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 |
|---|---|---|---|---|---|
deokwooj/DDEA | webgui/pack_cluster.py | 1 | 20586 | #!/usr/bin/python
# To force float point division
from __future__ import division
"""
Created on Thu Mar 13 16:34:54 2014
Author : Deokwoo Jung
E-mail : deokwoo.jung@gmail.com
"""
import numpy as np
#from numpy.linalg import norm
from sklearn import cluster
from sklearn.cluster import Ward
from sklearn.cluster import... | gpl-2.0 |
MatthewRueben/multiple-explorers | classes/environment.py | 1 | 13566 | #!/usr/bin/env python
from geography import Bounds2D, Location, POI
from rovers import Rover
from roverSettingsStruct import RoverSettings
import random
import itertools
from matplotlib import pyplot
import copy
class World():
def __init__(self, world_bounds, N_poi, poi_bounds, rover_settings, rover_start, rovHea... | mit |
Srisai85/scikit-learn | sklearn/utils/multiclass.py | 83 | 12343 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.... | bsd-3-clause |
xtonyjiang/GNOVA | calculate.py | 1 | 5915 | #!/usr/bin/python
from __future__ import division
import collections
from itertools import product
import numpy as np
import pandas as pd
from sklearn import linear_model
from scipy.stats import norm
from numpy.linalg import inv
def calculate(gwas_snps, ld_scores, annots, N1, N2):
np.seterr(invalid='ignore')
... | gpl-3.0 |
ECP-CANDLE/Benchmarks | Pilot1/P1B2/p1b2.py | 1 | 4456 | from __future__ import print_function
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
import os
import sys
import logging
import argparse
try:
import configparser
except ImportError:
import ConfigParser as configparser
file_path = os.path.dirname(os.path.realpath(__file__)... | mit |
Edu-Glez/Bank_sentiment_analysis | env/lib/python3.6/site-packages/pandas/tests/test_compat.py | 9 | 2455 | # -*- coding: utf-8 -*-
"""
Testing that functions from compat work as expected
"""
from pandas.compat import (range, zip, map, filter, lrange, lzip, lmap,
lfilter, builtins, iterkeys, itervalues, iteritems,
next)
import pandas.util.testing as tm
class TestBuilti... | apache-2.0 |
Nyker510/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 |
stiphyMT/plantcv | plantcv/plantcv/morphology/segment_tangent_angle.py | 2 | 5430 | # Find tangent angles in degrees of skeleton segments
import os
import cv2
import numpy as np
import pandas as pd
from plantcv.plantcv import params
from plantcv.plantcv import outputs
from plantcv.plantcv import plot_image
from plantcv.plantcv import print_image
from plantcv.plantcv import find_objects
from plantcv.p... | mit |
zhwa/thunder | thunder/rdds/fileio/tifffile.py | 9 | 173192 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# tifffile.py
# Copyright (c) 2008-2014, Christoph Gohlke
# Copyright (c) 2008-2014, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or wit... | apache-2.0 |
nathania/networkx | examples/multigraph/chess_masters.py | 54 | 5146 | #!/usr/bin/env python
"""
An example of the MultiDiGraph clas
The function chess_pgn_graph reads a collection of chess
matches stored in the specified PGN file
(PGN ="Portable Game Notation")
Here the (compressed) default file ---
chess_masters_WCC.pgn.bz2 ---
contains all 685 World Chess Championship matches
from... | bsd-3-clause |
murali-munna/scikit-learn | sklearn/decomposition/__init__.py | 147 | 1421 | """
The :mod:`sklearn.decomposition` module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.
