def setup_method(self, _):
self.lst = [1, 2, 3, 4, 5]
self.container = FrozenList(self.lst)
def check_mutable_error(self, *args, **... | bsd-3-clause |
trungnt13/scikit-learn | examples/plot_multioutput_face_completion.py | 330 | 3019 | """
==============================================
Face completion with a multi-output estimators
==============================================
This example shows the use of multi-output estimator to complete images.
The goal is to predict the lower half of a face given its upper half.
The first column of images sho... | bsd-3-clause |
pratapvardhan/scikit-learn | examples/tree/unveil_tree_structure.py | 67 | 4824 | """
=========================================
Understanding the decision tree structure
=========================================
The decision tree structure can be analysed to gain further insight on the
relation between the features and the target to predict. In this example, we
show how to retrieve:
- the binary t... | bsd-3-clause |
otmaneJai/Zipline | zipline/utils/tradingcalendar.py | 9 | 11195 | #
# 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 |
Tobychev/tardis | tardis/simulation/base.py | 2 | 16854 | import os
import logging
import time
import itertools
import pandas as pd
import numpy as np
from astropy import units as u
from tardis.montecarlo.base import MontecarloRunner
from tardis.plasma.properties.base import Input
# Adding logging support
logger = logging.getLogger(__name__)
class Simulation(object):
... | bsd-3-clause |
voxlol/scikit-learn | examples/decomposition/plot_pca_3d.py | 354 | 2432 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to ch... | bsd-3-clause |
shangwuhencc/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 218 | 3893 | """
==============================================
Feature agglomeration vs. univariate selection
==============================================
This example compares 2 dimensionality reduction strategies:
- univariate feature selection with Anova
- feature agglomeration with Ward hierarchical clustering
Both metho... | bsd-3-clause |
eig-2017/the-magical-csv-merge-machine | merge_machine/exact_linker.py | 1 | 5694 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 19 19:57:59 2017
@author: leo
"""
import numpy as np
import pandas as pd
from dedupe_linker import pd_pre_process
def enrich_sirene(tab):
# TODO: this has nothing to do here
'''Splits column L6_DECLAREE to create L6_DECLAREE.code_commune ... | mit |
bsipocz/astroML | astroML/correlation.py | 2 | 10579 | """
Tools for computing two-point correlation functions.
"""
import numpy as np
from sklearn.neighbors import KDTree
from sklearn.utils import check_random_state
def uniform_sphere(RAlim, DEClim, size=1):
"""Draw a uniform sample on a sphere
Parameters
----------
RAlim : tuple
select Right ... | bsd-2-clause |
Laurae2/LightGBM | tests/python_package_test/test_consistency.py | 1 | 4056 | # coding: utf-8
# pylint: skip-file
import os
import unittest
import lightgbm as lgb
import numpy as np
from sklearn.datasets import load_svmlight_file
class FileLoader(object):
def __init__(self, directory, prefix, config_file='train.conf'):
directory = os.path.join(os.path.dirname(os.path.realpath(__f... | mit |
billy-inn/scikit-learn | sklearn/naive_bayes.py | 128 | 28358 | # -*- coding: utf-8 -*-
"""
The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
are supervised learning methods based on applying Bayes' theorem with strong
(naive) feature independence assumptions.
"""
# Author: Vincent Michel <vincent.michel@inria.fr>
# Minor fixes by Fabian Pedre... | bsd-3-clause |
ilyes14/scikit-learn | sklearn/utils/random.py | 234 | 10510 | # Author: Hamzeh Alsalhi <ha258@cornell.edu>
#
# License: BSD 3 clause
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ =... | bsd-3-clause |
grundgruen/zipline | tests/pipeline/test_frameload.py | 4 | 7620 | """
Tests for zipline.pipeline.loaders.frame.DataFrameLoader.
