repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
AntonelliLab/seqcap_processor | src/remove_short_contigs.py | 1 | 1290 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 26 15:21:03 2019
@author: Tobias Andermann (tobias.andermann@bioenv.gu.se)
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob
contig_folder = '/Users/tobias/GitHub/seqcap_processor/data/processed/contigs/'
cont... | mit |
kernalphage/adventOfCode | day6.py | 1 | 1105 | from __future__ import print_function
import re
import numpy as np
from matplotlib import pyplot as plt
re_rect = re.compile("[^\d]*(\d*),(\d*) through (\d*),(\d*)")
re_on = re.compile(".*on")
re_off = re.compile(".*off")
lights = np.zeros((1000,1000))
#### part 1
def turnOn(pt):
lights[ pt[0],pt[1] ] = 1
def tu... | mit |
mirandadam/bioinspired-optimization | src_python/multi_objective/test_mode.py | 1 | 3224 | #!/usr/bin/python3
# -*- coding: utf8 -*-
import numpy as np
import mode
import base
import sys
import time
sys.path.append('./ZDT')
sys.path.append('./DTLZ')
import ZDT1
import ZDT2
import ZDT3
import ZDT4
import DTLZ1_3obj
import DTLZ2_3obj
import DTLZ3_3obj
import DTLZ5_3obj
test_set=[
{'name':'ZDT1' ,'fun':ZDT1... | gpl-2.0 |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/scipy/stats/morestats.py | 8 | 94811 | # Author: Travis Oliphant, 2002
#
# Further updates and enhancements by many SciPy developers.
#
from __future__ import division, print_function, absolute_import
import math
import warnings
from collections import namedtuple
import numpy as np
from numpy import (isscalar, r_, log, around, unique, asarray,
... | mit |
ndingwall/scikit-learn | sklearn/linear_model/_logistic.py | 6 | 84460 | """
Logistic Regression
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Fabian Pedregosa <f@bianp.net>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Lars Buitinck
# Simon Wu <s8wu@uwaterloo.ca>
# ... | bsd-3-clause |
Lawrence-Liu/scikit-learn | examples/model_selection/plot_roc_crossval.py | 247 | 3253 | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.
ROC curv... | bsd-3-clause |
ruohoruotsi/Wavelet-Tree-Synth | nnet/autoencoder_variational.py | 1 | 4984 | '''This script demonstrates how to build a variational autoencoder with Keras.
Reference: "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114
'''
import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras import backend as K... | gpl-2.0 |
702nADOS/sumo | tools/sumolib/visualization/helpers.py | 1 | 13123 | """
@file helpers.py
@author Daniel Krajzewicz
@author Laura Bieker
@author Michael Behrisch
@date 2013-11-11
@version $Id: helpers.py 22608 2017-01-17 06:28:54Z behrisch $
Helper methods for plotting
SUMO, Simulation of Urban MObility; see http://sumo.dlr.de/
Copyright (C) 2013-2017 DLR (http://www.dlr.de/)... | gpl-3.0 |
joshloyal/scikit-learn | sklearn/neighbors/lof.py | 33 | 12186 | # Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from warnings import warn
from scipy.stats import scoreatpercentile
from .base import NeighborsBase
from .base import KNeighborsMixin
from .bas... | bsd-3-clause |
larose/ena | draw.py | 1 | 1309 | import itertools
import numpy
from matplotlib.collections import LineCollection
import matplotlib.pyplot as plt
def draw_intermediate_solution(cities, neurons, filename):
figure = plt.figure()
figure.gca().axison = False
_draw_cities(figure, cities)
_draw_elastic(figure, neurons)
figure.savefig(fil... | bsd-2-clause |
murali-munna/scikit-learn | sklearn/manifold/locally_linear.py | 206 | 25061 | """Locally Linear Embedding"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) INRIA 2011
import numpy as np
from scipy.linalg import eigh, svd, qr, solve
from scipy.sparse import eye, csr_matrix
from ..base import B... | bsd-3-clause |
tsurumeso/waifu2x-chainer | appendix/benchmark.py | 1 | 7652 | from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import time
import chainer
import matplotlib.pyplot as plt
import matplotlib.ticker as tick
import numpy as np
from PIL import Image
import six
sys.path.append('..')