"""
from .nmf import NMF, ProjectedGradientNMF
from .pca import PCA, RandomizedPCA
from .incrementa... | bsd-3-clause |
k8si/691CL_project | Baseline - files.py | 1 | 5438 | # -*- coding: utf-8 -*-
"""
Created on Sat May 03 16:40:23 2014
@author: Helene
"""
import glob, os, re, nltk, random, time, sklearn
import numpy as np
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.metrics.pairwise import linear_kernel
sto... | mit |
thareUSGS/GDAL_scripts | gdal_baseline_slope/python2/slope_histogram_cumulative_graph.py | 2 | 4103 | #!/usr/bin/env python
#/******************************************************************************
# * $Id$
# *
# * Project: GDAL Make Histogram and Cumulative graph from Tab delimited tab as
# generated by gdal_hist.py
# * Purpose: Take a gdal_hist.py output and create a histogram plot using matp... | unlicense |
sarunya-w/CS402-PROJECT | Project/feature_extraction/feature_extraction_parallel/fftengine.py | 1 | 3973 | # -*- coding: utf-8 -*-
"""
Created on Mon Apr 27 17:31:34 2015
@author: Sarunya
"""
import sys
import numpy as np
from PIL import Image
from matplotlib import pyplot as plt
import scipy.ndimage
#from scipy.ndimage import filters
#import time
sys.setrecursionlimit(10000)
bs = 200
wd = 8 # theta_range=wd*wd*2
clmax ... | mit |
oesteban/seaborn | doc/sphinxext/plot_directive.py | 38 | 27578 | """
A directive for including a matplotlib plot in a Sphinx document.
By default, in HTML output, `plot` will include a .png file with a
link to a high-res .png and .pdf. In LaTeX output, it will include a
.pdf.
The source code for the plot may be included in one of three ways:
1. **A path to a source file** as t... | bsd-3-clause |
wdurhamh/statsmodels | statsmodels/graphics/tests/test_boxplots.py | 28 | 1257 | import numpy as np
from numpy.testing import dec
from statsmodels.graphics.boxplots import violinplot, beanplot
from statsmodels.datasets import anes96
try:
import matplotlib.pyplot as plt
have_matplotlib = True
except:
have_matplotlib = False
@dec.skipif(not have_matplotlib)
def test_violinplot_beanpl... | bsd-3-clause |
iancze/PSOAP | scripts/make_fake_primary_data.py | 1 | 1680 | import matplotlib.pyplot as plt
import numpy as np
from scipy.linalg import cho_factor, cho_solve
from psoap import constants as C
from psoap.data import lkca14, redshift
from psoap import covariance
# Optimized parameters for this chunk. Not that relevant though, since we are just using the GP
# as an interpolator.... | mit |
wesm/statsmodels | scikits/statsmodels/sandbox/distributions/examples/ex_mvelliptical.py | 1 | 5134 | # -*- coding: utf-8 -*-
"""examples for multivariate normal and t distributions
Created on Fri Jun 03 16:00:26 2011
@author: josef
for comparison I used R mvtnorm version 0.9-96
"""
import numpy as np
import scikits.statsmodels.sandbox.distributions.mv_normal as mvd
from numpy.testing import assert_array_almost... | bsd-3-clause |
toobaz/pandas | pandas/tests/indexes/period/test_asfreq.py | 2 | 6254 | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, PeriodIndex, Series, period_range
from pandas.util import testing as tm
class TestPeriodIndex:
def test_asfreq(self):
pi1 = period_range(freq="A", start="1/1/2001", end="1/1/2001")
pi2 = period_range(freq="Q", star... | bsd-3-clause |
hsuantien/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 121 | 6117 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.base import ClassifierMixin
from skle... | bsd-3-clause |
giancarloescobar/project-kappa | code/utils/eda.py | 2 | 4040 | """ Script to run eda
"""
from convolve import *
from events2neural_fixed import *
from harmonic import *
from loading_data import *
import numpy as np
import numpy.linalg as npl
import matplotlib.pyplot as plt
import nibabel as nib
from dipy.segment.mask import median_otsu
from design_matrix import *
... | bsd-3-clause |
jarryliu/queue-sim | measure/plot/process_log.py | 1 | 21095 | #!/usr/local/bin/python3
# support only python3
import numpy as np
import matplotlib.pyplot as plt
import re
from math import sqrt, floor, ceil
from process_theory import *
import scipy as sp
import scipy.stats
import matplotlib.patches as mpatches
from matplotlib.colors import colorConverter as cc
# get the all the l... | mit |
pypot/scikit-learn | sklearn/ensemble/weight_boosting.py | 30 | 40648 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