"""
from unittest import TestCase
from mock import patch
from numpy import arange, ones
from numpy.testing import assert_array_equal
from pandas import (
DataFrame,
DatetimeIndex,
Int64Index,
)
from zipline.lib.adjustment import (
ADD,
... | apache-2.0 |
perryjohnson/biplaneblade | sandia_blade_lib/layer_plane_angles_stn08.py | 1 | 5257 | """Determine the layer plane angle of all the elements in a grid.
Author: Perry Roth-Johnson
Last modified: March 18, 2014
References:
http://stackoverflow.com/questions/3365171/calculating-the-angle-between-two-lines-without-having-to-calculate-the-slope/3366569#3366569
http://stackoverflow.com/questions/1929... | gpl-3.0 |
andybrnr/QuantEcon.py | examples/illustrates_clt.py | 7 | 1257 | """
Filename: illustrates_clt.py
Authors: John Stachurski and Thomas J. Sargent
Visual illustration of the central limit theorem. Histograms draws of
Y_n := \sqrt{n} (\bar X_n - \mu)
for a given distribution of X_i, and a given choice of n.
"""
import numpy as np
from scipy.stats import expon, norm
import matpl... | bsd-3-clause |
schlegelp/tanglegram | tanglegram/tangle.py | 1 | 28171 | # A Python package to plot tanglegrams
#
# Copyright (C) 2017 Philipp Schlegel
#
# 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 opt... | gpl-3.0 |
elijah513/scikit-learn | benchmarks/bench_random_projections.py | 397 | 8900 | """
===========================
Random projection benchmark
===========================
Benchmarks for random projections.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import collections
import numpy as np
import scipy.s... | bsd-3-clause |
omeed-maghzian/mtag | ldsc_mod/test/test_munge_sumstats.py | 3 | 12198 | from __future__ import division
import munge_sumstats as munge
import unittest
import numpy as np
import pandas as pd
import nose
from pandas.util.testing import assert_series_equal
from pandas.util.testing import assert_frame_equal
from numpy.testing import assert_array_equal, assert_array_almost_equal, assert_allclos... | gpl-3.0 |
LohithBlaze/scikit-learn | examples/applications/face_recognition.py | 191 | 5513 | """
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (2... | bsd-3-clause |
RobertABT/heightmap | build/matplotlib/examples/pylab_examples/pcolor_demo.py | 6 | 1430 | """
Demonstrates similarities between pcolor, pcolormesh, imshow and pcolorfast
for drawing quadrilateral grids.
"""
import matplotlib.pyplot as plt
import numpy as np
# make these smaller to increase the resolution
dx, dy = 0.15, 0.05
# generate 2 2d grids for the x & y bounds
y, x = np.mgrid[slice(-3, 3 + dy, dy),... | mit |
jakobkolb/MayaSim | Experiments/mayasim_X6_scan_drought_parameters.py | 1 | 7378 | """
Experiment to test the influence of drought events.
Drought events start once the civilisation has reached
a 'complex society' state and vary in length and severity.
Therefore, starting point is at t = 150 where the model has
reached a complex society state in all previous studies.
