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 |
|---|---|---|---|---|---|
smukoehler/SDB-control | SDBmodel.py | 1 | 1900 | import os
import sys
from sklearn import linear_model
import numpy
import collections
class SDBmodel:
def __init__(self):
self.clf = linear_model.Lasso(alpha=0.001 , max_iter=10000)
self.input_data = []
self.state_data = []
def add_data(self, input_vector, state_vector):
self.input_data.append( input_vector... | bsd-2-clause |
jchodera/MSMs | code/sandbox/tica_kde.py | 3 | 1065 | from sklearn.covariance import EllipticEnvelope
import sklearn.neighbors
from sklearn.svm import OneClassSVM
import os
from msmbuilder import example_datasets, cluster, msm, featurizer, lumping, utils, dataset, decomposition
sysname = os.path.split(os.getcwd())[-1]
dt = 0.25
tica_lagtime = 400
regularization_string = ... | gpl-2.0 |
lcharleux/oscillators | oscillators/example_code/linear_oscillator_energy.py | 1 | 1849 | import numpy as np
import matplotlib.pyplot as plt
from scipy import integrate, misc, fftpack, ndimage
from oscillators.oscillators import Oscillator, FindSteadyState
# Inputs
a = .01 # damping / mass
omega0 = 1. # resonance pulsation
omegad = 2. # drive pulsation
def ep_func(x): return .5 * omega0**2 * x**2
def ... | gpl-2.0 |
shyamalschandra/scikit-learn | benchmarks/bench_plot_omp_lars.py | 266 | 4447 | """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 |
trueyao/spark-lever | python/pyspark/sql/context.py | 2 | 25683 | #
# 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 us... | apache-2.0 |
mrcslws/htmresearch | projects/capybara/sandbox/clustering/demo_online_clustering.py | 8 | 5795 | import random
import time
import numpy as np
import scipy
from htmresearch.frameworks.clustering.distances import kernel_dist
from htmresearch.frameworks.clustering.online_agglomerative_clustering \
import OnlineAgglomerativeClustering
from matplotlib import pyplot as plt
from htmresearch.frameworks.capybara.unsupe... | agpl-3.0 |
keiserlab/e3fp-paper | e3fp_paper/plotting/defaults.py | 1 | 1723 | """Defaults used for plotting.
Author: Seth Axen
E-mail: seth.axen@gmail.com
"""
from matplotlib import rc, rcParams
try:
import seaborn as sns
sns.set_style("white")
except ImportError:
pass
rcParams['text.latex.preamble'] = [r'\usepackage{siunitx}',
r'\sisetup{detect-... | lgpl-3.0 |
AmineEch/BrainCNN | predict_categ.py | 1 | 4720 | from __future__ import print_function, division
import matplotlib.pyplot as plt
plt.interactive(False)
from scipy.stats import pearsonr
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import Dense, Dropout, Flatten, Activation
from keras.layers.advanced_activations import L... | mit |
christer155/PTVS | Python/Product/ML/ProjectTemplates/ClusteringTemplate/clustering.py | 18 | 10394 | '''
This script perfoms the basic process for applying a machine learning
algorithm to a dataset using Python libraries.
The four steps are:
1. Download a dataset (using pandas)
2. Process the numeric data (using numpy)
3. Train and evaluate learners (using scikit-learn)
4. Plot and compare results... | apache-2.0 |
bugra/l1 | l1/example/snp500.py | 1 | 2499 | from l1 import l1, strip_outliers
from matplotlib import pyplot as plt
import numpy as np
import os
plt.style.use('fivethirtyeight')
_DATA_DIR = 'data'
_FIG_DIR = 'figures'
_SNP500_FILE_NAME = 'snp500.txt'
_SNP500_FILE_PATH = os.path.join(_DATA_DIR, _SNP500_FILE_NAME)
def get_signal(file_path=_SNP500_FILE_PATH):
... | apache-2.0 |
pratapvardhan/pandas | pandas/_version.py | 5 | 16218 | # This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains th... | bsd-3-clause |
cactusbin/nyt | matplotlib/examples/api/line_with_text.py | 6 | 1629 | """
Show how to override basic methods so an artist can contain another
artist. In this case, the line contains a Text instance to label it.