tdhopper/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 |
galad-loth/DescHash | ProductQuant.py | 1 | 3595 | import numpy as npy
from LoadData import ReadFvecs,ReadIvecs
from sklearn.cluster import KMeans
from scipy.spatial.distance import pdist,squareform
from Utils import GetRetrivalMetric, GetKnnIdx
def PQTrain(data, lenSubVec,numSubCenter):
(dataSize, dataDim)=data.shape
if 0!=dataDim%lenSubVec:
... | apache-2.0 |
geektoni/Influenza-Like-Illness-Predictor | data_analysis/get_model_statistics.py | 1 | 2940 | # -*- coding: utf-8 -*-
"""Script which can be used to compare the features obtained of two different influenza models
Usage:
get_model_statistics.py <model> [--country=<country_name>] [--no-future] [--basedir=<directory>] [--start-year=<start_year>] [--end-year=<end_year>] [--save] [--no-graph]
<baseline> ... | mit |
jm-begon/scikit-learn | benchmarks/bench_plot_neighbors.py | 287 | 6433 | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import pylab as pl
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
return np.... | bsd-3-clause |
lucidfrontier45/scikit-learn | examples/plot_feature_selection.py | 2 | 2800 | """
===============================
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 |
alivecor/tensorflow | tensorflow/examples/learn/iris_custom_model.py | 37 | 3651 | # 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 |
toastedcornflakes/scikit-learn | examples/gaussian_process/plot_gpc_iris.py | 81 | 2231 | """
=====================================================
Gaussian process classification (GPC) on iris dataset
=====================================================
This example illustrates the predicted probability of GPC for an isotropic
and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
... | bsd-3-clause |
juhaj/topics-python-in-research | codes/python/solve_heat_equation_with_flow.py | 1 | 1751 | # Julius B. Kirkegaard 11/01/17
# jbk28@cam.ac.uk
import numpy as np
import matplotlib.pyplot as plt
def to_vector(mat):
return np.ravel(mat)
def to_matrix(vec):
return np.reshape(vec, shape)
### Define grid
dx = 0.02
x = np.arange(0, 1 + dx, dx)
m = len(x)
X, Y = np.meshgrid(x, x)
shape = X.shape
# Transfe... | gpl-3.0 |
ThomasMiconi/htmresearch | htmresearch/frameworks/capybara/supervised/plot.py | 7 | 3244 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2017, 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 progra... | agpl-3.0 |
elkingtonmcb/scikit-learn | sklearn/neighbors/graph.py | 208 | 7031 | """Nearest Neighbors graph functions"""
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
import warnings
from .base import KNeighborsMixin, RadiusNeighborsMixin
from .unsupervised import NearestNeighbors
def _check_params(X, metric, p, metric_... | bsd-3-clause |
AdaptiveApplications/carnegie | tarc_bus_locator_client/numpy-1.8.1/build/lib.linux-x86_64-2.7/numpy/lib/recfunctions.py | 17 | 35016 | """
Collection of utilities to manipulate structured arrays.
Most of these functions were initially implemented by John Hunter for matplotlib.
They have been rewritten and extended for convenience.
"""
from __future__ import division, absolute_import, print_function
import sys
import itertools
import numpy as np
imp... | mit |
ilyes14/scikit-learn | sklearn/decomposition/nmf.py | 35 | 39369 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Mathieu Blondel <mathieu@mblondel.org>
# Tom Dupre la Tour
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# ... | bsd-3-clause |
cvjena/libmaxdiv | experiments/hpw.py | 1 | 4781 | """ Runs the MDI algorithm on the Hurricane time-series. """
import sys
sys.path.append('..')
import numpy as np
import matplotlib.pylab as plt
import csv, datetime
from collections import OrderedDict
from maxdiv import maxdiv, preproc, baselines_noninterval, eval
HURRICANE_GT = { \
'Sandy' : (datetime.dat... | lgpl-3.0 |
henridwyer/scikit-learn | sklearn/decomposition/nmf.py | 15 | 19103 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# NMF implementation)
# Author: Anthony Di Franco (original Python and NumPy port)
# License: BSD 3 clause
from __future__ ... | bsd-3-clause |
mikeireland/pymfe | ghost_fit.py | 1 | 2187 | """A script to fit tramlines etc for Ghost data.