We also use parameters for incom... | gpl-3.0 |
ywcui1990/nupic.research | projects/sequence_prediction/continuous_sequence/run_tm_model.py | 3 | 16411 | ## ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This ... | agpl-3.0 |
ibadami/pytorch-semseg | ptsemseg/loader/camvid_loader.py | 2 | 3322 | import os
import collections
import torch
import torchvision
import numpy as np
import scipy.misc as m
import matplotlib.pyplot as plt
from torch.utils import data
class camvidLoader(data.Dataset):
def __init__(self, root, split="train", is_transform=False, img_size=None):
self.root = root
self.s... | mit |
toastedcornflakes/scikit-learn | doc/sphinxext/numpy_ext/docscrape_sphinx.py | 408 | 8061 | import re
import inspect
import textwrap
import pydoc
from .docscrape import NumpyDocString
from .docscrape import FunctionDoc
from .docscrape import ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots... | bsd-3-clause |
frodre/pyLIM | calib_test_scripts/verif_utils.py | 1 | 21049 | import os
import pandas as pd
import numpy as np
import dask.array as da
from multiprocessing import Pool
from itertools import product
import lim_utils as lutils
import plot_tools as ptools
import misc_utils as mutils
import data_utils as dutils
import pylim.Stats as ST
def get_scalar_outputs(dobj, nelem_in_yr, v... | mit |
TobiasLundby/UAST | Module5/exercise_imu/imu_exercise_4_2_1.py | 1 | 3266 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# IMU exercise
# Copyright (c) 2015-2017 Kjeld Jensen kjen@mmmi.sdu.dk kj@kjen.dk
##### Insert initialize code below ###################
## Uncomment the file to read ##
#fileName = 'nmea_data.txt'
#fileName = 'imu_razor_data_static.txt'
fileName = 'imu_razor_data_yaw_90deg.... | bsd-3-clause |
hothHowler/pymc3 | pymc3/examples/ARM12_6uranium.py | 14 | 1919 | import numpy as np
from pymc3 import *
import pandas as pd
data = pd.read_csv(get_data_file('pymc3.examples', 'data/srrs2.dat'))
cty_data = pd.read_csv(get_data_file('pymc3.examples', 'data/cty.dat'))
data = data[data.state == 'MN']
data['fips'] = data.stfips * 1000 + data.cntyfips
cty_data['fips'] = cty_data.stfip... | apache-2.0 |
ioam/holoviews | holoviews/plotting/mpl/sankey.py | 1 | 6343 | from __future__ import absolute_import, division, unicode_literals
import param
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
from ...core.util import basestring, max_range
from ...util.transform import dim
from .graphs import GraphPlot
from .util import filter_styles
... | bsd-3-clause |
aakashsinha19/Aspectus | Image Classification/models/autoencoder/AdditiveGaussianNoiseAutoencoderRunner.py | 10 | 1859 | import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from autoencoder.autoencoder_models.DenoisingAutoencoder import AdditiveGaussianNoiseAutoencoder
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
def standard_sca... | apache-2.0 |
pratapvardhan/scikit-image | doc/examples/segmentation/plot_threshold_adaptive.py | 5 | 1307 | """
=====================
Adaptive Thresholding
=====================
Thresholding is the simplest way to segment objects from a background. If that
background is relatively uniform, then you can use a global threshold value to
binarize the image by pixel-intensity. If there's large variation in the
background intensi... | bsd-3-clause |
SusanJL/iris | docs/iris/example_code/General/custom_file_loading.py | 6 | 12521 | """
Loading a cube from a custom file format
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This example shows how a custom text file can be loaded using the standard Iris
load mechanism.
The first stage in the process is to define an Iris :class:`FormatSpecification
<iris.io.format_picker.FormatSpecification>` for the fil... | gpl-3.0 |
blink1073/image_inspector | iminspector/linetool.py | 1 | 9446 | import numpy as np
import scipy.ndimage as ndi
from base import ToolHandles
from roi import ROIToolBase
__all__ = ['LineTool', 'ThickLineTool']
class LineTool(ROIToolBase):
"""Widget for line selection in a plot.
Parameters
----------
ax : :class:`matplotlib.axes.Axes`
Matp... | mit |
tkaitchuck/nupic | external/darwin64/lib/python2.6/site-packages/matplotlib/backends/backend_tkagg.py | 69 | 24593 | # Todd Miller jmiller@stsci.edu
from __future__ import division
import os, sys, math
import Tkinter as Tk, FileDialog
import tkagg # Paint image to Tk photo blitter extension
from backend_agg import FigureCanvasAgg
import os.path
import matplotlib
from matplotlib.cbook import is_string_like
from ... | gpl-3.0 |
dkoes/qsar-tools | applyclassifier.py | 1 | 1085 | #!/usr/bin/env python3
'''Apply a classification model trained with trainclassifier.py'''
import numpy as np
import pandas as pd
import argparse, sys, pickle
from sklearn.linear_model import *
from sklearn.metrics import *
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbor... | apache-2.0 |
jennolsen84/PyTables | c-blosc/bench/plot-speeds.py | 11 | 6852 | """Script for plotting the results of the 'suite' benchmark.