from lib import iproc # NOQA
from lib import... | mit |
mast-group/sequence-mining | scripts/pr.py | 1 | 1869 | # Plot itemset precision-recall
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
rc('ps', fonttype=42)
rc('pdf', fonttype=42)
rc('xtick', labelsize=16)
rc('ytick', labelsize=16)
def main():
path = '/afs/inf.ed.ac.uk/user/j/jfowkes/Code/Sequences/PrecisionRecall/Background/'
... | gpl-3.0 |
mph-/lcapy | lcapy/zexpr.py | 1 | 10952 | """This module provides the ZDomainExpression class to represent z-domain expressions.
Copyright 2020--2021 Michael Hayes, UCECE
"""
from __future__ import division
from .domains import ZDomain
from .inverse_ztransform import inverse_ztransform
from .sym import j, pi, fsym, omegasym
from .dsym import nsym, ksym, zsy... | lgpl-2.1 |
XianliangJ/collections | DCTCPTest/plot_k_sweep.py | 1 | 2427 | '''
Plot queue occupancy over time
'''
from helper import *
import plot_defaults
from matplotlib.ticker import MaxNLocator
from pylab import figure
parser = argparse.ArgumentParser()
parser.add_argument('--files', '-f',
help="Queue timeseries output to one plot",
required=True... | gpl-3.0 |
felipebetancur/scipy | scipy/signal/spectral.py | 25 | 34809 | """Tools for spectral analysis.
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy import fftpack
from . import signaltools
from .windows import get_window
from ._spectral import lombscargle
import warnings
from scipy._lib.six import string_types
__all__ = ['periodogr... | bsd-3-clause |
klaus385/openpilot | selfdrive/test/plant/maneuverplots.py | 2 | 4751 | import os
import numpy as np
import matplotlib.pyplot as plt
import pylab
from selfdrive.config import Conversions as CV
class ManeuverPlot(object):
def __init__(self, title = None):
self.time_array = []
self.gas_array = []
self.brake_array = []
self.steer_torque_array = []
self.distance_arr... | mit |
leonardbinet/Transilien-Api | data_exploration/delay_prediction.py | 2 | 6368 | """ Module made to analyze training sets and provide predictions.
Parameters to chose:
- lines considered
- sequence_diff considered (predictions for how many stations ahead)
Then you should compute your own predictions on the test sample and assign it
to the y_pred variable so that plot and scores are computed.
"""
... | mit |
jamesturner246/mpfa | tools/arpra_mpfr_2d.py | 1 | 1265 |
import numpy as np
import matplotlib.pyplot as plt
# SETUP
# %load_ext autoreload
# %autoreload 2
# from tools.arpra_mpfr_2d import arpra_mpfr_2d
# #####
def arpra_mpfr_2d (x, y, t, i_start, i_stop, path='./', ax_traj=None, ax_x=None, ax_y=None):
with open(path + x, 'r') as xx_file, \
open(path + y, 'r... | lgpl-3.0 |
NunoEdgarGub1/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 394 | 1770 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal, assert_almost_equal
from sklearn.metrics.cluster.bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([T... | bsd-3-clause |
dieterich-lab/rp-bp | rpbp/reference_preprocessing/extract_orf_coordinates.py | 1 | 11486 | #! /usr/bin/env python3
"""This script extract the ORFs from the transcripts and
write them as a BED12+ file, using genomic coordinates.