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.lines as lines
import matplotlib.transforms as mtransforms
import matplotlib.text as mtext
class MyLine... | unlicense |
terrychenism/caffe-windows-cudnn | python/detect.py | 23 | 5743 | #!/usr/bin/env python
"""
detector.py is an out-of-the-box windowed detector
callable from the command line.
By default it configures and runs the Caffe reference ImageNet model.
Note that this model was trained for image classification and not detection,
and finetuning for detection can be expected to improve results... | bsd-2-clause |
IDEALLab/design_method_recommendation_JMD_2014 | rec_utils.py | 1 | 2681 | '''
Some helper functions to assist in the plotting and wrangling of data
'''
import numpy as np
import matplotlib.pylab as plt
from matplotlib.ticker import MaxNLocator,AutoLocator
almost_black = '#262626'
def is_homogeneous(l):
''' Checks to see if a list has all identical entries
'''
for i in rang... | apache-2.0 |
mattgiguere/doglodge | code/drive_bf_us_cities.py | 1 | 3134 | #!/usr/bin/env python
"""
PURPOSE:
To scrape all the bf data for all US cities with more than 100k people.
Created on 2015-09-22T21:29:50
"""
from __future__ import division, print_function
import sys
import datetime
import argparse
import pandas as pd
import splinter_scrape_bf as ssbf
__author__ = "Matt Giguere ... | mit |
YinongLong/scikit-learn | sklearn/semi_supervised/label_propagation.py | 17 | 15941 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
SciTools/iris | docs/iris/gallery_code/meteorology/plot_lagged_ensemble.py | 2 | 5977 | """
Seasonal Ensemble Model Plots
=============================
This example demonstrates the loading of a lagged ensemble dataset from the
GloSea4 model, which is then used to produce two types of plot:
* The first shows the "postage stamp" style image with an array of 14 images,
one for each ensemble member wit... | lgpl-3.0 |
h2oai/h2o-3 | h2o-py/tests/testdir_algos/xgboost/pyunit_xgboost_reweight_tree.py | 2 | 2701 | from __future__ import print_function
import sys
sys.path.insert(1,"../../../")
import h2o
from h2o.estimators.xgboost import H2OXGBoostEstimator
from tests import pyunit_utils
from pandas.testing import assert_frame_equal
import json
import math
def xgboost_reweight_tree():
prostate_frame = h2o.import_file(path=... | apache-2.0 |
david-hoffman/scripts | imreg.py | 1 | 9130 | # -*- coding: utf-8 -*-
# imreg.py
# Copyright (c) 2011-2014, Christoph Gohlke
# Copyright (c) 2011-2014, 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 without
# modification, are ... | apache-2.0 |
Nyker510/scikit-learn | sklearn/utils/random.py | 234 | 10510 | # Author: Hamzeh Alsalhi <ha258@cornell.edu>
#
# License: BSD 3 clause
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ =... | bsd-3-clause |
mehdidc/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 4 | 50656 | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
import warnings
from abc import ABCMeta, abstr... | bsd-3-clause |
DonBeo/scikit-learn | sklearn/feature_selection/tests/test_rfe.py | 2 | 7885 | """
Testing Recursive feature elimination
"""
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_equal, assert_true
from scipy import sparse
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris, make_friedman1... | bsd-3-clause |
xwolf12/scikit-learn | sklearn/utils/__init__.py | 132 | 14185 | """
The :mod:`sklearn.utils` module includes various utilities.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array,
