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 |
|---|---|---|---|---|---|
AlexanderFabisch/scikit-learn | examples/neural_networks/plot_mnist_filters.py | 57 | 2195 | """
=====================================
Visualization of MLP weights on MNIST
=====================================
Sometimes looking at the learned coefficients of a neural network can provide
insight into the learning behavior. For example if weights look unstructured,
maybe some were not used at all, or if very l... | bsd-3-clause |
lilleswing/deepchem | examples/low_data/tox_rf_one_fold.py | 9 | 2037 | """
Train low-data Tox21 models with random forests. Test last fold only.
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import tempfile
import numpy as np
import deepchem as dc
from datasets import load_tox21_ecfp
from sklearn.ensemble import RandomFo... | mit |
dtaht/ns-3-codel-dev | src/core/examples/sample-rng-plot.py | 188 | 1246 | # -*- Mode:Python; -*-
# /*
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License version 2 as
# * published by the Free Software Foundation
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRA... | gpl-2.0 |
rafiqsaleh/VERCE | verce-hpc-pe/src/pyflex/tests/test_pyflex.py | 2 | 12276 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Pyflex test suite.
Run with pytest.
:copyright:
Lion Krischer (krischer@geophysik.uni-muenchen.de), 2014
:license:
GNU General Public License, Version 3
(http://www.gnu.org/copyleft/gpl.html)
"""
import inspect
import matplotlib as mpl
import matplotlib.py... | mit |
wronk/mne-python | examples/inverse/plot_covariance_whitening_dspm.py | 9 | 7201 | # doc:slow-example
"""
===================================================
Demonstrate impact of whitening on source estimates
===================================================
This example demonstrates the relationship between the noise covariance
estimate and the MNE / dSPM source amplitudes. It computes source es... | bsd-3-clause |
massmutual/scikit-learn | sklearn/tests/test_discriminant_analysis.py | 35 | 11709 | try:
# Python 2 compat
reload
except NameError:
# Regular Python 3+ import
from importlib import reload
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.t... | bsd-3-clause |
adamrvfisher/TechnicalAnalysisLibrary | PriceRelativeSMA.py | 1 | 6467 | # -*- coding: utf-8 -*-
"""
Created on Fri Jan 19 15:03:41 2018
@author: AmatVictoriaCuramIII
"""
#lets check out the drag in TBT, eh?
import numpy as np
import random as rand
import pandas as pd
import time as t
from DatabaseGrabber import DatabaseGrabber
from YahooGrabber import YahooGrabber
iterati... | apache-2.0 |
elsid/kaggle-titanic | predict.py | 1 | 11252 | #!/usr/bin/env python3
# coding: utf-8
import re
import yaml
from argparse import ArgumentParser, FileType
from collections import Counter, Iterable
from functools import reduce
from numpy import unique
from pandas import read_csv, concat
from sklearn.ensemble import AdaBoostClassifier
from sys import stdin, stdout
... | mit |
isb-cgc/ISB-CGC-data-proc | tcga_etl_pipeline/mirna_isoform_matrix/melt_matrix.py | 1 | 3675 | import pandas as pd
import sys
import os
import sqlite3
import json
from cStringIO import StringIO
from bigquery_etl.extract.gcloud_wrapper import GcsConnector
from bigquery_etl.extract.utils import convert_file_to_dataframe
from bigquery_etl.transform.tools import cleanup_dataframe
from bigquery_etl.utils.logging_ma... | apache-2.0 |
ClimbsRocks/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
jdreaver/vispy | vispy/visuals/isocurve.py | 18 | 7809 | # -*- coding: utf-8 -*-
# Copyright (c) 2015, Vispy Development Team.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
from __future__ import division
import numpy as np
from .line import LineVisual
from ..color import ColorArray
from ..color.colormap import _normalize, get_colormap
from ..g... | bsd-3-clause |
EducationalTestingService/rsmtool | tests/test_configuration_parser.py | 1 | 48707 | import json
import logging
import os
import tempfile
import warnings
from io import StringIO
from os import getcwd
from os.path import abspath, dirname, join
from pathlib import Path
from shutil import rmtree
import pandas as pd
from nose.tools import assert_equal, assert_not_equal, eq_, ok_, raises
from numpy.testing... | apache-2.0 |
tks0123456789/ParamTune_experiments | utility.py | 1 | 4756 | from datetime import datetime
import numpy as np
import pandas as pd