"""
from __future__ import division, print_function
import pymfe
import astropy.io.fits as pyfits
import numpy as np
import matplotlib.pyplot as plt
import pdb
import shutil
import matplotlib.cm as cm
#plt.ion()
#Define the files in use (NB xmod.txt and wavemod.txt ... | mit |
Juggerr/pykml | docs/sphinxext/matplotlib/ipython_directive.py | 7 | 15656 | import sys, os, shutil, imp, warnings, cStringIO, re
import IPython
from IPython.Shell import MatplotlibShell
try:
from hashlib import md5
except ImportError:
from md5 import md5
from docutils.parsers.rst import directives
import sphinx
sphinx_version = sphinx.__version__.split(".")
# The split is necessar... | bsd-3-clause |
mwalton/artificial-olfaction | evoVisualization/cloud.py | 1 | 1382 | import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import argparse
from sklearn.preprocessing import StandardScaler
from sklearn import decomposition
from sklearn import datasets
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argum... | mit |
poffuomo/spark | python/pyspark/sql/tests.py | 1 | 113287 | # -*- encoding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the ... | apache-2.0 |
batuhaniskr/Social-Network-Tracking-And-Analysis | analysis.py | 1 | 4391 | #!/usr/bin/env python3
# -*- encoding: utf-8 -*-
import getopt
import json
import os.path
import sqlite3
import sys
from collections import Counter
import matplotlib.pyplot as pl
import numpy as np
from flask import Flask, render_template
from geopy.geocoders import Nominatim
from termcolor import colored
import set... | mit |
HWNi/DATA515-Project | uberTaxi/script/split_data.py | 1 | 1590 | import pandas as pd
import os
def split_uber_data(file_name):
"""
A function split the data/time of Uber data set.
Parameter: Uber data file, month
Return: True or False for unittest purpose
True, the output file saved
False, the output file fail to saved
"""
df = pd.... | mit |
mayblue9/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
michigraber/scikit-learn | examples/neighbors/plot_nearest_centroid.py | 264 | 1804 | """
===============================
Nearest Centroid Classification
===============================
Sample usage of Nearest Centroid classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
f... | bsd-3-clause |
Nelca/buildMLSystem | ch06/03_clean.py | 6 | 5976 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
#
# This script tries to improve the classifier by cleaning the tweets a bit
#
import time
start_time ... | mit |
Ademan/NumPy-GSoC | numpy/lib/npyio.py | 4 | 56059 | __all__ = ['savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
'packbits', 'unpackbits', 'fromregex', 'DataSource']
import numpy as np
import format
import sys
import os
import sys
import itertools
import warnings
from operator imp... | bsd-3-clause |
lancezlin/ml_template_py | lib/python2.7/site-packages/pandas/core/groupby.py | 7 | 147947 | import types
from functools import wraps
import numpy as np
import datetime
import collections
import warnings
import copy
from pandas.compat import(
zip, range, long, lzip,
callable, map
)
from pandas import compat
from pandas.compat.numpy import function as nv
from pandas.compat.numpy import _np_version_unde... | mit |
qbilius/streams | streams/utils.py | 1 | 13227 | import functools
import numpy as np
import scipy.stats
import pandas
import matplotlib.pyplot as plt
import seaborn as sns
def splithalf(data, aggfunc=np.nanmean, rng=None):
data = np.array(data)
if rng is None:
rng = np.random.RandomState(None)
inds = list(range(data.shape[0]))
rng.shuffle(i... | gpl-3.0 |
DamCB/tyssue | tyssue/dynamics/planar_gradients.py | 2 | 1567 | import pandas as pd
from ..utils.utils import _to_2d
def area_grad(sheet):
coords = sheet.coords
inv_area = sheet.edge_df.eval("1 / (4 * sub_area)")
face_pos = sheet.edge_df[["f" + c for c in coords]]
srce_pos = sheet.edge_df[["s" + c for c in coords]]
trgt_pos = sheet.edge_df[["t" + c for c in... | gpl-3.0 |
jzt5132/scikit-learn | examples/cluster/plot_digits_linkage.py | 369 | 2959 | """
=============================================================================
Various Agglomerative Clustering on a 2D embedding of digits
=============================================================================
An illustration of various linkage option for agglomerative clustering on
a 2D embedding of the di... | bsd-3-clause |
ElDeveloper/scikit-learn | examples/neighbors/plot_classification.py | 287 | 1790 | """
================================
Nearest Neighbors Classification
================================
Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColorm... | bsd-3-clause |
pierreberthet/local-scripts | compare.py | 1 | 5832 | import numpy as np
import json
import matplotlib
#matplotlib.use('Agg')