Contains:
get_orf_positions
get_matching_stop_position
get_orf_bed_entry
get_orfs
get_transcript
"""
import sys
import logging
import argparse
import collection... | mit |
jvbalen/cover_id | learn.py | 1 | 13663 |
from __future__ import division, print_function
import numpy as np
import pandas as pd
import tensorflow as tf
class siamese_network():
def __init__(self, input_shape=(512,12)):
"""
"""
n_frames, n_bins = input_shape
self.x_A = tf.placeholder('float', shape=[None, n_frames, n_... | mit |
kaiserroll14/301finalproject | main/pandas/tests/test_graphics.py | 9 | 152089 | #!/usr/bin/env python
# coding: utf-8
import nose
import itertools
import os
import string
import warnings
from distutils.version import LooseVersion
from datetime import datetime, date
import pandas as pd
from pandas import (Series, DataFrame, MultiIndex, PeriodIndex, date_range,
bdate_range)
fr... | gpl-3.0 |
djfan/why_yellow_taxi | Output/1_dumbo_run_ys.py | 1 | 4662 | import sys
import pyproj
import csv
import shapely.geometry as geom
import fiona
import fiona.crs
import shapely
import rtree
import geopandas as gpd
import numpy as np
import operator
import pandas as pd
import pyspark
from pyspark import SparkContext
from shapely.geometry import Point
from pyspark.sql import SQLConte... | mit |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/sklearn/linear_model/tests/test_least_angle.py | 11 | 15904 | from nose.tools import assert_equal
import numpy as np
from scipy import linalg
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import ass... | mit |
jadelord/caeroc | setup.py | 1 | 2942 | import os
import sys
from runpy import run_path
from glob import glob
from setuptools import setup, find_packages
here = os.path.abspath(os.path.dirname(__file__))
# Get the long description from the relevant file
with open(os.path.join(here, 'README.rst')) as f:
long_description = f.read()
lines = long_descrip... | gpl-3.0 |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/preprocessing/tests/test_function_transformer.py | 46 | 3387 | import numpy as np
from sklearn.utils import testing
from sklearn.preprocessing import FunctionTransformer
from sklearn.utils.testing import assert_equal, assert_array_equal
def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
def _func(X, *args, **kwargs):
args_store.append(X)
ar... | mit |
wbadart/OS-Proj-5 | parse.py | 1 | 2773 | #!/afs/nd.edu/user15/pbui/pub/anaconda-2.3.0/bin/python
'''
' parse.py
'
' Take the results of benchmark.sh and turn them
' into pretty plots.
' Record page faults, disk reads, and disk writes for each
' program, for each eviction algorithm, using fixed 100 pages,
' for each N frames from 3 to 100.
'
' Badart, Cat
' ... | gpl-3.0 |
grapesmoker/nba | drawing/court.py | 1 | 1250 | __author__ = 'jerry'
from matplotlib.patches import Arc, RegularPolygon, Circle
from matplotlib.colors import Normalize, BoundaryNorm, ListedColormap
from matplotlib.colorbar import ColorbarBase
from matplotlib import gridspec
import matplotlib.pyplot as mpl
def draw_court(ax):
ax.set_xlim(-25, 25)
ax.set_y... | gpl-2.0 |
adamallo/scripts_singlecrypt | subsmodel/evaluate28_DM.py | 1 | 3537 | # This program uses the 28-state model to evaluate the likelihood
# of a tiny tree at various branch lengths, demonstrating how
# the evaluations work. It relies on a rate matrix made by
# program ratematrix28.py, and uses the eigenvalue/eigenvector
# approach to compute the likelihoods.
epsilon = 0.0000000000... | gpl-3.0 |
murali-munna/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 |
escorciav/video-utils | tools/video_info.py | 1 | 2177 | "Dump CSV with metadata of many videos"
import argparse
import os
import pandas as pd
from joblib import Parallel, delayed
from okvideo.ffmpeg import (get_duration, get_frame_rate, get_num_frames,
get_resolution)
def video_stats(filename, dirname):
stats = {}
stats['video_name'] ... | mit |
mahajrod/MACE | scripts/draw_coverage_per_scaffold.py | 1 | 11064 | #!/usr/bin/env python
__author__ = 'Sergei F. Kliver'
import os
import pandas as pd
import argparse
from copy import deepcopy
from _collections import OrderedDict
import pandas as pd
from BCBio import GFF
from RouToolPa.Collections.General import SynDict, IdList
from RouToolPa.Parsers.VCF import CollectionVCF
from MAC... | apache-2.0 |
cwoodall/doppler-gestures-py | pydoppler/ambiguity.py | 2 | 3333 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# ryanvolz's Ambiguity Function](https://gist.github.com/ryanvolz/8b0d9f3e48ec8ddcef4d
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
def ambiguity(code, nfreq=1):
