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 |
|---|---|---|---|---|---|
yuanming-hu/taichi | examples/tree_gravity.py | 1 | 11571 | # N-body gravity simulation in 300 lines of Taichi, tree method, no multipole, O(N log N)
# Author: archibate <1931127624@qq.com>, all left reserved
import taichi_glsl as tl
import taichi as ti
ti.init()
if not hasattr(ti, 'jkl'):
ti.jkl = ti.indices(1, 2, 3)
kUseTree = True
#kDisplay = 'tree mouse pixels cmap s... | mit |
ahoyosid/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
aewhatley/scikit-learn | benchmarks/bench_sample_without_replacement.py | 397 | 8008 | """
Benchmarks for sampling without replacement of integer.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import operator
import matplotlib.pyplot as plt
import numpy as np
import random
from sklearn.externals.six.moves i... | bsd-3-clause |
droundy/deft | papers/fuzzy-fmt/plot-FE_vs_gw.py | 1 | 2561 | #!/usr/bin/python2
#This program creates a plot of Free Energy difference vs gw at a specified
#temperature and density from data in kT*n*alldat.dat (or kT*n*alldat_tensor.dat) files
#which are generated as output data files by figs/new-melting.cpp
#NOTE: Run this plot script from directory deft/papers/fuzzy-fmt
#w... | gpl-2.0 |
cybernet14/scikit-learn | sklearn/cluster/tests/test_birch.py | 342 | 5603 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.l... | bsd-3-clause |
rohangoel96/IRCLogParser | IRCLogParser/lib/deprecated/scripts/parser-time_series.py | 2 | 6235 | #This code generates a time-series graph. Such a graph has users on the y axis and msg transmission time on x axis.This means that if there exit 4 users- A,B,C,D.
#Then if any of these users send a message at time t, then we put a dot infront of that user at time t in the graph.
import os.path
import re
import network... | mit |
ZENGXH/scikit-learn | sklearn/covariance/robust_covariance.py | 198 | 29735 | """
Robust location and covariance estimators.
Here are implemented estimators that are resistant to outliers.
"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
from scipy import linalg
from scipy.stats import chi2
from . import empir... | bsd-3-clause |
Ziqi-Li/bknqgis | bokeh/bokeh/plotting/tests/test_helpers.py | 1 | 5233 | import pytest
from bokeh.models import ColumnDataSource
from bokeh.models.ranges import Range1d, DataRange1d, FactorRange
from bokeh.models.scales import LinearScale, LogScale, CategoricalScale
from bokeh.plotting.helpers import _get_legend_item_label, _get_scale, _get_range, _stack
def test__stack_raises_when_spec_i... | gpl-2.0 |
rhyolight/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_fltkagg.py | 69 | 20839 | """
A backend for FLTK
Copyright: Gregory Lielens, Free Field Technologies SA and
John D. Hunter 2004
This code is released under the matplotlib license
"""
from __future__ import division
import os, sys, math
import fltk as Fltk
from backend_agg import FigureCanvasAgg
import os.path
import matplotli... | agpl-3.0 |
plissonf/scikit-learn | examples/classification/plot_lda.py | 70 | 2413 | """
====================================================================
Normal and Shrinkage Linear Discriminant Analysis for classification
====================================================================
Shows how shrinkage improves classification.
"""
from __future__ import division
import numpy as np
import... | bsd-3-clause |
justincassidy/scikit-learn | examples/svm/plot_rbf_parameters.py | 132 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radial Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
evgchz/scikit-learn | benchmarks/bench_glmnet.py | 297 | 3848 | """
To run this, you'll need to have installed.