import pylab as pl
import sys
import pprint as pp
import difflib
#from difflib_data import *
# Plot the different figures for the merged spikes and voltages recordings.
# This file, as the MergeSpikefiles.py should be one level up than Test/..., th... | gpl-2.0 |
js0701/chromium-crosswalk | chrome/test/data/nacl/gdb_rsp.py | 42 | 2542 | # Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
# This file is based on gdb_rsp.py file from NaCl repository.
import re
import socket
import time
def RspChecksum(data):
checksum = 0
for char in ... | bsd-3-clause |
kaichogami/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 286 | 4353 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation me... | bsd-3-clause |
vybstat/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | 176 | 2169 | from nose.tools import assert_equal
import numpy as np
from sklearn.preprocessing import FunctionTransformer
def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
def _func(X, *args, **kwargs):
args_store.append(X)
args_store.extend(args)
kwargs_store.update(kwargs)
... | bsd-3-clause |
walterreade/scikit-learn | examples/linear_model/plot_robust_fit.py | 147 | 3050 | """
Robust linear estimator fitting
===============================
Here a sine function is fit with a polynomial of order 3, for values
close to zero.
Robust fitting is demoed in different situations:
- No measurement errors, only modelling errors (fitting a sine with a
polynomial)
- Measurement errors in X
- M... | bsd-3-clause |
meduz/scikit-learn | doc/sphinxext/sphinx_gallery/gen_rst.py | 14 | 23291 | # -*- coding: utf-8 -*-
# Author: Óscar Nájera
# License: 3-clause BSD
"""
==================
RST file generator
==================
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
# Don't use unicode_literals here (be explici... | bsd-3-clause |
arabenjamin/scikit-learn | examples/semi_supervised/plot_label_propagation_digits.py | 268 | 2723 | """
===================================================
Label Propagation digits: Demonstrating performance
===================================================
This example demonstrates the power of semisupervised learning by
training a Label Spreading model to classify handwritten digits
with sets of very few labels.... | bsd-3-clause |
mikewolfli/IE_MBom | src/eds_pane.py | 1 | 60893 | # coding=utf-8
'''
Created on 2017年1月24日
@author: 10256603
'''
from global_list import *
global login_info
logger = logging.getLogger()
mat_heads = ['位置号', '物料号', '中文名称', '英文名称', '图号', '数量', '单位', '材料', '重量', '备注']
mat_keys = ['st_no', 'mat_no', 'mat_name_cn', 'mat_name_en', 'drawing_no',
... | unlicense |
lenovor/scikit-learn | sklearn/utils/fixes.py | 133 | 12882 | """Compatibility fixes for older version of python, numpy and scipy
If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# ... | bsd-3-clause |
paulgradie/SeqPyPlot | main_app/seqpyplot/plot/de_tally_plotter.py | 1 | 4840 | import os
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rcParams
import seaborn as sns
from seqpyplot.plot.base.plot_base import PlotBase
from tqdm import tqdm
from ..analyzer.paired_sample_filter import PairedSampleFilter
class TallyDe(PlotBase):
... | gpl-3.0 |
rvraghav93/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 35 | 7372 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
varia... | bsd-3-clause |
jreback/pandas | pandas/tests/frame/methods/test_sort_index.py | 1 | 29215 | import numpy as np
import pytest
import pandas as pd
from pandas import (
CategoricalDtype,
CategoricalIndex,
DataFrame,
Index,
IntervalIndex,
MultiIndex,
Series,
Timestamp,
)
import pandas._testing as tm
class TestDataFrameSortIndex:
def test_sort_index_and_reconstruction_doc_exa... | bsd-3-clause |
YinongLong/scikit-learn | examples/linear_model/plot_omp.py | 385 | 2263 | """
===========================
Orthogonal Matching Pursuit
===========================
Using orthogonal matching pursuit for recovering a sparse signal from a noisy
measurement encoded with a dictionary
"""
print(__doc__)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import OrthogonalM... | bsd-3-clause |