Invoke without parameters for usage hints.
:Author: Francesc Alted
:Date: 2010-06-01
"""
import matplotlib as mpl
from pylab import *
KB_ = 1024
MB_ = 1024*KB_
GB_ = 1024*MB_
NCHUNKS = 128 # keep in sync with bench.c
linewidth=2
#markers= ['+', ',', 'o... | bsd-3-clause |
xyguo/scikit-learn | sklearn/tree/tests/test_tree.py | 32 | 52369 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import platform
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import sparse_rand... | bsd-3-clause |
nikste/tensorflow | tensorflow/examples/learn/iris_val_based_early_stopping.py | 62 | 2827 | # 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 |
jakobworldpeace/scikit-learn | examples/model_selection/grid_search_text_feature_extraction.py | 99 | 4163 |
"""
==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================
The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the d... | bsd-3-clause |
dhruv13J/scikit-learn | examples/plot_kernel_ridge_regression.py | 230 | 6222 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
bennlich/scikit-image | skimage/transform/tests/test_radon_transform.py | 16 | 14464 | from __future__ import print_function, division
import numpy as np
from numpy.testing import assert_raises
import itertools
import os.path
from skimage.transform import radon, iradon, iradon_sart, rescale
from skimage.io import imread
from skimage import data_dir
from skimage._shared.testing import test_parallel
PH... | bsd-3-clause |
lesserwhirls/scipy-cwt | scipy/interpolate/tests/test_rbf.py | 3 | 3557 | #!/usr/bin/env python
# Created by John Travers, Robert Hetland, 2007
""" Test functions for rbf module """
import numpy as np
from numpy.testing import assert_, assert_array_almost_equal, assert_almost_equal
from numpy import linspace, sin, random, exp, allclose
from scipy.interpolate.rbf import Rbf
FUNCTIONS = ('mu... | bsd-3-clause |
Tong-Chen/scikit-learn | sklearn/utils/tests/test_utils.py | 12 | 4539 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal)
from sklearn.utils import check_random_state
from sklearn.utils import d... | bsd-3-clause |
NikNitro/Python-iBeacon-Scan | sympy/interactive/tests/test_ipythonprinting.py | 24 | 6208 | """Tests that the IPython printing module is properly loaded. """
from sympy.interactive.session import init_ipython_session
from sympy.external import import_module
from sympy.utilities.pytest import raises
# run_cell was added in IPython 0.11
ipython = import_module("IPython", min_module_version="0.11")
# disable ... | gpl-3.0 |
mattgiguere/scikit-learn | sklearn/utils/mocking.py | 38 | 1807 | from sklearn.base import BaseEstimator
from sklearn.utils.testing import assert_true
class ArraySlicingWrapper(object):
def __init__(self, array):
self.array = array
def __getitem__(self, aslice):
return MockDataFrame(self.array[aslice])
class MockDataFrame(object):
# have shape an len... | bsd-3-clause |
giorgiop/scikit-learn | examples/ensemble/plot_gradient_boosting_quantile.py | 392 | 2114 | """
=====================================================
Prediction Intervals for Gradient Boosting Regression
=====================================================
This example shows how quantile regression can be used
to create prediction intervals.
"""
import numpy as np
import matplotlib.pyplot as plt
from skle... | bsd-3-clause |
bflaven/BlogArticlesExamples | extending_streamlit_usage/010_streamlit_design/streamlit_design_5.py | 1 | 6097 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
[path]
cd /Users/brunoflaven/Documents/01_work/blog_articles/extending_streamlit_usage/streamlit_design/
[file]
streamlit run streamlit_design_5.py
# source
https://github.com/Jcharis/Streamlit_DataScience_Apps/blob/master/Streamlit_Python_Crash_Course/docs_app.py
... | mit |
krez13/scikit-learn | examples/cluster/plot_dbscan.py | 346 | 2479 | # -*- coding: utf-8 -*-
"""
===================================
Demo of DBSCAN clustering algorithm
===================================
Finds core samples of high density and expands clusters from them.