assert_all_finite,
... | bsd-3-clause |
russellclarke82/CV | Pi/NevinsMcTwis.py | 1 | 10509 | #!/usr/bin/python
# PROJECT ON HOLD FOR NOW #
# This needs to be completely objective and work with minimal
# effort and config at best a list or URLs and some search terms.
# Docs: https://www.crummy.com/software/BeautifulSoup/bs4/doc/
# Source 1: http://web.stanford.edu/~zlotnick/TextAsData/Web_Scraping_with_Beaut... | apache-2.0 |
sidgonuts/march-madness | sim_tournament.py | 1 | 5495 | # File: sim_tournament.py
# Author: Siddhartha Nutulapati
#
# Goal: simulate a march madness tournament based solely on the seeding
# of the inital teams, assuming that each team wins with a probability
# proportional to the difference in seeding
# Import required packages
import csv
import random
import numpy as np
i... | mit |
alvations/Sensible-SemEval | paramsearch.py | 2 | 1904 | # -*- coding: utf-8 -*-
from __future__ import print_function
import io
import random
import sys
from itertools import product
try:
import cPickle as pickle
except:
import pickle
from sklearn import cross_validation
from passage.preprocessing import Tokenizer
from passage.layers import Embedding, GatedRecurrent,... | mit |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/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
... | mit |
cogmission/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/artist.py | 69 | 33042 | from __future__ import division
import re, warnings
import matplotlib
import matplotlib.cbook as cbook
from transforms import Bbox, IdentityTransform, TransformedBbox, TransformedPath
from path import Path
## Note, matplotlib artists use the doc strings for set and get
# methods to enable the introspection methods of ... | agpl-3.0 |
victorbergelin/scikit-learn | sklearn/datasets/svmlight_format.py | 114 | 15826 | """This module implements a loader and dumper for the svmlight format
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.
The first element of each line can be used to store a target variable to
predict.
This format is used as the... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/build/lib.linux-i686-2.7/matplotlib/testing/jpl_units/StrConverter.py | 6 | 5174 | #===========================================================================
#
# StrConverter
#
#===========================================================================
"""StrConverter module containing class StrConverter."""
#===========================================================================
# Place al... | mit |
rishita/mxnet | example/kaggle-ndsb1/submission_dsb.py | 15 | 4287 | from __future__ import print_function
import pandas as pd
import os
import time as time
## Receives an array with probabilities for each class (columns) X images in test set (as listed in test.lst) and formats in Kaggle submission format, saves and compresses in submission_path
def gen_sub(predictions,test_lst_path="... | apache-2.0 |
tkarna/cofs | test/firedrake/test_divergence_2d.py | 1 | 5049 | """
Tests convergence of div(uv) in 2D
"""
from firedrake import *
from thetis.utility import get_functionspace
import numpy
from scipy import stats
import os
op2.init(log_level=WARNING)
def compute(refinement=1, order=1, do_export=False):
print('--- soving refinement {:}'.format(refinement))
n = 5*refinemen... | mit |
StevePny/NOAA-GFDL-MOM6-examples | tools/analysis/VerticalSplitScale.py | 2 | 7428 | from __future__ import unicode_literals
import numpy as np
from numpy import ma
from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
from matplotlib.ticker import Formatter, FixedLocator, MaxNLocator, AutoLocator
class VerticalSplitScale(mscale.ScaleBase):
"""
Scales data i... | gpl-3.0 |
datapythonista/pandas | pandas/tests/indexes/categorical/test_append.py | 3 | 2191 | import pytest
from pandas import (
CategoricalIndex,
Index,
)
import pandas._testing as tm
class TestAppend:
@pytest.fixture
def ci(self):
categories = list("cab")
return CategoricalIndex(list("aabbca"), categories=categories, ordered=False)
def test_append(self, ci):
# a... | bsd-3-clause |
egalli/Node-DC-EIS | Node-DC-EIS-client/runspec.py | 1 | 54186 | #!/usr/bin/python
# Copyright (c) 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | apache-2.0 |
pratiknarang/peershark | plotGraphs.py | 2 | 1498 | import numpy
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pylab
import os
import multiprocessing as MP
from P2P_CONSTANTS import *
def plotGraph(x, y, z, filename):
v = [0,5000,0,200]
plt.axis(v)
plt.scatter(x, y, alpha = 0.10, cmap=plt.cm.cool, edgecolors='None')
# plt.colorbar()... | mit |
Tjorriemorrie/trading | 06_randomforests/minmax/predict.py | 1 | 3710 | import logging
import pandas as pd
import numpy as np
from pprint import pprint
from sklearn import cross_validation, externals
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.preprocessing import scale, MinMaxScaler
from progressbar import ProgressBar
currencies = [
'GBPUSD... | mit |
webmasterraj/FogOrNot | flask/lib/python2.7/site-packages/pandas/io/common.py | 4 | 4935 | """Common IO api utilities"""
import sys
import os
import zipfile
from contextlib import contextmanager, closing
from pandas.compat import StringIO, string_types, BytesIO
from pandas import compat
if compat.PY3:
from urllib.request import urlopen, pathname2url
_urlopen = urlopen
from urllib.parse import... | gpl-2.0 |
dinos66/termAnalysis | forTateDataset/clusterEvolutionDetection.py | 1 | 19992 | # -*- coding: utf-8 -*-
'''
Term cluster evolution detection
'''
print('Term cluster evolution detection and term similarity estimation')
#--------------------------------------------
import glob, pickle, pprint, random, re, itertools, time, os
from nltk.corpus import wordnet as wn
from itertools import product
impo... | apache-2.0 |
IPGP/DSM-Kernel | examples/single_kernel/simple_plot_kernel.py | 1 | 2675 | #!/usr/bin/env python
"""
This is a very simple plot script that reads a 3D DSM-Kernel sensitivity kernel
file and make a plot of it.