"""Calculate the ambiguity function of code for nfreq ... | mit |
amandalund/openmc | docs/source/conf.py | 3 | 7739 | # -*- coding: utf-8 -*-
#
# metasci documentation build configuration file, created by
# sphinx-quickstart on Sun Feb 7 22:29:49 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.
#
# All... | mit |
jaidevd/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
chrsrds/scikit-learn | sklearn/inspection/tests/test_permutation_importance.py | 1 | 5572 | import pytest
import numpy as np
from numpy.testing import assert_allclose
from sklearn.compose import ColumnTransformer
from sklearn.datasets import load_boston
from sklearn.datasets import load_iris
from sklearn.datasets import make_regression
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble... | bsd-3-clause |
zcbenz/cefode-chromium | chrome/browser/nacl_host/test/gdb_rsp.py | 99 | 2431 | # 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 |
pv/scikit-learn | examples/cluster/plot_ward_structured_vs_unstructured.py | 320 | 3369 | """
===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================
Example builds a swiss roll dataset and runs
hierarchical clustering on their position.
For more information, see :ref:`hierarchical_clus... | bsd-3-clause |
anugrah-saxena/pycroscopy | pycroscopy/viz/plot_utils.py | 1 | 50315 | # -*- coding: utf-8 -*-
"""
Created on Thu May 05 13:29:12 2016
@author: Suhas Somnath
"""
# TODO: All general plotting functions should support data with 1, 2, or 3 spatial dimensions.
from __future__ import division, print_function, absolute_import, unicode_literals
import inspect
from warnings import warn
import ... | mit |
cedadev/cis | cis/plotting/formatted_plot.py | 2 | 8986 | """
Routines for creating a plot and then formatting it, using command line options. It is not intended for plotting
directly from Python, although it could be used for that.
"""
def set_log_scales(ax, logx, logy, rescale=True):
"""
Optionally log-scale one or both of the axis
"""
if logx:
ax.... | lgpl-3.0 |
Obus/scikit-learn | examples/linear_model/plot_sgd_loss_functions.py | 249 | 1095 | """
==========================
SGD: convex loss functions
==========================
A plot that compares the various convex loss functions supported by
:class:`sklearn.linear_model.SGDClassifier` .
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def modified_huber_loss(y_true, y_pred):
z ... | bsd-3-clause |
adamgreenhall/scikit-learn | sklearn/datasets/tests/test_lfw.py | 230 | 7880 | """This test for the LFW require medium-size data dowloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
... | bsd-3-clause |
longle2718/audio_loc | python/audio_loc.py | 1 | 7082 | '''
Utility functions
Long Le <longle1@illinois.edu>
University of Illinois
'''
import numpy as np
import matplotlib.pyplot as plt
import multiprocessing
from joblib import Parallel, delayed
import os,sys
os.system("taskset -p 0xff %d" % os.getpid())
sys.path.append(os.path.expanduser('~')+'/audio_class/python')
sys.... | mit |
h2educ/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
rajat1994/scikit-learn | sklearn/tests/test_grid_search.py | 83 | 28713 | """
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 |
lancezlin/ml_template_py | lib/python2.7/site-packages/sklearn/utils/multiclass.py | 41 | 14732 |
# 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.... | mit |
sammosummo/sammosummo.github.io | assets/scripts/bimodal-distribution.py | 1 | 1213 | """Figure illustrating a bimodal distribution.