* glmnet-python
* scikit-learn (of course)
Does two benchmarks
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of... | bsd-3-clause |
surgebiswas/poker | PokerBots_2017/Johnny/scipy/special/add_newdocs.py | 8 | 137472 | # Docstrings for generated ufuncs
#
# The syntax is designed to look like the function add_newdoc is being
# called from numpy.lib, but in this file add_newdoc puts the
# docstrings in a dictionary. This dictionary is used in
# generate_ufuncs.py to generate the docstrings for the ufuncs in
# scipy.special at the C lev... | mit |
WhatDo/FlowFairy | examples/denoise_reg_mult/stages.py | 1 | 3899 | import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import os
import io
from datetime import datetime
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from flowfairy.core.stage import register, Stage, stage
from flowfairy.conf import settings
def get_log_dir():
... | mit |
mojoboss/scikit-learn | sklearn/neighbors/classification.py | 106 | 13987 | """Nearest Neighbor Classification"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output support by ... | bsd-3-clause |
joonaslomps/hiragana-ocr | code/test.py | 1 | 20424 | # import the necessary packages
import argparse
import datetime
import imutils
import time
import cv2
import numpy as np
from random import shuffle
from matplotlib import pyplot as plt
from os import listdir
from os.path import isfile, join
letters = ["a","i","u","e","o","ka","ki","ku","ke","ko","sa","shi","su","se","... | mit |
thientu/scikit-learn | sklearn/cluster/setup.py | 263 | 1449 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
cblas_libs, blas_info = ... | bsd-3-clause |
thientu/scikit-learn | sklearn/datasets/lfw.py | 141 | 19372 | """Loader for the Labeled Faces in the Wild (LFW) dataset
This dataset is a collection of JPEG pictures of famous people collected
over the internet, all details are available on the official website:
http://vis-www.cs.umass.edu/lfw/
Each picture is centered on a single face. The typical task is called
Face Veri... | bsd-3-clause |
btabibian/scikit-learn | examples/linear_model/plot_sgd_loss_functions.py | 86 | 1234 | """
==========================
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 |
meduz/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 24 | 14430 | 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 |
samnashi/howdoflawsgetlonger | generator_columns_tester.py | 1 | 10893 | from __future__ import print_function
import numpy as np
from random import shuffle
import matplotlib.pyplot as plt
from keras.models import Sequential, Model
from keras.utils import plot_model
from keras.layers import Dense, LSTM, GRU, Flatten, Input, Reshape, TimeDistributed, Bidirectional, Dense, Dropout, \
Acti... | gpl-3.0 |
achim1/HErmes | HErmes/selection/dataset.py | 2 | 29071 | """
Datasets group categories together. Method calls on datasets invoke the individual methods
on the individual categories. Cuts applied to datasets will act on each individual category.
"""
import pandas as pd
import numpy as np
from collections import OrderedDict
from copy import deepcopy as copy
from ..visual i... | gpl-2.0 |
alemottura/PyCAPI | uob_scripts/timeline.py | 1 | 6159 | #
# timeline.py
#
# This code will create a timeline plot for a university year of all
# assignment deadlines for all courses against key dates such as holidays
#
#
# Things that need to be set:
#
# year - the university year the timeline is plotted for
year = 2016
import uob_utils
import ... | mit |
hdzierz/Kaka | mongcore/connectors.py | 1 | 12040 | # -*- coding: utf-8 -*-
# Django imports
from django.db import connection, connections
# import data serializers
import gzip
import csv
import xlrd
import pandas as pd
import vcf
# Project imports
from .logger import *
from .algorithms import *
############################
## Data connectors are building on teh alg... | gpl-2.0 |
zetaris/zeppelin | python/src/main/resources/python/mpl_config.py | 41 | 3653 | # Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use ... | apache-2.0 |
rohit21122012/DCASE2013 | runs/2016/baseline32/src/dataset.py | 37 | 78389 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import urllib2
import socket
import locale
import zipfile
import tarfile
from sklearn.cross_validation import StratifiedShuffleSplit, KFold
from ui import *
from general import *
from files import *
class Dataset(object):
"""Dataset base class.
The sp... | mit |
nickabattista/IB2d | pyIB2d/Examples/Rubberband_with_Beams/Rubberband.py | 1 | 10247 | '''-------------------------------------------------------------------------
IB2d is an Immersed Boundary Code (IB) for solving fully coupled non-linear
fluid-structure interaction models. This version of the code is based off of
Peskin's Immersed Boundary Method Paper in Acta Numerica, 2002.