"""
import matplotlib.pyplot as plt
import numpy as np
def read_fortran_record(binfile, count, dtype):
"""reads a sequential fortran binary file record"""
rec_start = np.fromfi... | gpl-3.0 |
awickert/river-network-evolution | ThreeChannels_generalizing.py | 1 | 17224 | #from __future__ import division
import numpy as np
from scipy.sparse import spdiags, block_diag
from scipy.sparse.linalg import spsolve, isolve
from matplotlib import pyplot as plt
import copy
class rnet(object):
def __init__(self):
pass
def sediment__discharge_per_unit_width(self):
"""
Compute q_s ... | gpl-3.0 |
wanggang3333/scikit-learn | sklearn/ensemble/forest.py | 176 | 62555 | """Forest of trees-based ensemble methods
Those methods include random forests and extremely randomized trees.
The module structure is the following:
- The ``BaseForest`` base class implements a common ``fit`` method for all
the estimators in the module. The ``fit`` method of the base ``Forest``
class calls the ... | bsd-3-clause |
rishikksh20/scikit-learn | examples/missing_values.py | 71 | 3055 | """
======================================================
Imputing missing values before building an estimator
======================================================
This example shows that imputing the missing values can give better results
than discarding the samples containing any missing value.
Imputing does not ... | bsd-3-clause |
schets/scikit-learn | examples/linear_model/plot_sgd_comparison.py | 167 | 1659 | """
==================================
Comparing various online solvers
==================================
An example showing how different online solvers perform
on the hand-written digits dataset.
"""
# Author: Rob Zinkov <rob at zinkov dot com>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot a... | bsd-3-clause |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/pandas/core/config.py | 11 | 22966 | """
The config module holds package-wide configurables and provides
a uniform API for working with them.
Overview
========
This module supports the following requirements:
- options are referenced using keys in dot.notation, e.g. "x.y.option - z".
- keys are case-insensitive.
- functions should accept partial/regex k... | mit |
dgwakeman/mne-python | examples/inverse/plot_lcmv_beamformer_volume.py | 18 | 3046 | """
===================================================================
Compute LCMV inverse solution on evoked data in volume source space
===================================================================
Compute LCMV inverse solution on an auditory evoked dataset in a volume source
space. It stores the solution in... | bsd-3-clause |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/numpy/lib/twodim_base.py | 83 | 26903 | """ Basic functions for manipulating 2d arrays
"""
from __future__ import division, absolute_import, print_function
from numpy.core.numeric import (
asanyarray, arange, zeros, greater_equal, multiply, ones, asarray,
where, int8, int16, int32, int64, empty, promote_types, diagonal,
)
from numpy.core import... | mit |
huobaowangxi/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_hyperparameters.py | 227 | 5170 | """
=================================================
Hyper-parameters of Approximate Nearest Neighbors
=================================================
This example demonstrates the behaviour of the
accuracy of the nearest neighbor queries of Locality Sensitive Hashing
Forest as the number of candidates and the numb... | bsd-3-clause |
simonsfoundation/CaImAn | caiman/source_extraction/cnmf/spatial.py | 1 | 42586 | #!/usr/bin/env python
"""
Created on Wed Aug 05 20:38:27 2015
# -*- coding: utf-8 -*-
@author: agiovann
"""
# noinspection PyCompatibility
from past.builtins import basestring
from past.utils import old_div
from builtins import zip
from builtins import map
from builtins import str
from builtins import range
import ... | gpl-2.0 |
nhejazi/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 108 | 2026 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
Denvi/FlatCAM | tests/other/test_plotg.py | 1 | 1609 | from shapely.geometry import LineString, Polygon
from shapely.ops import cascaded_union, unary_union
from matplotlib.pyplot import plot, subplot, show
from camlib import *
def plotg2(geo, solid_poly=False, color="black", linestyle='solid'):
try:
for sub_geo in geo:
plotg2(sub_geo, solid_poly=... | mit |
tjctw/PythonNote | thinkstat/descriptive.py | 3 | 4308 | """This file contains code used in "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2010 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
import first
import math
import Pmf
import survey
import thinkstats
import matplotlib.pyplot as pyplot
import myplot
def ... | cc0-1.0 |
imaculate/scikit-learn | doc/datasets/mldata_fixture.py | 367 | 1183 | """Fixture module to skip the datasets loading when offline
Mock urllib2 access to mldata.org and create a temporary data folder.