from sklearn.cross_validation import StratifiedKFold, StratifiedShuffleSplit
from sklearn.metrics import roc_auc_score, log_loss
from sklearn.datasets import make_classification
from xgboost.sklearn import XGBClassifier
from hyperopt import fmin, tpe,... | mit |
jkeung/yellowbrick | tests/test_text/test_base.py | 2 | 1473 | # tests.test_text.test_base
# Tests for the text visualization base classes
#
# Author: Benjamin Bengfort <bbengfort@districtdatalabs.com>
# Created: Mon Feb 20 06:34:50 2017 -0500
#
# Copyright (C) 2016 District Data Labs
# For license information, see LICENSE.txt
#
# ID: test_base.py [] benjamin@bengfort.com $
""... | apache-2.0 |
Agent007/deepchem | scripts/split_csv.py | 5 | 1651 | """
Splits large CSVs into multiple shards.
"""
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
import argparse
import gzip
import pandas as pd
def parse_args(input_args=None):
parser = argparse.ArgumentParser()
parser.add_argument(
"--csv-file", re... | mit |
wlamond/scikit-learn | sklearn/metrics/cluster/tests/test_unsupervised.py | 66 | 5806 | import numpy as np
import scipy.sparse as sp
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from skl... | bsd-3-clause |
tobias47n9e/innstereo | innstereo/polar_axes.py | 3 | 8727 | #!/usr/bin/python3
"""
This module contains the custom projection for a north-up polar plot.
The NorthPolarAxes-class is a polar projection that has its origin (0 degrees)
at the top and counts clockwise in the positive direction. This makes it
easier and fater to plot azimuth measurements. The class is called from t... | gpl-2.0 |
alphaBenj/zipline | tests/risk/test_risk_cumulative.py | 2 | 4263 | #
# Copyright 2016 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 |
phrb/opentuner | stats_app/stats_app/views/charts.py | 6 | 1669 | import datetime
import django
from django.shortcuts import render
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.dates import DateFormatter
from matplotlib.figure import Figure
import random
from opentuner.utils import stats_matplotlib as stats
def display_graph(request):... | mit |
liyu1990/sklearn | sklearn/manifold/tests/test_locally_linear.py | 232 | 4761 | from itertools import product
from nose.tools import assert_true
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from scipy import linalg
from sklearn import neighbors, manifold
from sklearn.manifold.locally_linear import barycenter_kneighbors_graph
from sklearn.utils.testi... | bsd-3-clause |
davidthaler/arboretum | arboretum/tests/test_mse_splitter.py | 1 | 4011 | import unittest
import numpy as np
from sklearn.tree import DecisionTreeRegressor
from ..datasets import load_mtcars, load_als
from .. mse_splitter import split
from .. import tree_constants as tc
NO_SPLIT = (tc.NO_FEATURE, tc.NO_THR)
X, Y = load_mtcars()
W = np.ones_like(Y)
XTR, YTR, XTE, YTE = load_als()
WTR = np.o... | mit |
elijah513/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 230 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
jreback/pandas | pandas/tests/tools/test_to_time.py | 8 | 2019 | from datetime import time
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import Series
import pandas._testing as tm
from pandas.core.tools.datetimes import to_time as to_time_alias
from pandas.core.tools.times import to_time
class TestToTime:
@td.skip_if_has_locale
d... | bsd-3-clause |
wdurhamh/statsmodels | statsmodels/tsa/base/tsa_model.py | 9 | 10574 | from statsmodels.compat.python import lrange
import statsmodels.base.model as base
from statsmodels.base import data
import statsmodels.base.wrapper as wrap
from statsmodels.tsa.base import datetools
from numpy import arange, asarray
from pandas import Index
from pandas import datetools as pandas_datetools
import datet... | bsd-3-clause |
yuhangc/HRI_planner | scripts/hri/planner_component_tester.py | 1 | 15083 | #!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import rospy
from hri_planner.srv import *
from plotting_utils import add_arrow
def belief_update_client(xh, uh, xr, ur, xh0, xr0, weights, acomm, tcomm, log_path):
rospy.wait_for_service("update_bel... | apache-2.0 |
kaichogami/scikit-learn | sklearn/utils/metaestimators.py | 283 | 2353 | """Utilities for meta-estimators"""
# Author: Joel Nothman
# Andreas Mueller
# Licence: BSD
from operator import attrgetter
from functools import update_wrapper
__all__ = ['if_delegate_has_method']
class _IffHasAttrDescriptor(object):
"""Implements a conditional property using the descriptor protocol.