Author: Nicholas A... | gpl-3.0 |
BiaDarkia/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | 17 | 7819 | """
===============================
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 |
henridwyer/scikit-learn | examples/cluster/plot_agglomerative_clustering.py | 343 | 2931 | """
Agglomerative clustering with and without structure
===================================================
This example shows the effect of imposing a connectivity graph to capture
local structure in the data. The graph is simply the graph of 20 nearest
neighbors.
Two consequences of imposing a connectivity can be s... | bsd-3-clause |
plotly/python-api | packages/python/plotly/plotly/graph_objs/_carpet.py | 1 | 63024 | from plotly.basedatatypes import BaseTraceType as _BaseTraceType
import copy as _copy
class Carpet(_BaseTraceType):
# class properties
# --------------------
_parent_path_str = ""
_path_str = "carpet"
_valid_props = {
"a",
"a0",
"aaxis",
"asrc",
"b",
... | mit |
Soya93/Extract-Refactoring | python/helpers/pydev/pydev_ipython/matplotlibtools.py | 12 | 5436 |
import sys
backends = {'tk': 'TkAgg',
'gtk': 'GTKAgg',
'wx': 'WXAgg',
'qt': 'Qt4Agg', # qt3 not supported
'qt4': 'Qt4Agg',
'osx': 'MacOSX'}
# We also need a reverse backends2guis mapping that will properly choose which
# GUI support to activate based on the... | apache-2.0 |
w1kke/pylearn2 | pylearn2/models/independent_multiclass_logistic.py | 44 | 2491 | """
Multiclass-classification by taking the max over a set of one-against-rest
logistic classifiers.
"""
__authors__ = "Ian Goodfellow"
__copyright__ = "Copyright 2010-2012, Universite de Montreal"
__credits__ = ["Ian Goodfellow"]
__license__ = "3-clause BSD"
__maintainer__ = "LISA Lab"
__email__ = "pylearn-dev@googleg... | bsd-3-clause |
thomasaarholt/hyperspy | hyperspy/tests/drawing/test_plot_signal.py | 3 | 10437 | # Copyright 2007-2020 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later ... | gpl-3.0 |
iModels/demos | demos/ethane_box/ethane_box.py | 1 | 2466 | import os
import time
import matplotlib.pyplot as plt
import seaborn as sns
import mbuild as mb
import metamds as mds
import mdtraj as md
def build_ethane_box(box, n_molecules, **kwargs):
from mbuild.examples import Ethane
ethane = Ethane()
full_box = mb.fill_box(ethane, n_molecules, box)
full_box.n... | mit |
tillahoffmann/tensorflow | tensorflow/python/estimator/inputs/inputs.py | 94 | 1290 | # 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 |
mne-tools/mne-tools.github.io | 0.11/_downloads/plot_ems_filtering.py | 19 | 2981 | """
==============================================
Compute effect-matched-spatial filtering (EMS)
==============================================
This example computes the EMS to reconstruct the time course of
the experimental effect as described in:
Aaron Schurger, Sebastien Marti, and Stanislas Dehaene, "Reducing mu... | bsd-3-clause |
ngoix/OCRF | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
ankurankan/scikit-learn | sklearn/metrics/setup.py | 299 | 1024 | import os
import os.path
import numpy
from numpy.distutils.misc_util import Configuration
from sklearn._build_utils import get_blas_info
def configuration(parent_package="", top_path=None):
config = Configuration("metrics", parent_package, top_path)
cblas_libs, blas_info = get_blas_info()
if os.name ==... | bsd-3-clause |
birdsarah/bokeh | bokeh/mplexporter/renderers/base.py | 11 | 14395 | from __future__ import absolute_import
import warnings
import itertools
from contextlib import contextmanager
import numpy as np
from matplotlib import transforms
from .. import utils
from .. import _py3k_compat as py3k
class Renderer(object):
@staticmethod
def ax_zoomable(ax):
return bool(ax and a... | bsd-3-clause |
schets/scikit-learn | examples/linear_model/plot_iris_logistic.py | 283 | 1678 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <http://en.wikipedia.org/wiki/Iris_f... | bsd-3-clause |
jayshonzs/ESL | PropertypeMethodsAndKNN/LVQ.py | 1 | 2783 | '''
Created on 2014-8-7
@author: xiajie
'''
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import simulate_data
import K_means
def euclidean(x1, x2):
return np.linalg.norm(x1-x2)
def move(center, x, eps):
d = x - center
center = center + eps*d
def train(X, model, distance=eu... | mit |
quimaguirre/diana | diana/classes/drug.py | 1 | 51685 | import os, sys, re
import pickle
import pandas as pd
import hashlib
class Drug(object):
"""
Class defining a Drug object
"""
def __init__(self, drug_name):
"""
@param: drug_name
@pdef: Name of the drug
@ptype: {String}
@raises: {IncorrectTypeID} if t... | mit |
gpotter2/scapy | setup.py | 2 | 3463 | #! /usr/bin/env python
"""
Distutils setup file for Scapy.