"""
from os import makedirs
from os.path import join
import numpy as np
import tempfile
import shutil
from sklearn import datasets
from sklearn.utils.testing import install_mldata_mock
fr... | bsd-3-clause |
h2oai/h2o-dev | h2o-py/h2o/model/model_base.py | 2 | 60005 | # -*- encoding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import traceback
import warnings
import h2o
from h2o.exceptions import H2OValueError
from h2o.job import H2OJob
from h2o.utils.backward_compatibility import backwards_compatible
from h2o.utils.compat... | apache-2.0 |
microsoft/EconML | econml/dml/dml.py | 1 | 68549 | # Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
from warnings import warn
import numpy as np
from sklearn.base import TransformerMixin, clone
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import (ElasticNetCV, LassoCV, LogisticRegressionCV)
from ... | mit |
snurkabill/pydeeplearn | code/similarity/similarityUtils.py | 3 | 18752 | """ Utils for the similarity experiments. """
__author__ = "Mihaela Rosca"
__contact__ = "mihaela.c.rosca@gmail.com"
from sklearn import cross_validation
import matplotlib.pyplot as plt
import itertools
import sys
# We need this to import other modules
sys.path.append("..")
from read.readfacedatabases import *
DEBU... | bsd-3-clause |
jacksapper/math-thesis | ode.py | 1 | 2110 | # -*- coding: utf-8 -*-
#---IMPORTS---
import numpy as np
import matplotlib.pyplot as plt
#---CONSTANTS---
LBOUND = 0.
UBOUND = 1.
POINTS = 2**7
EPSILON = 10**-9
INITIAL = (0,1)
#Matrix is O(POINTS**2)
#---DERIVED CONSTANTS---
INTERVAL_LENGTH = (UBOUND-LBOUND)/(POINTS-1)
D0 = .5*(np.eye(POINTS-1,POINTS) \
+ np.roll(n... | gpl-3.0 |
rmackay9/rmackay9-ardupilot | Tools/FilterTestTool/FilterTest.py | 30 | 22307 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
""" ArduPilot IMU Filter Test Class
This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
versi... | gpl-3.0 |
openai/baselines | baselines/logger.py | 1 | 14802 | import os
import sys
import shutil
import os.path as osp
import json
import time
import datetime
import tempfile
from collections import defaultdict
from contextlib import contextmanager
DEBUG = 10
INFO = 20
WARN = 30
ERROR = 40
DISABLED = 50
class KVWriter(object):
def writekvs(self, kvs):