alekz112/statsmodels | statsmodels/stats/tests/test_weightstats.py | 30 | 21864 | '''tests for weightstats, compares with replication
no failures but needs cleanup
update 2012-09-09:
added test after fixing bug in covariance
TODOs:
- I don't remember what all the commented out code is doing
- should be refactored to use generator or inherited tests
- still gaps in test coverage... | bsd-3-clause |
pmav99/satellite-change-detect | tiledelta/scripts/cli.py | 2 | 2678 | #!/usr/bin/env python
import rasterio as rio
import numpy as np
import click, json, os
import tiledelta, mercantile
@click.group()
def cli():
pass
@click.command(short_help="HELP")
@click.argument('bounds', default='-', required=False)
@click.option('--stride', default=1)
def loaddata(bounds, stride):
"""Doe... | mit |
jougs/nest-simulator | pynest/examples/sinusoidal_poisson_generator.py | 7 | 5526 | # -*- coding: utf-8 -*-
#
# sinusoidal_poisson_generator.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 o... | gpl-2.0 |
r-mart/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 130 | 6059 | import numpy as np
from scipy import linalg
from sklearn.decomposition import nmf
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import raises
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_gr... | bsd-3-clause |
omad/damootils | scripts/gqa_report.py | 1 | 1420 | import io
import pandas as pd
'''select name, dataset.metadata ? 'gqa' as has_gqa, count(dataset.id)
from agdc.dataset
left join agdc.dataset_type on dataset_type.id = dataset.dataset_type_ref
where name like '%level1%'
group by name, has_gqa
order by name;'''
csv = io.StringIO('''
name,has_gqa,count
ls7_pq_scene,... | apache-2.0 |
horance-liu/tensorflow | tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py | 24 | 40534 | # 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 |
arabenjamin/scikit-learn | examples/applications/plot_out_of_core_classification.py | 255 | 13919 | """
======================================================
Out-of-core classification of text documents
======================================================
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. ... | bsd-3-clause |
AnasGhrab/scikit-learn | sklearn/manifold/tests/test_spectral_embedding.py | 216 | 8091 | from nose.tools import assert_true
from nose.tools import assert_equal
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_raises
from nose.plugins.skip import SkipTest
from sk... | bsd-3-clause |
xuyongzhi/scan_volume | src/rotate3D/scripts/rotate_to_3D.py | 1 | 7338 | #!/usr/bin/env python
import rospy
import rosbag
from sensor_msgs.msg import LaserScan
from sensor_msgs.msg import PointCloud2
import sensor_msgs.point_cloud2 as pc2
from sensor_msgs.msg import PointField
from std_msgs.msg import Int64
from laser_geometry import LaserProjection
import numpy as np
import matplotlib.pyp... | mit |
probml/pyprobml | scripts/Old/boss_motifs.py | 1 | 1696 |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import boss_utils
import utils
np.random.seed(0)
def motif_distance(x, m):
# hamming distance of x to motif
# If m[i]=nan, it means locn i is a don't care
mask = [not(np.isnan(v)) for v in m] #np.where(m>0)
return np.sum(x[mask] != m[mas... | mit |
hainm/scikit-learn | sklearn/svm/tests/test_bounds.py | 280 | 2541 | import nose
from nose.tools import assert_equal, assert_true
from sklearn.utils.testing import clean_warning_registry
import warnings
import numpy as np
from scipy import sparse as sp
from sklearn.svm.bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model.logistic import LogisticRegression... | bsd-3-clause |
tmerrick1/spack | var/spack/repos/builtin/packages/py-localcider/package.py | 5 | 1786 | ##############################################################################
# Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-64... | lgpl-2.1 |
spallavolu/scikit-learn | doc/conf.py | 210 | 8446 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# 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.