... | bsd-3-clause |
msmbuilder/msmbuilder | msmbuilder/example_datasets/muller.py | 9 | 4306 | from __future__ import print_function, division, absolute_import
from multiprocessing import cpu_count
from multiprocessing.pool import ThreadPool
import numpy as np
from .base import _NWell
from ._muller import propagate, muller_potential
__all__ = ['load_muller', 'MullerPotential']
#############################... | lgpl-2.1 |
appapantula/scikit-learn | sklearn/manifold/t_sne.py | 106 | 20057 | # Author: Alexander Fabisch -- <afabisch@informatik.uni-bremen.de>
# License: BSD 3 clause (C) 2014
# This is the standard t-SNE implementation. There are faster modifications of
# the algorithm:
# * Barnes-Hut-SNE: reduces the complexity of the gradient computation from
# N^2 to N log N (http://arxiv.org/abs/1301.... | bsd-3-clause |
JimingAndYuqi/secret | src/ReadExternalData.py | 1 | 2986 | import json
import numpy as np
import pandas as pd
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from enhancement.ManipulateAndValidation import *
from enhancement.LoadAndSubmit import *
STOPWORDS = nltk.corpus.stopwords.words('english')
train = pd.read_csv("train_stemmed.csv").fillna('')... | apache-2.0 |
ee-in/python-api | plotly/tools.py | 1 | 107644 | # -*- coding: utf-8 -*-
"""
tools
=====
Functions that USERS will possibly want access to.
"""
from __future__ import absolute_import
import os.path
import warnings
import six
import math
from plotly import utils
from plotly import exceptions
from plotly import session
from plotly.graph_objs import graph_objs
... | mit |
classner/barrista | examples/MNIST/visualize.py | 2 | 3101 | #!/usr/bin/env python
"""Create visualizations."""