raise NotImpl... | mit |
eternallyBaffled/itrade | itrade_wxabout.py | 1 | 8673 | #!/usr/bin/env python
# ============================================================================
# Project Name : iTrade
# Module Name : itrade_wxabout.py
#
# Description: wxPython About box
#
# The Original Code is iTrade code (http://itrade.sourceforge.net).
#
# The Initial Developer of the Original Cod... | gpl-3.0 |
m-kostrzewa/FuzzyCarRisk | gui.py | 1 | 16811 | #!/usr/bin/env python3
"""
author: Kamil Cukrowski, 2016
"""
from tkinter import *
import tkinter
import tkinter.ttk
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
import numpy as ... | mit |
akionakamura/scikit-learn | examples/manifold/plot_swissroll.py | 330 | 1446 | """
===================================
Swiss Roll reduction with LLE
===================================
An illustration of Swiss Roll reduction
with locally linear embedding
"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# License: BSD 3 clause (C) INRIA 2011
print(__doc__)
import matplotlib.pyplot... | bsd-3-clause |
ekumenlabs/terminus | terminus/generators/street_plot_generator.py | 1 | 2486 | """
Copyright (C) 2017 Open Source Robotics Foundation
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... | apache-2.0 |
NicovincX2/Python-3.5 | Analyse (mathématiques)/Analyse numérique/Interpolation numérique/Interpolation spatiale/reg_grid_inter.py | 1 | 1862 | # -*- coding: utf-8 -*-
import os
import matplotlib as mpl
mpl.rcParams["font.family"] = "serif"
mpl.rcParams["font.size"] = "12"
import numpy as np
from numpy import polynomial as P
from scipy import interpolate
import matplotlib.pyplot as plt
from scipy import linalg
x = y = np.linspace(-2, 2, 10)
def f(x, y):
... | gpl-3.0 |
felipebetancur/scipy | doc/source/tutorial/stats/plots/kde_plot3.py | 132 | 1229 | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
np.random.seed(12456)
x1 = np.random.normal(size=200) # random data, normal distribution
xs = np.linspace(x1.min()-1, x1.max()+1, 200)
kde1 = stats.gaussian_kde(x1)
kde2 = stats.gaussian_kde(x1, bw_method='silverman')
fig = plt.figure(figsi... | bsd-3-clause |
nhejazi/scikit-learn | sklearn/mixture/tests/test_gaussian_mixture.py | 27 | 40216 | # Author: Wei Xue <xuewei4d@gmail.com>
# Thierry Guillemot <thierry.guillemot.work@gmail.com>
# License: BSD 3 clauseimport warnings
import sys
import warnings
import numpy as np
from scipy import stats, linalg
from sklearn.covariance import EmpiricalCovariance
from sklearn.datasets.samples_generator import... | bsd-3-clause |
Lawrence-Liu/scikit-learn | sklearn/datasets/mlcomp.py | 289 | 3855 | # Copyright (c) 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""
import os
import numbers
from sklearn.datasets.base import load_files
def _load_document_classification(dataset_path, metadata, set_=None, **kwargs):
if ... | bsd-3-clause |
google/gcnn-survey-paper | utils/link_prediction_utils.py | 1 | 3591 | #Copyright 2018 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, softwa... | apache-2.0 |
winklerand/pandas | pandas/tests/frame/test_alter_axes.py | 2 | 43964 | # -*- coding: utf-8 -*-
from __future__ import print_function
import inspect
import pytest
from datetime import datetime, timedelta
import numpy as np
from pandas.compat import lrange, PY2
from pandas import (DataFrame, Series, Index, MultiIndex,
RangeIndex, date_range, IntervalIndex,
... | bsd-3-clause |
lixun910/pysal | pysal/model/spvcm/custom_plots/svcp.py | 2 | 1686 | import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
def corrplot(m, burn=0, thin=None,
percentiles=[25,50,75], support=np.linspace(.001,1,num=1000),
figure_kw=None, plot_kw=None, kde_kw=None):
if figure_kw is None:
figure_kw = {'figsize':(2.1*8,8), 'sharey':Tr... | bsd-3-clause |
clemkoa/scikit-learn | sklearn/neural_network/tests/test_stochastic_optimizers.py | 146 | 4310 | import numpy as np
from sklearn.neural_network._stochastic_optimizers import (BaseOptimizer,
SGDOptimizer,
AdamOptimizer)
from sklearn.utils.testing import (assert_array_equal, assert_true,
... | bsd-3-clause |
nelango/ViralityAnalysis | model/lib/pandas/io/tests/test_clipboard.py | 13 | 3748 | import numpy as np
from numpy.random import randint
import nose
import pandas as pd
from pandas import DataFrame
from pandas import read_clipboard
from pandas import get_option
from pandas.util import testing as tm
from pandas.util.testing import makeCustomDataframe as mkdf, disabled
try:
import pandas.util.cli... | mit |
brguez/TEIBA | src/python/prepareHistology.py | 1 | 2762 | #!/usr/bin/env python
#coding: utf-8
def header(string):
"""
Display header
"""
timeInfo = time.strftime("%Y-%m-%d %H:%M")
print '\n', timeInfo, "****", string, "****"
def info(string):
"""
Display basic information
"""
timeInfo = time.strftime("%Y-%m-%d %H:%M")
print ... | gpl-3.0 |
molpopgen/fwdpy11 | examples/python_genetic_values/pysnowdrift.py | 1 | 3865 | """
Simulates the dynamics of Figure 1A from
DOI: 10.1126/science.1101456.