"""
print(__doc__)
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn... | bsd-3-clause |
jrderuiter/pybiomart | src/pybiomart/mart.py | 1 | 4194 | from __future__ import absolute_import, division, print_function
# pylint: disable=wildcard-import,redefined-builtin,unused-wildcard-import
from builtins import *
# pylint: enable=wildcard-import,redefined-builtin,unused-wildcard-import
from io import StringIO
import pandas as pd
# pylint: disable=import-error
from... | mit |
xzh86/scikit-learn | examples/applications/svm_gui.py | 287 | 11161 | """
==========
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 |
YudinYury/Python_Netology_homework | less_4_1_classwork_for_dig_data.py | 1 | 1630 | """lesson_4_1_Classwork "Data processing tools"
"""
import os
import pandas as pd
def count_of_len(row):
return len(row.Name)
def main():
source_path = 'D:\Python_my\Python_Netology_homework\data_names'
source_dir_path = os.path.normpath(os.path.abspath(source_path))
source_file = os.path.normpa... | gpl-3.0 |
Mitchkoens/sympy | sympy/external/tests/test_importtools.py | 91 | 1215 | from sympy.external import import_module
# fixes issue that arose in addressing issue 6533
def test_no_stdlib_collections():
'''
make sure we get the right collections when it is not part of a
larger list
'''
import collections
matplotlib = import_module('matplotlib',
__import__kwargs={... | bsd-3-clause |
AdamRTomkins/libSpineML2NK | libSpineML2NK/examples/Narx/Narx_Python/python/mean_contrast_filter.py | 1 | 1559 | import numpy as np
from matplotlib import pyplot as plt
import scipy
from scipy import io
def _1st_digital_linear_filter(x,
x_1,
y_1,
fs,
Tau,
Kss,
... | gpl-3.0 |
zihua/scikit-learn | sklearn/datasets/tests/test_20news.py | 280 | 3045 | """Test the 20news downloader, if the data is available."""
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import SkipTest
from sklearn import datasets
def test_20news():
try:
data = dat... | bsd-3-clause |
FederatedAI/FATE | examples/benchmark_quality/hetero_linear_regression/local-linr.py | 1 | 2555 | #
# Copyright 2019 The FATE 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 appli... | apache-2.0 |
hsiaoyi0504/scikit-learn | sklearn/cluster/tests/test_spectral.py | 262 | 7954 | """Testing for Spectral Clustering methods"""
from sklearn.externals.six.moves import cPickle
dumps, loads = cPickle.dumps, cPickle.loads
import numpy as np
from scipy import sparse
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
arahuja/scikit-learn | examples/decomposition/plot_image_denoising.py | 84 | 5820 | """
=========================================
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 |
hugobowne/scikit-learn | sklearn/utils/tests/test_multiclass.py | 34 | 13405 |
from __future__ import division
import numpy as np
import scipy.sparse as sp
from itertools import product
from sklearn.externals.six.moves import xrange
from sklearn.externals.six import iteritems
from scipy.sparse import issparse
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sp... | bsd-3-clause |
exa-analytics/atomic | exatomic/formula.py | 2 | 2582 | # -*- coding: utf-8 -*-
# Copyright (c) 2015-2020, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
Simple Formula
##################
"""
import numpy as np
import pandas as pd
from .core.error import StringFormulaError
from exatomic.base import isotopes, sym2mass
... | apache-2.0 |
AlexanderFabisch/scikit-learn | examples/linear_model/plot_ridge_path.py | 14 | 1599 | """
===========================================================
Plot Ridge coefficients as a function of the regularization
===========================================================
Shows the effect of collinearity in the coefficients of an estimator.
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regressi... | bsd-3-clause |
aetilley/scikit-learn | sklearn/linear_model/least_angle.py | 57 | 49338 | """
Least Angle Regression algorithm. See the documentation on the
Generalized Linear Model for a complete discussion.