# pylint: disable=no-member, invalid-name, wrong-import-position
from __future__ import print_function
import os
import json
import logging
import click
import numpy as np
_LOGGER = logging.getLogger(__name__)
try:
import matplotlib.pyplot as plt
MPL_AVAILABL... | mit |
formath/mxnet | example/ssd/detect/detector.py | 30 | 7112 | # 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 u... | apache-2.0 |
datapythonista/pandas | pandas/tests/indexes/period/test_join.py | 3 | 1790 | import numpy as np
import pytest
from pandas._libs.tslibs import IncompatibleFrequency
from pandas import (
Index,
PeriodIndex,
period_range,
)
import pandas._testing as tm
class TestJoin:
def test_join_outer_indexer(self):
pi = period_range("1/1/2000", "1/20/2000", freq="D")
result... | bsd-3-clause |
smjhnits/Praktikum_TU_D_16-17 | Anfängerpraktikum/Protokolle/V703_Geiger_Müller/auswertung/test.py | 1 | 5089 | import numpy as np
from scipy.stats import sem
from uncertainties import ufloat
import uncertainties.unumpy as unp
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import scipy.constants as const
from uncertainties.unumpy import (nominal_values as noms, std_devs as stds)
Spannung = np.linspace(320,... | mit |
waterponey/scikit-learn | sklearn/model_selection/_validation.py | 2 | 37620 | """
The :mod:`sklearn.model_selection._validation` module includes classes and
functions to validate the model.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from __... | bsd-3-clause |
hugobowne/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
harisbal/pandas | pandas/tests/scalar/timestamp/test_arithmetic.py | 2 | 2852 | # -*- coding: utf-8 -*-
from datetime import datetime, timedelta
import pytest
import numpy as np
import pandas.util.testing as tm
from pandas.compat import long
from pandas.tseries import offsets
from pandas import Timestamp, Timedelta
class TestTimestampArithmetic(object):
def test_overflow_offset(self):
... | bsd-3-clause |
rishikksh20/scikit-learn | examples/model_selection/plot_precision_recall.py | 23 | 6873 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both ... | bsd-3-clause |
jvbalen/cover_id | SHS_data.py | 1 | 4156 | #!/usr/bin/env python
"""I/O methods for the SHS dataset."""
from __future__ import division, print_function
import numpy as np
import os
from pandas import read_csv
# global vars
data_dir = '/Users/Jan/Documents/Work/Data/SHS_julien/'
chroma_dir = os.path.join(data_dir, 'chroma/')
def read_cliques(clique_file='... | mit |
chanderbgoel/pybrain | examples/rl/valuebased/nfq.py | 25 | 1973 | from __future__ import print_function
#!/usr/bin/env python
__author__ = 'Thomas Rueckstiess, ruecksti@in.tum.de'
from pybrain.rl.environments.cartpole import CartPoleEnvironment, DiscreteBalanceTask, CartPoleRenderer
from pybrain.rl.agents import LearningAgent
from pybrain.rl.experiments import EpisodicExperiment
fr... | bsd-3-clause |
UNR-AERIAL/scikit-learn | sklearn/manifold/isomap.py | 229 | 7169 | """Isomap for manifold learning"""
# Author: Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) 2011
import numpy as np
from ..base import BaseEstimator, TransformerMixin
from ..neighbors import NearestNeighbors, kneighbors_graph
from ..utils import check_array
from ..utils.graph import... | bsd-3-clause |
tiagofrepereira2012/bob.measure | bob/measure/plot.py | 1 | 12669 | #!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# Chakka Murali Mohan, Trainee, IDIAP Research Institute, Switzerland.
# Mon 23 May 2011 14:36:14 CEST
"""Methods to plot error analysis figures such as ROC, precision-recall curve, EPC and DET"""
def roc(negatives, positives, npoints=100, CAR=False, **kwargs):
... | bsd-3-clause |
rashidsabbir/extractor | annotation_engine.py | 1 | 14705 | #!/usr/bin/python
'''general approach:
construct a high-dimension space of meaning for the various attributes under consideration
develop algorithms to map an input string to a point in this space
Approaches may include:
Naive text analysis, adding a vector corresponding to each word + nearest neigh... | mit |
dsquareindia/scikit-learn | sklearn/ensemble/partial_dependence.py | 33 | 15265 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals im... | bsd-3-clause |
trungnt13/scikit-learn | examples/tree/plot_iris.py | 271 | 2186 | """
================================================================
Plot the decision surface of a decision tree on the iris dataset
================================================================
Plot the decision surface of a decision tree trained on pairs
of features of the iris dataset.