... | bsd-3-clause |
RPGOne/Skynet | scikit-learn-0.18.1/examples/classification/plot_classification_probability.py | 138 | 2871 | """
===============================
Plot classification probability
===============================
Plot the classification probability for different classifiers. We use a 3
class dataset, and we classify it with a Support Vector classifier, L1
and L2 penalized logistic regression with either a One-Vs-Rest or multinom... | bsd-3-clause |
whereaswhile/DLSR | convnet-folk_master/shownet_bbx.py | 1 | 22862 | # Copyright (c) 2011, Alex Krizhevsky (akrizhevsky@gmail.com)
# 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 source code must retain the above copyright notice,
# this ... | gpl-2.0 |
YihaoLu/statsmodels | statsmodels/examples/l1_demo/sklearn_compare.py | 33 | 3710 | """
For comparison with sklearn.linear_model.LogisticRegression
Computes a regularzation path with both packages. The coefficient values in
either path are related by a "constant" in the sense that for any fixed
value of the constraint C and log likelihood, there exists an l1
regularization constant alpha... | bsd-3-clause |
lucidfrontier45/scikit-learn | sklearn/utils/setup.py | 1 | 2475 | 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 |
3324fr/spinalcordtoolbox | dev/old/sct_straighten_spinalcord__testing.py | 1 | 144941 | #!/usr/bin/env python
## @package sct_straighten_spinalcord
#
# - run a gaussian weighted slice by slice registration over a machine to straighten the spinal cord.
# - use the centerline previously calculated during the registration but fitted with spline interpolation to improve the spinal cord straightening
# - find... | mit |
JasonKessler/scattertext | scattertext/termscoring/MannWhitneyU.py | 1 | 3706 | import pandas as pd
import numpy as np
from scipy.stats import norm, mannwhitneyu, ranksums
from scattertext.termscoring.CorpusBasedTermScorer import CorpusBasedTermScorer
class MannWhitneyU(CorpusBasedTermScorer):
'''
term_scorer = (MannWhitneyU(corpus).set_categories('Positive', ['Negative'], ['Plot']))
... | apache-2.0 |
mikkokemppainen/complex-networks | ERgraphs.py | 1 | 6428 | """Random graph tools
This module contains tools for numerical demonstrations of basic
properties of Erdos-Renyi random graphs.
We use NetworkX to generate Erdos-Renyi random graphs of n nodes
with fixed expected average degree K.
We generate a graph G using nx.gnp_random_graph(n,p) with wiring
probabili... | mit |
abhisg/scikit-learn | examples/linear_model/plot_ols.py | 220 | 1940 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
yonglehou/scikit-learn | sklearn/feature_selection/tests/test_chi2.py | 221 | 2398 | """
Tests for chi2, currently the only feature selection function designed
specifically to work with sparse matrices.
"""
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
import scipy.stats
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.feature_selection.univariate_selection im... | bsd-3-clause |
sly-ninja/python_for_ml | Module5/assignment7.py | 1 | 5807 | import pandas as pd
import numpy as np
# If you'd like to try this lab with PCA instead of Isomap,
# as the dimensionality reduction technique:
Test_PCA = True
def plotDecisionBoundary(model, X, y):
print("Plotting...")