"""
from __future__ import print_function
# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux
#
# License: BSD 3 ... | bsd-3-clause |
wackymaster/QTClock | Libraries/matplotlib/markers.py | 8 | 26608 | """
This module contains functions to handle markers. Used by both the
marker functionality of `~matplotlib.axes.Axes.plot` and
`~matplotlib.axes.Axes.scatter`.
All possible markers are defined here:
============================== ===============================================
marker descrip... | mit |
texta-tk/texta | task_manager/tasks/workers/entity_extractor_worker.py | 1 | 19137 | import os
import sys
import json
import logging
import numpy as np
import pickle as pkl
import psutil
from itertools import chain, product
from task_manager.models import Task
from searcher.models import Search
from utils.es_manager import ES_Manager
from utils.datasets import Datasets
from texta.settings import ERRO... | gpl-3.0 |
kgullikson88/HET-Scripts | FitPrimarySpectrum.py | 1 | 22695 | from scipy.interpolate import InterpolatedUnivariateSpline as interp
from scipy.optimize import leastsq, brute
from scipy import mat
from scipy.linalg import svd, diagsvd
import sys
import os
from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
import DataStructures
from astropy impor... | gpl-3.0 |
AlexanderFabisch/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 22 | 13165 | import numpy as np
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from scipy.special import psi
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
_dirichlet_expect... | bsd-3-clause |
RPGOne/scikit-learn | examples/ensemble/plot_random_forest_regression_multioutput.py | 46 | 2640 | """
============================================================
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... | bsd-3-clause |
IntelLabs/hpat | examples/series/series_corr.py | 1 | 1773 | # *****************************************************************************
# Copyright (c) 2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | bsd-2-clause |
wdbm/datavision | setup.py | 1 | 1970 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import setuptools
def main():
setuptools.setup(
name = "datavision",
version = "2018.01.08.2333",
description = "Python data visualisation",
long_description = long_description(),
url ... | gpl-3.0 |
Avsecz/concise | concise/layers.py | 1 | 28897 | import numpy as np
from keras import backend as K
from keras.engine.topology import Layer
from keras.layers.pooling import _GlobalPooling1D
from keras.layers import Conv1D, Input, LocallyConnected1D
from keras.layers.core import Dropout
from concise.utils.plot import seqlogo, seqlogo_fig
import matplotlib.pyplot as plt... | mit |
bhillmann/gingivere | tests/test_lr.py | 2 | 1117 | from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import StratifiedKFold
import numpy as np
from sklearn.metrics import classification_report
from sklearn.metrics import roc_auc_score
from tests import shelve_api
XX, yy = shelve_api.load('lr')
X = XX[2700:]
y = yy[2700:]
clf = LinearRe... | mit |
gnocchixyz/gnocchi | tools/duration_perf_analyse.py | 1 | 2586 | #!/usr/bin/env python
#
# Copyright (c) 2014 eNovance
#
# 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 a... | apache-2.0 |
cavestruz/L500analysis | plotting/profiles/T_Vcirc_evolution/Vcirc_evolution/plot_Vcirc2_Vc200m.py | 1 | 2692 | from L500analysis.data_io.get_cluster_data import GetClusterData
from L500analysis.utils.utils import aexp2redshift
from L500analysis.plotting.tools.figure_formatting import *
from L500analysis.plotting.profiles.tools.profiles_percentile \
import *
from L500analysis.utils.constants import rbins
from derived_field_f... | mit |
alonecoder1337/Dos-Attack-Detection-using-Machine-Learning | App.py | 1 | 1271 | from Classifier import Classififer
import pandas as pd
import numpy as np
from Dataset import Dataset
class App:
def __init__(self):
self.classifier = Classififer().get_classifier();
def train(self):
df = pd.read_csv('data/train.csv', header=None)
data = np.array(df)
self.x_t... | mit |
benjaminoh1/tensorflowcookbook | Chapter 03/logistic_regression.py | 1 | 3832 | # Logistic Regression