"""
try:
from setuptools import setup, find_packages
except:
raise ImportError("setuptools is required to install scapy !")
import io
import os
def get_long_description():
"""Extract description from README.md, for PyPI's usage"""
def proces... | gpl-2.0 |
fzenke/morla | scripts/compute_gramian.py | 1 | 5542 | #!/usr/bin/python3
from __future__ import print_function
import os,sys,inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir)
import numpy as np
import scipy
from scipy import sparse
from tqdm import tqdm
i... | mit |
blaisb/cfdemUtilities | mixing/pca/pcaGenerator.py | 2 | 4377 | #--------------------------------------------------------------------------------------------------
#
# Description : Sample program to generate random trajectories and to analyse them using PCA
#
# Usage : python pcaMixingRadial
#
#
# Author : Bruno Blais
#
#----------------------------------------------------... | lgpl-3.0 |
manashmndl/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 |
mbaumBielefeld/popkin | popkin/visualization/placefieldvisualizer.py | 1 | 3753 | import matplotlib.pyplot as plt
import numpy as np
import math
class PlaceFieldVisualizer:
def __init__(self, fields):
self.fields=fields
self.n_fields=len(fields)
self.callbacks={}
self.callbacks_click={}
self.fignum=56
fig=plt.figure(self.fignum)
cid = f... | gpl-2.0 |
Xinglab/rmats2sashimiplot | src/MISO/misopy/sashimi_plot/plot_utils/plot_gene.py | 1 | 33223 | ##
## Draw gene structure from a GFF file
##
import os, sys, operator, subprocess
import math
import pysam
import numpy as np
import glob
from pylab import *
from matplotlib.patches import PathPatch
from matplotlib.path import Path
import matplotlib.cm as cm
import misopy
import misopy.gff_utils as gff_utils
import mi... | gpl-2.0 |
JPFrancoia/scikit-learn | benchmarks/bench_plot_omp_lars.py | 28 | 4471 | """Benchmarks of orthogonal matching pursuit (:ref:`OMP`) versus least angle
regression (:ref:`least_angle_regression`)
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model impo... | bsd-3-clause |
nathanhilbert/ulmo | ulmo/util/misc.py | 3 | 9838 | from contextlib import contextmanager
import datetime
import email.utils
import ftplib
import functools
import os
import re
import urlparse
import warnings
import appdirs
from lxml import etree
import numpy as np
import pandas
import requests
# pre-compiled regexes for underscore conversion
first_cap_re = re.compile... | bsd-3-clause |
idf/scipy_util | scipy_util/regressions/logistic_regression.py | 1 | 1748 | import numpy as np
from scipy.stats import logistic
import matplotlib.pyplot as plt
class LogisticRegressioner(object):
def __init__(self, tol=1e-6):
self.tol = tol
def first_derivative(self, X, Y, w):
"""
Calculate the 1st derivative of log-loss function
"""
d, T = X.s... | bsd-3-clause |
studywolf/blog | tracking_control/tracking_control5.py | 1 | 4790 | """ An implementation based on the 2-link arm plant and controller from
(Slotine & Sastry, 1983).