"""
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
from scipy.stats import norm
if __name__ == "__main__":
from matplotlib import rcParams as defaults
figsize = defaults["figure.figsize"]
# defaults["figure.figsize"] = [figsize[0],... | mit |
poojavade/Genomics_Docker | Dockerfiles/gedlab-khmer-filter-abund/pymodules/python2.7/lib/python/ipython-2.2.0-py2.7.egg/IPython/kernel/zmq/kernelapp.py | 7 | 18674 | """An Application for launching a kernel
Authors
-------
* MinRK
"""
#-----------------------------------------------------------------------------
# Copyright (C) 2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full license is in
# the file COPYING.txt, distributed as pa... | apache-2.0 |
SCP-028/UGA | archive/metastasis/classifier/libs/logistic.py | 1 | 1290 | #!python3
from sklearn.linear_model import LogisticRegressionCV
def logistic_regression(train, train_labels, n_jobs=3,
score_method='f1_weighted', max_iter=4000):
"""Train a logistic regression model for multi-class classification.
Parameters
----------
train: ar... | apache-2.0 |
bnaul/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | 15 | 7570 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
RomainBrault/scikit-learn | sklearn/tests/test_multioutput.py | 23 | 12429 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.utils import shuffle
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_raises_regex
from s... | bsd-3-clause |
jakevdp/bokeh | sphinx/source/tutorial/solutions/stocks.py | 3 | 2503 |
import numpy as np
import pandas as pd
from bokeh.plotting import *
# Here is some code to read in some stock data from the Yahoo Finance API
AAPL = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000",
parse_dates=['Date'])
GOOG = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=GOOG&... | bsd-3-clause |
kiyoto/statsmodels | statsmodels/stats/contingency_tables.py | 2 | 43471 | """
Methods for analyzing two-way contingency tables (i.e. frequency
tables for observations that are cross-classified with respect to two
categorical variables).
The main classes are:
* Table : implements methods that can be applied to any two-way
contingency table.
* SquareTable : implements methods that can... | bsd-3-clause |
cuemacro/finmarketpy | finmarketpy_examples/fx_options_pricing_examples.py | 1 | 14004 | __author__ = 'saeedamen'
#
# Copyright 2020 Cuemacro
#
# 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 ... | apache-2.0 |
julien6387/supvisors | supvisors/tests/test_plot.py | 2 | 2736 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# ======================================================================
# Copyright 2017 Julien LE CLEACH
#
# 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 Lic... | apache-2.0 |
ryanbressler/pydec | Examples/ResonantCavity/driver.py | 6 | 2164 | """
Solve the resonant cavity problem with Whitney forms.
References:
Douglas N. Arnold and Richard S. Falk and Ragnar Winther
"Finite element exterior calculus: from Hodge theory to numerical
stability"
Bull. Amer. Math. Soc. (N.S.), vol. 47, No. 2, pp. 281--354
DOI : 10.1090/S0273-0979-10-01278-4... | bsd-3-clause |
canavandl/bokeh | examples/compat/mpl/subplots.py | 13 | 1798 | """
Edward Tufte uses this example from Anscombe to show 4 datasets of x
and y that have the same mean, standard deviation, and regression
line, but which are qualitatively different.
matplotlib fun for a rainy day
"""
import matplotlib.pyplot as plt
import numpy as np
from bokeh import mpl
from bokeh.plotting import... | bsd-3-clause |
squishbug/DataScienceProgramming | DataScienceProgramming/09-Machine-Learning-II/create_configurations.py | 2 | 1120 | #!/usr/bin/env python3.4
import pandas as pd
import itertools
DATAFILE = '/home/data/archive.ics.uci.edu/BankMarketing/bank.csv'
MAX_DEPTH = '5,10'
N_FEATURE = '5,14'
NITER = 20
def spl_range(X):
v = [int(t) for t in X.split(',')]
return range(v[0], v[1]+1)
if __name__ == '__main__':
maxdepth = MAX_DEP... | cc0-1.0 |
anntzer/scikit-learn | sklearn/isotonic.py | 6 | 14227 | # Authors: Fabian Pedregosa <fabian@fseoane.net>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Nelle Varoquaux <nelle.varoquaux@gmail.com>
# License: BSD 3 clause
import numpy as np
from scipy import interpolate
from scipy.stats import spearmanr
import warnings
import math
from .base import B... | bsd-3-clause |
mxjl620/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
kazemakase/scikit-learn | examples/decomposition/plot_ica_blind_source_separation.py | 349 | 2228 | """
=====================================
Blind source separation using FastICA
=====================================
An example of estimating sources from noisy data.