See :ref:`decision tree ... | bsd-3-clause |
MatthieuBizien/scikit-learn | sklearn/feature_extraction/tests/test_dict_vectorizer.py | 110 | 3768 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
assert_false... | bsd-3-clause |
rubikloud/scikit-learn | examples/mixture/plot_gmm_selection.py | 248 | 3223 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in th... | bsd-3-clause |
gagneurlab/concise | concise/utils/pwm.py | 2 | 10205 | import numpy as np
import copy
from concise.preprocessing.sequence import DNA, _get_vocab_dict
from io import StringIO
import gzip
from concise.utils.plot import seqlogo, seqlogo_fig
import matplotlib.pyplot as plt
DEFAULT_LETTER_TO_INDEX = _get_vocab_dict(DNA)
DEFAULT_INDEX_TO_LETTER = dict((DEFAULT_LETTER_TO_INDEX[... | mit |
rubind/forward_3d | scripts/model.py | 1 | 13841 | from numpy import *
import commands
import pyfits
from scipy.interpolate import RectBivariateSpline, interp1d
import time
import multiprocessing as mp
from matplotlib import use
use("PDF")
import matplotlib.pyplot as plt
import sys
from scipy.optimize import minimize
def save_img(dat, imname, waves = None):
comma... | mit |
Sammers21/math_stat_python | problem1.py | 2 | 1341 | import numpy as np
from scipy.stats import rv_continuous
class customDist(rv_continuous):
""""
Distribution F(x)=1-exp(-exp(0.1x)
"""
#TODO поменять тут на своё распределение
def _cdf(self, x, *args):
return 1 - np.exp(-np.exp(x / 10.))
d = customDist()
#Генерируем выборку из 100 случа... | apache-2.0 |
huzq/scikit-learn | benchmarks/bench_plot_ward.py | 14 | 1277 | """
Benchmark scikit-learn's Ward implement compared to SciPy's
"""
import time
import numpy as np
from scipy.cluster import hierarchy
import matplotlib.pyplot as plt
from sklearn.cluster import AgglomerativeClustering
ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
n_samples = np.logspace(.5, 3, 9)
n... | bsd-3-clause |
krisaju95/NewsArticleClustering | IncrementalClustering/module6_Classifier.py | 1 | 2735 | import pickle
import numpy as np
import pandas as pd
import math
import os
newsPaperName = "NewsPaper A"
path = "C:/Users/hp/Desktop/FINAL YEAR PROJECT/S8/"
words = set()
dataFrame2 = pickle.load( open(os.path.join(path , 'Crawled Articles' , newsPaperName , 'Feature Set','dataFrame2.p'), "rb" ))
dataFrame... | gpl-3.0 |
saketkc/statsmodels | statsmodels/genmod/tests/test_glm.py | 19 | 37824 | """
Test functions for models.GLM
"""
from statsmodels.compat import range
import os
import numpy as np
from numpy.testing import (assert_almost_equal, assert_equal, assert_raises,
assert_allclose, assert_, assert_array_less, dec)
from scipy import stats
import statsmodels.api as sm
from st... | bsd-3-clause |
UnitedThruAction/Data | Tools/FuzzyMatch.py | 1 | 5653 | """A utility for fuzzy-matching people between the NY State Voter File and
other data sources, e.g. Campaign Finance records or call records.
Uses [fuzzywuzzy](https://github.com/seatgeek/fuzzywuzzy) to fuzzy-match
name strings, with a bit of added interpersonal intelligence.