"""
import matplotlib.pyplot as plt
import numpy as np
import seaborn
class plant:
def __init__(self, dt=.001, theta1=[0.0, 0.0], theta2=[0.0, 0.0]):
"""
dt float: simulation time step
th... | gpl-3.0 |
srgblnch/MeasuredFillingPattern | tango-ds/MeasuredFillingPatternPhCt/phAnalyser.py | 1 | 25406 | #! /usr/bin/env python
# -*- coding:utf-8 -*-
##############################################################################
## license : GPLv3+
##============================================================================
##
## File : phAnalyser.py
##
## Project : Filling Pattern from the Photon Counter... | gpl-3.0 |
airanmehr/bio | Scripts/HLI/Kyrgyz/IBD.py | 1 | 3336 | import os
import matplotlib as mpl
import pandas as pd;
import numpy as np;
import seaborn as sns
np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;
from matplotlib.backends.backend_pdf import PdfPages
pd.options.display.max_rows = 50;
pd.options.display.expand_frame_repr = False
i... | mit |
laurensdeprez/RMPCDMD | experiments/01-single-dimer/plot_msd.py | 1 | 1627 | #!/usr/bin/env python
from __future__ import print_function, division
import argparse
description = "Plot the mean square displacement of the dimer's center of mass."
parser = argparse.ArgumentParser(description=description)
parser.add_argument('file', type=str, help='H5MD datafile', nargs='+')
args = parser.parse_ar... | bsd-3-clause |
christopher-gillies/MultiplePhenotypeAssociationBayesianNetwork | tests/test_normal.py | 1 | 2226 | from .context import mpabn
from mpabn import bayesian_network as bn
import numpy as np
from scipy import stats
import pandas as pd
from mpabn import helpers
from scipy.stats import norm
np.random.seed(0)
"""
py.test -s tests/test_normal.py
"""
def test_prob():
node = bn.LinearGaussianNode("X1")
#set intercept... | mit |
chatcannon/scipy | scipy/interpolate/ndgriddata.py | 39 | 7457 | """
Convenience interface to N-D interpolation
.. versionadded:: 0.9
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from .interpnd import LinearNDInterpolator, NDInterpolatorBase, \
CloughTocher2DInterpolator, _ndim_coords_from_arrays
from scipy.spatial import cKDTree
_... | bsd-3-clause |
ChanChiChoi/scikit-learn | sklearn/neural_network/rbm.py | 206 | 12292 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
dblalock/bolt | experiments/python/datasets/caltech.py | 1 | 2804 | #!/bin/env python
# from __future__ import absolute_import, division, print_function
from __future__ import division, print_function
import numpy as np
from . import paths
from . import image_utils as imgs
from joblib import Memory
_memory = Memory('.', verbose=1)
DATADIR_101 = paths.CALTECH_101
DATADIR_256 = pat... | mpl-2.0 |
webmasterraj/GaSiProMo | flask/lib/python2.7/site-packages/pandas/tools/rplot.py | 4 | 29150 | import random
import warnings
from copy import deepcopy
from pandas.core.common import _values_from_object
import numpy as np
from pandas.compat import range, zip
#
# TODO:
# * Make sure legends work properly
#
warnings.warn("\n"
"The rplot trellis plotting interface is deprecated and will be "
... | gpl-2.0 |
gawrysz/piernik | python/interactive_plot_crs.py | 3 | 29896 | #!/usr/bin/python
# -*- coding: utf-8 -*-
from colored_io import die, prtinfo, prtwarn, read_var
from copy import copy
from crs_h5 import crs_initialize, crs_plot_main, crs_plot_main_fpq
from crs_pf import initialize_pf_arrays
from math import isnan, pi
import matplotlib.pyplot as plt
from matplotlib.colors import LogN... | gpl-3.0 |
tapomayukh/projects_in_python | classification/Classification_with_kNN/Single_Contact_Classification/Scaled_Features/best_kNN_PCA/4_categories/test11_cross_validate_categories_1200ms_scaled_method_i.py | 1 | 5041 |
# Principal Component Analysis Code :
from numpy import mean,cov,double,cumsum,dot,linalg,array,rank,size,flipud
from pylab import *
import numpy as np
import matplotlib.pyplot as pp
#from enthought.mayavi import mlab
import scipy.ndimage as ni
import roslib; roslib.load_manifest('sandbox_tapo_darpa_m3')
import ro... | mit |
NunoEdgarGub1/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 |
xyguo/scikit-learn | examples/svm/plot_svm_anova.py | 85 | 2024 | """
=================================================
SVM-Anova: SVM with univariate feature selection
=================================================
This example shows how to perform univariate feature selection before running a
SVC (support vector classifier) to improve the classification scores.