#----------------------------------
#
# This function shows how to use Tensorflow to
# solve logistic regression.
# y = sigmoid(Ax + b)
#
# We will use the low birth weight data, specifically:
# y = 0 or 1 = low birth weight
# x = demographic and medical history data
import matplotlib.pyplot as... | mit |
lucidfrontier45/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 2 | 2021 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
rsivapr/scikit-learn | sklearn/tests/test_lda.py | 22 | 1521 | import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from .. import lda
# Data is just 6 separable points in the plane
X = np.array([[-2, -1], [-... | bsd-3-clause |
weaver-viii/h2o-3 | h2o-py/tests/testdir_algos/glm/pyunit_link_functions_gammaGLM.py | 3 | 2022 | import sys
sys.path.insert(1, "../../../")
import h2o
import pandas as pd
import zipfile
import statsmodels.api as sm
def link_functions_gamma(ip,port):
print("Read in prostate data.")
h2o_data = h2o.import_file(path=h2o.locate("smalldata/prostate/prostate_complete.csv.zip"))
h2o_data.head()
sm_data = pd.rea... | apache-2.0 |
pp-mo/iris | docs/iris/src/userguide/plotting_examples/1d_with_legend.py | 2 | 1103 | import matplotlib.pyplot as plt
import iris
import iris.plot as iplt
fname = iris.sample_data_path("air_temp.pp")
# Load exactly one cube from the given file
temperature = iris.load_cube(fname)
# We are only interested in a small number of longitudes (the 4 after and
# including the 5th element), so index them out... | lgpl-3.0 |
samzhang111/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 26 | 8544 | import numpy as np
from scipy import linalg
from sklearn.decomposition import (NMF, ProjectedGradientNMF,
non_negative_factorization)
from sklearn.decomposition import nmf # For testing internals
from scipy.sparse import csc_matrix
from sklearn.utils.testing import assert_true
from... | bsd-3-clause |
jiangzhonglian/MachineLearning | src/py3.x/ml/7.AdaBoost/adaboost.py | 1 | 11037 | #!/usr/bin/python
# coding:utf8
"""
Created on Nov 28, 2010
Update on 2017-05-18
Adaboost is short for Adaptive Boosting
Author: Peter/片刻/BBruceyuan
GitHub: https://github.com/apachecn/AiLearning
"""
import numpy as np
def load_sim_data():
"""
测试数据，
:return: data_arr feature对应的数据集
label_arr... | gpl-3.0 |
lewisc/spark-tk | regression-tests/sparktkregtests/testcases/scoretests/random_forest_test.py | 10 | 3004 | # 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 |
breznak/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/axes.py | 69 | 259904 | from __future__ import division, generators
import math, sys, warnings, datetime, new
import numpy as np
from numpy import ma
import matplotlib
rcParams = matplotlib.rcParams
import matplotlib.artist as martist
import matplotlib.axis as maxis
import matplotlib.cbook as cbook
import matplotlib.collections as mcoll
im... | agpl-3.0 |
jm-begon/scikit-learn | sklearn/tests/test_grid_search.py | 68 | 28778 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import sys
import numpy as np
import scipy.sparse as sp
... | bsd-3-clause |
evgchz/scikit-learn | examples/covariance/plot_mahalanobis_distances.py | 348 | 6232 | r"""
================================================================
Robust covariance estimation and Mahalanobis distances relevance
================================================================
An example to show covariance estimation with the Mahalanobis
distances on Gaussian distributed data.