@author n.o.franklin@gmail.com
"""
import... | apache-2.0 |
sinhrks/pyopendata | pyopendata/tests/test_eurostat.py | 1 | 4053 | # pylint: disable-msg=E1101,W0613,W0603
from pyopendata import EurostatStore, EurostatResource
import numpy as np
import pandas as pd
from pandas.compat import range
import pandas.util.testing as tm
class TestEurostatTestSite(tm.TestCase):
def setUp(self):
self.store = EurostatStore()
def test_isv... | bsd-2-clause |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/neighbors/tests/test_kde.py | 80 | 5560 | import numpy as np
from sklearn.utils.testing import (assert_allclose, assert_raises,
assert_equal)
from sklearn.neighbors import KernelDensity, KDTree, NearestNeighbors
from sklearn.neighbors.ball_tree import kernel_norm
from sklearn.pipeline import make_pipeline
from sklearn.dataset... | mit |
nikitasingh981/scikit-learn | sklearn/neighbors/tests/test_lof.py | 34 | 4142 | # Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
from math import sqrt
import numpy as np
from sklearn import neighbors
from numpy.testing import assert_array_equal
from sklearn import metrics
from sklearn.metr... | bsd-3-clause |
pcmagic/stokes_flow | src/StokesFlowMethod_bck.py | 1 | 70276 | # coding=utf-8
import numpy as np
from mpi4py import MPI
from petsc4py import PETSc
from numpy import pi
from src import stokes_flow as sf
from src import geo
from tqdm import tqdm
# from multiprocessing import cpu_count, Pool
# from numba import jit
# import numexpr as ne
# from numpy import linalg as LA
def delta... | mit |
metalpen1984/SciTool_Py | gridtransU.py | 1 | 15622 | #!/usr/bin/python
#Purpose: 1. To convert the grid data of following format to each other:
# .sa .xyz .asc .pfb
# (simple ascii, xyz, ascii grid, parflow binary)
# 2. To simply plot the figure from the single output file.
#ChangeLog: 20150429: Changing the reading method of read pfb. Make ... | lgpl-3.0 |
githubgir/LittleVeniceML | StrategyModule.py | 1 | 47213 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 6 10:47:43 2018
@author: Andreas
"""
import pandas as pd
import dateutil
import datetime
import numpy as np
import xarray as xr
import re
import pickle
import seaborn as sns
from StrategyUtils import *
import matplotlib.pyplot as plt
from IPython.core... | gpl-3.0 |
mizzao/ggplot | ggplot/tests/test_element_text.py | 12 | 1362 | from nose.tools import assert_equal, assert_true
from ggplot.tests import image_comparison, cleanup
from ggplot import *
from numpy import linspace
from pandas import DataFrame
df = DataFrame({"blahblahblah": linspace(999, 1111, 9),
"yadayadayada": linspace(999, 1111, 9)})
simple_gg = ggplot(aes(x="b... | bsd-2-clause |
NehaMadalmatti/ml_lab_ecsc_306 | labwork/lab2/sci-learn/non_linear_regression.py | 120 | 1520 | """
===================================================================
Support Vector Regression (SVR) using linear and non-linear kernels
===================================================================
Toy example of 1D regression using linear, polynomial and RBF kernels.
"""
print(__doc__)
import numpy as np
... | apache-2.0 |
zak-k/cis | cis/plotting/scatter_plot.py | 1 | 5546 | from cis.plotting.generic_plot import Generic_Plot
class Scatter_Plot(Generic_Plot):
def plot(self):
"""
Plots one or many scatter plots
Stores the plot in a list to be used for when adding the legend
"""
from cis.plotting.plot import colors
from cis.exceptions impo... | gpl-3.0 |
luiscruz/udacity_data_analyst | P05/src/tester.py | 1 | 4508 | #!/usr/bin/pickle
""" a basic script for importing student's POI identifier,
and checking the results that they get from it
requires that the algorithm, dataset, and features list
be written to my_classifier.pkl, my_dataset.pkl, and
my_feature_list.pkl, respectively
that process should happen a... | mit |
waterponey/scikit-learn | examples/plot_compare_reduction.py | 19 | 2489 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Selecting dimensionality reduction with Pipeline and GridSearchCV
=================================================================
This example constructs a pipeline that does dimensionality
reduction follo... | bsd-3-clause |
moonbury/pythonanywhere | github/MasteringMLWithScikit-learn/8365OS_05_Codes/ch5.py | 3 | 20416 | ################# Figures 05_01 #################
"""
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report
instances = [
{'plays fetch': True, 'species': 'Dog'},
{'plays ... | gpl-3.0 |
3manuek/scikit-learn | benchmarks/bench_sgd_regression.py | 283 | 5569 | """
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import pylab as pl
import gc
from time import time
from sklearn.linear_model i... | bsd-3-clause |
archangdcc/avalon-extras | farm-manager/status-report/tmplot.py | 3 | 14721 | #!/usr/bin/env python2
from __future__ import print_function
import os
import re
import datetime
import sys
import numpy as np
import matplotlib
matplotlib.use('Agg', warn=False)
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib import gridspec
from statlogging import readlog
def tmplot(ti... | unlicense |
manpen/hypergen | libs/NetworKit/scripts/DynamicBetweennessExperiments_fixed_batch.py | 3 | 4514 | from networkit import *
from networkit.dynamic import *
from networkit.centrality import *
import pandas as pd
import random
def isConnected(G):
cc = properties.ConnectedComponents(G)
cc.run()
return (cc.numberOfComponents() == 1)
def removeAndAddEdges(G, nEdges, tabu=None):
if nEdges > G.numberOfEdges() - tabu.... | gpl-3.0 |
ajdawson/eofs | examples/iris/hgt_example.py | 1 | 1873 | """
Compute and plot the leading EOF of geopotential height on the 500 hPa
pressure surface over the European/Atlantic sector during winter time.