"""
print(__doc_... | bsd-3-clause |
ibukanov/boulder | test/load-generator/latency-charter.py | 3 | 5438 | #!/usr/bin/python
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import datetime
import json
import pandas
import matplotlib
import argparse
import os
matplotlib.style.use('ggplot')
# sacrifical plot for single legend
matplotlib.rcParams['figure.figsize'] = 1, 1
r... | mpl-2.0 |
ndingwall/scikit-learn | examples/model_selection/plot_learning_curve.py | 5 | 7001 | """
========================
Plotting Learning Curves
========================
In the first column, first row the learning curve of a naive Bayes classifier
is shown for the digits dataset. Note that the training score and the
cross-validation score are both not very good at the end. However, the shape
of the curve can... | bsd-3-clause |
sangwook236/general-development-and-testing | sw_dev/python/rnd/test/image_processing/skimage/skimage_thresholding.py | 2 | 6105 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import numpy as np
import skimage
import skimage.filters, skimage.morphology
import matplotlib
import matplotlib.pyplot as plt
# REF [site] >> https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_thresholding.html
def try_all_threshold_example():
img = skima... | gpl-2.0 |
Chaparqanatoos/kaggle-knowledge | src/main/python/BagOfWords.py | 1 | 4030 | #!/usr/bin/env python
# Author: Angela Chapman
# Date: 8/6/2014
#
# This file contains code to accompany the Kaggle tutorial
# "Deep learning goes to the movies". The code in this file
# is for Part 1 of the tutorial on Natural Language Processing.
#
# *************************************** #
import os
from sk... | apache-2.0 |
bousmalis/models | autoencoder/AutoencoderRunner.py | 12 | 1660 | import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from autoencoder_models.Autoencoder import Autoencoder
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
def standard_scale(X_train, X_test):
preprocessor = pr... | apache-2.0 |
chrinide/theanets | examples/recurrent-text.py | 1 | 2017 | #!/usr/bin/env python
import climate
import matplotlib.pyplot as plt
import numpy as np
import theanets
import utils
climate.enable_default_logging()
COLORS = ['#d62728', '#1f77b4', '#2ca02c', '#9467bd', '#ff7f0e',
'#e377c2', '#8c564b', '#bcbd22', '#7f7f7f', '#17becf']
URL = 'http://www.gutenberg.org/cac... | mit |
google/audio-to-tactile | extras/python/phonetics/phone_model.py | 1 | 22911 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
JJGO/Parallel-Computing | 4 Homework 4/3 Testing/2 Analysis OLD/13/time_analysis.py | 1 | 3821 | from matplotlib import pyplot
# from mpltools import style
import prettyplotlib as ppl
# from mpltools import layout
# style.use('ggplot')
# figsize = layout.figaspect(scale=1.2)
ps = [1,2,4,6,8,12,16,24,28,30,31,32,33,34]
# ps = [1,2,4,8]
# ps = range(1,9)
best_paths = {}
best_paths[13] = [0, 9, 1, 8, 7, 2, 3, 4, 1... | gpl-2.0 |
NunoEdgarGub1/scikit-learn | sklearn/cluster/mean_shift_.py | 106 | 14056 | """Mean shift clustering algorithm.