:ref:`ICA` is used to estimate sources given noisy measurements.
Imagine 3 instruments playing simultaneously and 3 microphones
recording the mixed si... | bsd-3-clause |
tensorflow/models | research/delf/delf/python/examples/extract_boxes.py | 1 | 7510 | # 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 applicab... | apache-2.0 |
acimmarusti/isl_exercises | chap5/chap5ex9.py | 1 | 2124 | from __future__ import print_function, division
import matplotlib.pyplot as plt
import numpy as np
import scipy
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_boston
import statsmodels.formula.api as smf
#Load boston dataset from sklearn#
boston = load_boston()
#Columns#
#print(boston['fe... | gpl-3.0 |
jrh154/ChibbarGroup | Phylogeny Scripts/ncbi_sequence_grabber.py | 2 | 2779 | '''
Script "suite" for grabbing and analyzing files from the NCBI database. Generally, the program will
take a list of accession numbers and retrieve either the protein or nucleotide sequence and can save the
file in either genbank or fasta form. The file containing the accession numbers should be in csv format
with a ... | mit |
cameronlai/ml-class-python | solutions/ex2/ex2_reg.py | 1 | 3520 | import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import fmin_ncg
from ex2 import *
## Machine Learning Online Class - Exercise 2: Logistic Regression
# Instructions
# ------------
#
# This file contains code that helps you get started on the second part
# of the exercise which covers regula... | mit |
CSB-IG/natk | ninnx/pruning/mi_triangles.py | 2 | 1793 | import networkx as nx
import itertools
import matplotlib.pyplot as plt
fig = plt.figure()
fig.subplots_adjust(left=0.2, wspace=0.6)
G = nx.Graph()
G.add_edges_from([(1,2,{'w': 6}),
(2,3,{'w': 3}),
(3,1,{'w': 4}),
(3,4,{'w': 12}),
(4,5,{'w': 13})... | gpl-3.0 |
vibhorag/scikit-learn | examples/linear_model/plot_ransac.py | 250 | 1673 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import ... | bsd-3-clause |
sorgerlab/indra | indra/assemblers/indranet/net.py | 3 | 14200 | import json
import logging
from os import path
import numpy as np
import pandas as pd
import networkx as nx
from decimal import Decimal
import indra
from indra.belief import SimpleScorer
from indra.statements import Evidence
from indra.statements import Statement
logger = logging.getLogger(__name__)
simple_scorer = ... | bsd-2-clause |
magne-max/zipline-ja | tests/pipeline/test_us_equity_pricing_loader.py | 1 | 20821 | #
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
rbalda/neural_ocr | env/lib/python2.7/site-packages/matplotlib/backends/backend_pdf.py | 7 | 95987 | # -*- coding: utf-8 -*-
"""
A PDF matplotlib backend
Author: Jouni K Seppänen <jks@iki.fi>
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import codecs
import os
import re
import struct
import sys
import time
impor... | mit |
aspiringguru/sentexTuts | PracMachLrng/sentex_ML_demo2.py | 1 | 2516 | #working exercise from sentex tutorials. with mods for clarification + api doc references.
#Regression Intro - Practical Machine Learning Tutorial with Python p.3
#
import pandas as pd
import sklearn
import quandl
import math
stockcode = 'WIKI/GOOGL'
print ("getting data")
df = quandl.get(stockcode)
#http://pandas.py... | mit |
gnomex/analysis | src/many_pairwise_correlations.py | 1 | 1085 | """
Plotting a diagonal correlation matrix
======================================
_thumb: .3, .6
"""
from string import ascii_letters
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white")
# Generate a large random dataset
rs = np.random.RandomState(33)
d ... | gpl-3.0 |
kubeflow/kfserving | python/sklearnserver/sklearnserver/test_model.py | 1 | 2107 | # Copyright 2019 kubeflow.org.