Final output is a plot of the phenotypes
over time, based on sampling every 100
generations.
"""
import math
import sys
import attr
import matplotlib.pyplot as plt
import numpy as np
import fwdpy11
@attr.s()
class PySnowdrift(fwdpy11.PyDipl... | gpl-3.0 |
jseabold/scikit-learn | sklearn/utils/setup.py | 296 | 2884 | 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 |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/tests/series/test_validate.py | 7 | 1058 | import pytest
from pandas.core.series import Series
class TestSeriesValidate(object):
"""Tests for error handling related to data types of method arguments."""
s = Series([1, 2, 3, 4, 5])
def test_validate_bool_args(self):
# Tests for error handling related to boolean arguments.
invalid_v... | mit |
oliverlee/antlia | python/antlia/trial.py | 1 | 10206 | # -*- coding: utf-8 -*-
import numpy as np
import scipy.signal
import matplotlib.pyplot as plt
import seaborn as sns
from antlia.filter import fft
from antlia.pattern import ExtremaList, SteerEvent, window
class Trial(object):
def __init__(self, data, period):
self.data = data
self.period... | bsd-2-clause |
WuShichao/computational-physics | 2/2_6/2_6.py | 1 | 2826 | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 13 14:17:40 2016
欧拉法计算无空气阻力和有空气阻力时炮弹的弹道
@author: nightwing
"""
from math import cos,sin,sqrt,pi
import matplotlib.pyplot as plt
g = 9.8 #重力加速度（m/s2）
dt = 0.01 #时间间隔（s）
v0 = 700.0 #初始速度（m/s）
k = 4*10**(-5) #B2/m（m-1）
trajectory1 = [] #此列表存储无空气阻力时的弹... | gpl-3.0 |
emilybache/texttest-runner | src/main/python/lib/default/batch/testoverview.py | 1 | 40966 | # Code to generate HTML report of historical information. This report generated
# either via the -coll flag, or via -s 'batch.GenerateHistoricalReport <batchid>'
import os, plugins, time, HTMLgen, HTMLcolors, cgi, sys, logging, jenkinschanges, locale
from cPickle import Unpickler, UnpicklingError
from ordereddict impo... | mit |
jaredwo/topowx | scripts/step07_update_stn_loc.py | 1 | 2436 | '''
Script to update locations of stations that failed location quality assurance
and had their location manually corrected.
'''
from twx.db import StationDataDb, build_por_mask, LON, LAT, ELEV
from twx.qa import LocQA
from twx.utils import TwxConfig
import numpy as np
import os
import pandas as pd
if __name__ == '__... | gpl-3.0 |
soulmachine/scikit-learn | sklearn/decomposition/tests/test_pca.py | 1 | 11194 | import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_rai... | bsd-3-clause |
tavo91/NER-WNUT17 | main.py | 1 | 5169 | import numpy as np
seed_number = 1337
np.random.seed(seed_number)
from common import utilities as utils
from common import representation as rep
from models import network
from models import crf
from settings import *
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
def ... | mit |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/matplotlib/tri/triplot.py | 21 | 3124 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import numpy as np
from matplotlib.tri.triangulation import Triangulation
def triplot(ax, *args, **kwargs):
"""
Draw a unstructured triangular grid as lines and/or markers.
The triang... | apache-2.0 |
luisera/hmtk | hmtk/plotting/seismicity/max_magnitude/cumulative_moment.py | 2 | 3482 | #!/usr/bin/env/python
# LICENSE
#
# Copyright (c) 2010-2013, GEM Foundation, G. Weatherill, M. Pagani, D. Monelli
#
# The Hazard Modeller's Toolkit (hmtk) is free software: you can redistribute
# it and/or modify it under the terms of the GNU Affero General Public License
# as published by the Free Software Foundation,... | agpl-3.0 |
chugunovyar/factoryForBuild | env/lib/python2.7/site-packages/matplotlib/tri/triplot.py | 21 | 3124 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import numpy as np
from matplotlib.tri.triangulation import Triangulation
def triplot(ax, *args, **kwargs):
"""
Draw a unstructured triangular grid as lines and/or markers.