Mean shift clustering aims to discover *blobs* in a smooth density of
samples. It is a centroid based algorithm, which works by updating candidates
for centroids to be the mean of the points within a given region. These
candidates are then filtered in a post-processing stage to elim... | bsd-3-clause |
eroicaleo/MachineLearningUW | course1/week2/quiz2/PredictingHousePrices.py | 1 | 6201 |
# coding: utf-8
# #Fire up graphlab create
# In[35]:
import graphlab
# #Load some house sales data
#
# Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
# In[36]:
sales = graphlab.SFrame('home_data.gl/')
# In[37]:
sales
# #Exploring the data for housing sales... | mit |
bthirion/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 |
fbcotter/dataset_loading | dataset_loading/tensorboard_logging.py | 1 | 2987 | """Simple example on how to log scalars and images to tensorboard without
tensor ops."""
__author__ = "Michael Gygli"
import tensorflow as tf
from io import StringIO
import matplotlib.pyplot as plt
import numpy as np
class Logger(object):
"""Logging in tensorboard without tensorflow ops."""
def __init__(sel... | mit |
ak681443/mana-deep | conv_ae/final_model.py | 2 | 3922 | from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras.callbacks import ModelCheckpoint, EarlyStopping ,LearningRateScheduler
from keras import regularizers
import tensorflow as tf
tf.python.control_flow_ops = tf
import os
from os import listdir
from... | apache-2.0 |
gundramleifert/exp_tf | models/lp_stn/lp_stn_v1.py | 1 | 19610 | '''
Author: Tobi and Gundram
'''
from __future__ import print_function
from itertools import chain
import tensorflow as tf
from util.spatial_transformer import transformer
from tensorflow.python.ops import ctc_ops as ctc
from tensorflow.contrib.layers import batch_norm
from tensorflow.python.ops import rnn_cell
fro... | apache-2.0 |
matthew-tucker/mne-python | mne/viz/tests/test_topomap.py | 5 | 6899 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: Simplified BSD
import os.path as op
import warnings
import numpy as np
from ... | bsd-3-clause |
AOSP-S4-KK/platform_external_chromium_org | chrome/test/nacl_test_injection/buildbot_chrome_nacl_stage.py | 26 | 11131 | #!/usr/bin/python
# 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.
"""Do all the steps required to build and test against nacl."""
import optparse
import os.path
import re
import shutil
import subproc... | bsd-3-clause |
MartinSavc/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 |
Vimos/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
webmasterraj/FogOrNot | flask/lib/python2.7/site-packages/pandas/io/tests/test_json/test_ujson.py | 5 | 53941 | # -*- coding: utf-8 -*-
from unittest import TestCase
try:
import json
except ImportError:
import simplejson as json
import math
import nose
import platform
import sys
import time
import datetime
import calendar
import re
import decimal
from functools import partial
from pandas.compat import range, zip, Strin... | gpl-2.0 |
pfnet/chainercv | chainercv/visualizations/vis_bbox.py | 2 | 5273 | import numpy as np
from chainercv.visualizations.vis_image import vis_image
def vis_bbox(img, bbox, label=None, score=None, label_names=None,
instance_colors=None, alpha=1., linewidth=3.,
sort_by_score=True, ax=None):
"""Visualize bounding boxes inside image.
Example:
>>> ... | mit |
yyjiang/scikit-learn | examples/manifold/plot_manifold_sphere.py | 258 | 5101 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=============================================
Manifold Learning methods on a severed sphere
=============================================
An application of the different :ref:`manifold` techniques
on a spherical data-set. Here one can see the use of
dimensionality reducti... | bsd-3-clause |
cuguilke/Treelogy | Treelogy_Server/Treelogy_Identifier.py | 1 | 5673 | #!/usr/bin/env python
import numpy as np
import pickle
import sys
from time import gmtime, strftime
from sklearn.externals import joblib
#one must change 'cuguilke' to his own username
caffe_root = '/home/cuguilke/caffe/'
svm_root = caffe_root + 'SVM'
sys.path.insert(0, caffe_root + 'python')
import caffe
import os
#C... | gpl-3.0 |
tosolveit/scikit-learn | sklearn/cluster/tests/test_dbscan.py | 176 | 12155 | """
Tests for DBSCAN clustering algorithm
"""
import pickle
import numpy as np
from scipy.spatial import distance
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing im... | bsd-3-clause |
PrashntS/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 |
uhjish/seaborn | seaborn/utils.py | 19 | 15509 | """Small plotting-related utility functions."""