#
# 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 writing,... | apache-2.0 |
UDST/activitysim | activitysim/abm/models/util/test/test_mandatory_tour_frequency.py | 2 | 1866 | # ActivitySim
# See full license in LICENSE.txt.
import pytest
import os
import pandas as pd
import pandas.util.testing as pdt
from ..tour_frequency import process_mandatory_tours
def mandatory_tour_frequency_alternatives():
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
f = os.path.join(c... | bsd-3-clause |
jaidevd/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 |
jfemiani/srp-boxes | srp/visualize/plots.py | 1 | 3158 | """Various functions to plot data.
Plotting data from the torch dataloader during training:
* plot_rgb: To plot the RGB portion of conctneated color+volumetric data
* plot_lidar: To plot the LiDAR portion of concatenated color+volumetric data
* plot_box: To plot the oriented bounding box, alligned with the plots ... | mit |
Vimos/scikit-learn | sklearn/gaussian_process/kernels.py | 31 | 67169 | """Kernels for Gaussian process regression and classification.
The kernels in this module allow kernel-engineering, i.e., they can be
combined via the "+" and "*" operators or be exponentiated with a scalar
via "**". These sum and product expressions can also contain scalar values,
which are automatically converted to... | bsd-3-clause |
yngcan/patentprocessor | get_invpat.py | 6 | 3576 | """
Copyright (c) 2013 The Regents of the University of California, AMERICAN INSTITUTES FOR RESEARCH
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the abo... | bsd-2-clause |
krez13/scikit-learn | examples/decomposition/plot_sparse_coding.py | 27 | 4037 | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` esti... | bsd-3-clause |
psi4/mongo_qcdb | qcfractal/interface/models/rest_models.py | 1 | 44921 | """
Models for the REST interface
"""
import functools
import re
import warnings
from typing import Any, Dict, List, Optional, Tuple, Union
from pydantic import Field, constr, root_validator, validator
from qcelemental.util import get_base_docs
from .common_models import KeywordSet, Molecule, ObjectId, ProtoModel
fro... | bsd-3-clause |
priyanmuthu/priyanmuthu.github.io | markdown_generator/talks.py | 199 | 4000 |
# coding: utf-8
# # Talks markdown generator for academicpages
#
# Takes a TSV of talks with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook ([see more info here](http://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_i... | mit |
zuku1985/scikit-learn | examples/gaussian_process/plot_gpr_prior_posterior.py | 104 | 2878 | """
==========================================================================
Illustration of prior and posterior Gaussian process for different kernels
==========================================================================
This example illustrates the prior and posterior of a GPR with different
kernels. Mean, st... | bsd-3-clause |
burakbayramli/emacs-ipython | ipython-tex.py | 2 | 6294 | from Pymacs import lisp
import re, time, os, glob
interactions = {}
from IPython.testing.globalipapp import get_ipython
from IPython.utils.io import capture_output
from memo import *
@memo
def get_ip():
ip = get_ipython()
ip.run_cell('%load_ext autoreload')
ip.run_cell('%autoreload 2')
ip... | gpl-3.0 |
IshankGulati/scikit-learn | sklearn/utils/tests/test_multiclass.py | 58 | 14316 |
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 |
Obus/scikit-learn | sklearn/svm/classes.py | 37 | 39951 | import warnings
import numpy as np
from .base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
LinearModel
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import check_X... | bsd-3-clause |
larsoner/mne-python | mne/decoding/tests/test_time_frequency.py | 14 | 1199 | # Author: Jean-Remi King, <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
from numpy.testing import assert_array_equal
import pytest
from mne.utils import requires_sklearn
from mne.decoding.time_frequency import TimeFrequency
@requires_sklearn
def test_timefrequency():
"""Test TimeFreq... | bsd-3-clause |
abhitopia/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/dataframe.py | 85 | 4704 | # 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 |
Jozhogg/iris | docs/iris/example_tests/extest_util.py | 1 | 2324 | # (C) British Crown Copyright 2010 - 2014, Met Office