For Gaussian dis... | bsd-3-clause |
dsullivan7/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 261 | 2836 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from nose.tools import assert_equa... | bsd-3-clause |
SpectreJan/gnuradio | gr-filter/examples/decimate.py | 58 | 6061 | #!/usr/bin/env python
#
# Copyright 2009,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your ... | gpl-3.0 |
fyffyt/scikit-learn | examples/cluster/plot_kmeans_assumptions.py | 270 | 2040 | """
====================================
Demonstration of k-means assumptions
====================================
This example is meant to illustrate situations where k-means will produce
unintuitive and possibly unexpected clusters. In the first three plots, the
input data does not conform to some implicit assumptio... | bsd-3-clause |
kaichogami/scikit-learn | sklearn/model_selection/_search.py | 8 | 38827 | """
The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the
parameters of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.... | bsd-3-clause |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/core/indexes/category.py | 3 | 27066 | import numpy as np
from pandas._libs import index as libindex
from pandas import compat
from pandas.compat.numpy import function as nv
from pandas.core.dtypes.generic import ABCCategorical, ABCSeries
from pandas.core.dtypes.common import (
is_categorical_dtype,
_ensure_platform_int,
is_list_like,
is_in... | apache-2.0 |
pcmoritz/Strada.jl | deps/src/caffe/python/detect.py | 23 | 5743 | #!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results... | bsd-2-clause |
mhdella/scikit-learn | examples/linear_model/plot_sgd_separating_hyperplane.py | 260 | 1219 | """
=========================================
SGD: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.
"""
print(__doc__)
import numpy as n... | bsd-3-clause |
ephes/scikit-learn | sklearn/utils/tests/test_testing.py | 144 | 4121 | import warnings
import unittest
import sys
from nose.tools import assert_raises
from sklearn.utils.testing import (
_assert_less,
_assert_greater,
assert_less_equal,
assert_greater_equal,
assert_warns,
assert_no_warnings,
assert_equal,
set_random_state,
assert_raise_message)
from ... | bsd-3-clause |
Zsailer/epistasis | epistasis/pyplot/coefs.py | 2 | 13923 | import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
import matplotlib as mpl
import numpy as np
import gpmap
from scipy.stats import norm as scipy_norm
from epistasis.utils import Bunch
def plot_coefs(model=None, sites=None, values=None, errors=None, **kwargs):
""... | unlicense |
luo66/scikit-learn | examples/feature_selection/plot_feature_selection.py | 249 | 2827 | """
===============================
Univariate Feature Selection
===============================
An example showing univariate feature selection.
Noisy (non informative) features are added to the iris data and
univariate feature selection is applied. For each feature, we plot the
p-values for the univariate feature s... | bsd-3-clause |
gakarak/FCN_MSCOCO_Food_Segmentation | MSCOCO_Processing/PythonAPI/run11_FCN_COCO_Segmentation_Train_v1.py | 1 | 5077 | #!/usr/bin/python
# -*- coding: utf-8 -*-
__author__ = 'ar'
import os
import sys
import time
import numpy as np
import json
import skimage.io as skio
import skimage.transform as sktf
import skimage.color as skolor
import pandas as pd
import matplotlib.pyplot as plt
try:
import cPickle as pickle
except:
import ... | apache-2.0 |
HeraclesHX/scikit-learn | sklearn/utils/tests/test_testing.py | 144 | 4121 | import warnings
import unittest
import sys
from nose.tools import assert_raises
from sklearn.utils.testing import (
_assert_less,
_assert_greater,
assert_less_equal,
assert_greater_equal,
assert_warns,
assert_no_warnings,
assert_equal,
set_random_state,
assert_raise_message)
from ... | bsd-3-clause |
anurag313/scikit-learn | examples/feature_stacker.py | 246 | 1906 | """
=================================================
Concatenating multiple feature extraction methods
=================================================
In many real-world examples, there are many ways to extract features from a
dataset. Often it is beneficial to combine several methods to obtain good
performance. Th... | bsd-3-clause |
ContinuumIO/dask | dask/sizeof.py | 1 | 4406 | import random
import sys
from distutils.version import LooseVersion
from .utils import Dispatch
try: # PyPy does not support sys.getsizeof
sys.getsizeof(1)
getsizeof = sys.getsizeof
except (AttributeError, TypeError): # Monkey patch
def getsizeof(x):
return 100
sizeof = Dispatch(name="sizeof"... | bsd-3-clause |
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