The triang... | gpl-3.0 |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/pandas/tests/test_nanops.py | 9 | 40509 | # -*- coding: utf-8 -*-
from __future__ import division, print_function
from functools import partial
import warnings
import numpy as np
from pandas import Series
from pandas.core.common import isnull, is_integer_dtype
import pandas.core.nanops as nanops
import pandas.util.testing as tm
use_bn = nanops._USE_BOTTLENE... | apache-2.0 |
bthirion/nistats | examples/04_low_level_functions/plot_hrf.py | 1 | 2356 | """Example of hemodynamic reponse functions.
=========================================
Plot the hemodynamic reponse function (hrf) model in SPM together with
the hrf shape proposed by G.Glover, as well as their time and
dispersion derivatives.
Requires matplotlib.
The hrf is the filter that couples neural responses ... | bsd-3-clause |
arahuja/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 251 | 1895 | #!/usr/bin/env python
"""
========================
Polynomial interpolation
========================
This example demonstrates how to approximate a function with a polynomial of
degree n_degree by using ridge regression. Concretely, from n_samples 1d
points, it suffices to build the Vandermonde matrix, which is n_samp... | bsd-3-clause |
fspaolo/scikit-learn | sklearn/svm/tests/test_sparse.py | 5 | 10546 | import warnings
from nose.tools import assert_raises, assert_true, assert_false
import numpy as np
from scipy import sparse
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from sklearn import datasets, svm, linear_model, base
from sklearn.datasets imp... | bsd-3-clause |
wasserfeder/lomap | examples/ijrr2014_rec_hor/view.py | 1 | 6104 | #! /usr/bin/env python
# Copyright (C) 2012-2015, Alphan Ulusoy (alphan@bu.edu)
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any late... | gpl-2.0 |
Batch21/pywr | examples/two_reservoir_moea.py | 1 | 8981 | """
This example shows the trade-off (pareto frontier) of deficit against cost by altering a reservoir control curve.
Two types of control curve are possible. The first is a monthly control curve containing one value for each
month. The second is a harmonic control curve with cosine terms around a mean. Both Parameter... | gpl-3.0 |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/linear_model/__init__.py | 83 | 3139 | """
The :mod:`sklearn.linear_model` module implements generalized linear models. It
includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-le... | unlicense |
mlskit/astromlskit | GMM/EMC/demo.py | 1 | 1989 |
import csv
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from algorithm import EM
import argparse
def line_plot(data_arrays, xlabel, ylabel, labels, title, f):
"""
Plots a scatter chart.
Parameters
----------
data_arrays: 2d numpy array
Data to be plotted. This a... | gpl-3.0 |
MechCoder/scikit-learn | sklearn/tree/tests/test_tree.py | 17 | 64758 | """
Testing for the tree module (sklearn.tree).
"""
import copy
import pickle
from functools import partial
from itertools import product
import struct
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import s... | bsd-3-clause |
bikong2/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 |
Yangqing/caffe | examples/web_demo/app.py | 10 | 7400 | import os
import time
import cPickle
import datetime
import logging
import flask
import werkzeug
import optparse
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image as PILImage
import cStringIO as StringIO
import urllib
import caffe
import exifutil
REPO_DIRNAME = ... | bsd-2-clause |
gingi99/research_dr | python/Experiment/ExperimentsStats.py | 1 | 17116 | # coding: utf-8
# python 3.5
from itertools import product
from sklearn.metrics import accuracy_score
from multiprocessing import Pool
from multiprocessing import freeze_support
import numpy as np
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath("__file__"))+'/../MLEM2')
#sys.path.append('/Users/ook... | mit |
msracver/Deformable-ConvNets | lib/dataset/pycocotools/coco.py | 5 | 18005 | __author__ = 'tylin'
__version__ = '1.0.1'
# Interface for accessing the Microsoft COCO dataset.
# Microsoft COCO is a large image dataset designed for object detection,
# segmentation, and caption generation. pycocotools is a Python API that
# assists in loading, parsing and visualizing the annotations in COCO.
# Ple... | mit |
lostcontrol/msp_tools | msp_vibration/msp_vibration.py | 1 | 7817 | #!/usr/bin/python
# msp_vibration - vibration analyzer for Cleanflight
# Copyright (C) 2016 - Cyril Jaquier
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or... | gpl-3.0 |
jmschrei/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 |
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