#
# This file is part of Iris.
#
# Iris is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any l... | lgpl-3.0 |
nmayorov/scipy | doc/source/tutorial/stats/plots/mgc_plot4.py | 11 | 1341 | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import multiscale_graphcorr
def mgc_plot(x, y, mgc_dict):
"""Plot sim and MGC-plot"""
plt.figure(figsize=(8, 8))
ax = plt.gca()
# local correlation map
mgc_map = mgc_dict["mgc_map"]
# draw heatmap
ax.set_title("Local Cor... | bsd-3-clause |
ternaus/kaggle_digit_recognizer | src/convolutional_modern.py | 1 | 5449 | from __future__ import division
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from lasagne.nonlinearities import softmax
from sklearn.preprocessing import StandardScaler
import numpy as np
from sklearn.preprocessing import LabelEncoder
__author__ = 'Vladi... | mit |
Achuth17/scikit-learn | examples/cluster/plot_mini_batch_kmeans.py | 265 | 4081 | """
====================================================================
Comparison of the K-Means and MiniBatchKMeans clustering algorithms
====================================================================
We want to compare the performance of the MiniBatchKMeans and KMeans:
the MiniBatchKMeans is faster, but give... | bsd-3-clause |
cangumeli/ResNets.jl | plot.py | 1 | 1509 | import matplotlib.pyplot as plt
files = [
('ResNet110', 'train_resnet110.out'),
('ResNet32', 'train_resnet32.out')
]
for tf in files:
title, fname = tf
with open(fname) as f:
lines = filter(lambda x: x.startswith('(:iter'), f.readlines())
iters = []
trns = []
tsts = []
... | gpl-3.0 |
HGladiator/MyCodes | Python/exercise/python_day3_exercise.py | 1 | 7166 | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 30 16:36:10 2017
@author: Isola
"""
'''
1.X1 LIMIT_BAL 代表额度
2.X2 GENDER 代表性别，1为男性，2为女性，值种类2种
3.X3 EDUCATION 代表受教育水平，值种类6种
4.X4 MARRIAGE 代表是否婚配，值种类4种
5.X4 AGE 代表年龄
6.X6-X11 PAY_0至PAY_6代表延期时间,从September-April；-1表示按时还款，-2表示未消费，正数代表延期几个月
7.X12-X17 BILL_AMT1至BILL_AMT6代表当期账单金额... | mit |
NeowithU/Trajectory | Outdated/osm_map.py | 1 | 6180 | # -*- coding:utf-8 -*-
__author__ = 'Neo'
import unicodecsv
import overpass
import os
import glob
import numpy as np
from sklearn.cluster import DBSCAN, MeanShift, estimate_bandwidth, Birch
from sklearn.metrics.pairwise import euclidean_distances
from utilities import read_json
from utilities import write_pickle
from... | mit |
ElDeveloper/scikit-learn | examples/cluster/plot_kmeans_digits.py | 230 | 4524 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
BrechtBa/mpcpy | examples/simple_space_heating_mpc.py | 1 | 11279 | #!/usr/bin/env python
################################################################################
# Copyright 2015 Brecht Baeten
# This file is part of mpcpy.
#
# mpcpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# th... | gpl-3.0 |
kaiserroll14/301finalproject | main/pandas/tests/test_stats.py | 12 | 6100 | # -*- coding: utf-8 -*-
from pandas import compat
import nose
from numpy import nan
import numpy as np
from pandas import Series, DataFrame
from pandas.compat import product
from pandas.util.testing import (assert_frame_equal,
assert_series_equal,
ass... | gpl-3.0 |
lucfra/RFHO | rfho/datasets.py | 1 | 47300 | """
This module contains utility functions to process and load various datasets. Most of the datasets are public,
but are not included in the package; MNIST dataset will be automatically downloaded.
There are also some classes to represent datasets. `ExampleVisiting` is an helper class that implements
the stochastic s... | mit |
belltailjp/scikit-learn | sklearn/utils/validation.py | 66 | 23629 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals i... | bsd-3-clause |
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