from __future__ import print_function, division
import colorsys
import warnings
import os
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.colors as mplcol
import matplotlib.pyplot as plt
from distutils.version import LooseVersion
pandas_... | bsd-3-clause |
zooniverse/aggregation | experimental/penguins/clusterAnalysis/distance_.py | 2 | 3492 | #!/usr/bin/env python
__author__ = 'greghines'
import numpy as np
import os
import sys
import cPickle as pickle
import math
import matplotlib.pyplot as plt
import pymongo
import urllib
import matplotlib.cbook as cbook
if os.path.exists("/home/ggdhines"):
sys.path.append("/home/ggdhines/PycharmProjects/reduction/ex... | apache-2.0 |
Edu-Glez/Bank_sentiment_analysis | env/lib/python3.6/site-packages/jupyter_core/tests/dotipython_empty/profile_default/ipython_console_config.py | 24 | 21691 | # Configuration file for ipython-console.
c = get_config()
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp configuration
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp will inherit config from: TerminalIP... | apache-2.0 |
timmeinhardt/ProxImaL | proximal/examples/test_noise_est.py | 2 | 1703 | # Proximal
import sys
sys.path.append('../../')
from proximal.utils.utils import *
from proximal.utils.metrics import *
from proximal.lin_ops import *
from proximal.prox_fns import *
import cvxpy as cvx
import numpy as np
from scipy import ndimage
import matplotlib.pyplot as plt
from PIL import Image
import cv2
imp... | mit |
pnedunuri/scikit-learn | examples/linear_model/plot_sgd_weighted_samples.py | 344 | 1458 | """
=====================
SGD: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
# we create 20 points
np.random.seed(0)
X ... | bsd-3-clause |
tridesclous/tridesclous | doc/script_for_figures/generate_peeler_sequence_example.py | 1 | 7284 | """
Find a good example of collision in striatum rat dataset.
"""
import os,shutil
from tridesclous import DataIO, CatalogueConstructor, Peeler
from tridesclous import download_dataset
from tridesclous.cataloguetools import apply_all_catalogue_steps
from tridesclous.peeler import make_prediction_signals
from tridesc... | mit |
MohammedWasim/scikit-learn | sklearn/neighbors/tests/test_ball_tree.py | 159 | 10196 | import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.ball_tree import (BallTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dis... | bsd-3-clause |
jlee335/cells | cells python/source_predator.py | 1 | 8877 | import math
import numpy as np
import random
import pickle
import matplotlib.pyplot as plt
import threading
from matplotlib.pyplot import plot, draw, ion, show
import os
from multiprocessing import Process
maparray = np.zeros((1024,1024,1))
totalscore = 0
fitness = 0
def sigmoid(x):
output = 1 / (1 + np.exp(-x))... | apache-2.0 |
Ambuj-UF/ConCat-1.0 | src/Utils/Bio/Phylo/BaseTree.py | 1 | 45007 | # Copyright (C) 2009 by Eric Talevich (eric.talevich@gmail.com)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""Base classes for Bio.Phylo objects.
All object representations for phylogenetic tree... | gpl-2.0 |
empeeu/numpy | doc/example.py | 81 | 3581 | """This is the docstring for the example.py module. Modules names should
have short, all-lowercase names. The module name may have underscores if
this improves readability.
Every module should have a docstring at the very top of the file. The
module's docstring may extend over multiple lines. If your docstring doe... | bsd-3-clause |
kastman/lyman | conftest.py | 1 | 10615 | import numpy as np
import pandas as pd
import nibabel as nib
import pytest
from moss import Bunch # TODO change to lyman version when implemented
@pytest.fixture()
def execdir(tmpdir):
origdir = tmpdir.chdir()
yield tmpdir
origdir.chdir()
@pytest.fixture()
def lyman_info(tmpdir):
data_dir = tmp... | bsd-3-clause |
tinghuiz/learn-reflectance | 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... | mit |
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