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot') # Look P... | mit |
johnmgregoire/NanoCalorimetry | maxcovariancecompositiondirection.py | 1 | 5675 | import time, copy
import os
import sys
import numpy
import h5py
#from PnSC_ui import *
#from PnSC_dataimport import *
from PnSC_SCui import *
#from PnSC_math import *
from PnSC_h5io import *
from PnSC_main import *
from matplotlib.ticker import FuncFormatter
import scipy.integrate
p='C:/Users/JohnnyG/Documents/Python... | bsd-3-clause |
xubenben/scikit-learn | benchmarks/bench_sgd_regression.py | 283 | 5569 | """
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import pylab as pl
import gc
from time import time
from sklearn.linear_model i... | bsd-3-clause |
ankurankan/pgmpy | pgmpy/tests/test_models/test_BayesianNetwork.py | 2 | 45449 | import unittest
import networkx as nx
import pandas as pd
import numpy as np
import numpy.testing as np_test
from pgmpy.models import BayesianNetwork, MarkovNetwork
from pgmpy.base import DAG
import pgmpy.tests.help_functions as hf
from pgmpy.factors.discrete import (
TabularCPD,
JointProbabilityDistribution,... | mit |
camallen/aggregation | experimental/penguins/clusterAnalysis/penguins_at_5.py | 2 | 5268 | #!/usr/bin/env python
__author__ = 'greghines'
import numpy as np
import os
import pymongo
import sys
import urllib
import matplotlib.cbook as cbook
from PIL import Image
import matplotlib.pyplot as plt
import warnings
if os.path.exists("/home/ggdhines"):
sys.path.append("/home/ggdhines/PycharmProjects/reduction/e... | apache-2.0 |
cqychen/quants | quants/loaddata/skyeye_ods_classified_zz500s.py | 1 | 1324 | #coding=utf8
import tushare as ts;
import pymysql;
import time as dt
from datashape.coretypes import string
from pandas.io.sql import SQLDatabase
import sqlalchemy
import datetime
from sqlalchemy import create_engine
from pandas.io import sql
import threading
import pandas as pd;
import sys
sys.path.append('../') #添加配... | epl-1.0 |
madjelan/scikit-learn | sklearn/__init__.py | 154 | 3014 | """
Machine learning module for Python
==================================
sklearn is a Python module integrating classical machine
learning algorithms in the tightly-knit world of scientific Python
packages (numpy, scipy, matplotlib).
It aims to provide simple and efficient solutions to learning problems
that are acc... | bsd-3-clause |
xubenben/scikit-learn | sklearn/mixture/gmm.py | 128 | 31069 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
import warnings
import numpy as... | bsd-3-clause |
hirofumi0810/tensorflow_end2end_speech_recognition | utils/training/plot.py | 1 | 2592 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
blue = '#... | mit |
allrod5/extra-trees | benchmarks/regression/regression.py | 1 | 2437 |
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import explained_variance_score
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error
from sklearn.metrics import median_absolute_error
from sklearn.metric... | mit |
vitale232/ves | ves/ves_inverse.py | 1 | 8387 | import os
import sys
import random # not for prod
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
os.chdir('./templates') # not for prod
print(os.getcwd())
print(os.listdir())
from templates.tempData import coefficients
schlumbergerFilterCoefficients, wennerFilterCoeffic... | lgpl-3.0 |
jorgemauricio/INIFAP_Course | algoritmos/algoritmo_ith.py | 1 | 1932 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 17 16:17:25 2017
@author: jorgemauricio
"""
#%% import libraries
import pandas as pd
import os
#%% Clear terminal
os.system('clear')
#%% load data from csv file
print('***** leer archivo csv')
data = pd.read_csv('../data/data_course_aguascaliente... | mit |
bavardage/statsmodels | statsmodels/tsa/base/tsa_model.py | 3 | 10216 | import statsmodels.base.model as base
from statsmodels.base import data
import statsmodels.base.wrapper as wrap
from statsmodels.tsa.base import datetools
from numpy import arange, asarray
from pandas import Index
from pandas import datetools as pandas_datetools
import datetime
_freq_to_pandas = datetools._freq_to_pan... | bsd-3-clause |
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