This example uses the metadata-retaining iris interface.
Additional requirements for this example:
* iris (http://scitools.org.uk/iris/)
* matplotlib (http://matpl... | gpl-3.0 |
depet/scikit-learn | examples/svm/plot_iris.py | 5 | 1952 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on the iris dataset. It
will plot the decision surface for four different SVM classifiers.
"""
print(__doc__)
imp... | bsd-3-clause |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/compat/numpy/__init__.py | 7 | 2205 | """ support numpy compatiblitiy across versions """
import re
import numpy as np
from distutils.version import LooseVersion
from pandas.compat import string_types, string_and_binary_types
# numpy versioning
_np_version = np.version.short_version
_nlv = LooseVersion(_np_version)
_np_version_under1p8 = _nlv < '1.8'
_n... | apache-2.0 |
rajat1994/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | 202 | 3757 | import scipy.sparse as sp
import numpy as np
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.testing import assert_raises_regex, assert_true
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.... | bsd-3-clause |
stkubr/zipline | zipline/utils/data_source_tables_gen.py | 40 | 7380 | #
# Copyright 2014 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 |
iamshang1/Projects | Basic_ML/Recommendation_System/nearest_neighbors.py | 1 | 4332 | import numpy as np
import pandas as pd
from sklearn import cross_validation
from scipy import sparse
from scipy.spatial import distance
#load data
print "loading data"
ratings = pd.read_csv('u.data', sep = "\t", names = ['uid','mid','rating','timestamp'], engine='python')
ratings.iloc[0,0] = 1
ratings['uid'] = pd.to_n... | mit |
madphysicist/numpy | numpy/fft/_pocketfft.py | 2 | 52860 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1, norm="backward")
ifft(a, n=None, axis=-1, norm="backward")
rfft(a, n=None, axis=-1, norm="backward")
irfft(a, n=None, axis=-1, norm="backward")
hfft(a, n=None, axis=-1, norm="backward")
ihfft(a, n=None, axis=-1, norm="backward")
fftn(a, ... | bsd-3-clause |
appapantula/pattern_classification | machine_learning/webapp/movieclassifier/app.py | 17 | 3048 | from flask import Flask, render_template, request
from wtforms import Form, TextAreaField, validators
import pickle
import sqlite3
import re
import os
import numpy as np
app = Flask(__name__)
######## Preparing the Classifier
import re
from sklearn.feature_extraction.text import HashingVectorizer
cur_dir = os.path.... | gpl-3.0 |
kyleabeauchamp/HMCNotes | code/optimize/test_optimize_hyper_ghmc_respa.py | 1 | 2379 | from hyperopt import fmin, tpe, hp
import lb_loader
import pandas as pd
import simtk.openmm.app as app
import numpy as np
import simtk.openmm as mm
from simtk import unit as u
from openmmtools import hmc_integrators, testsystems
pd.set_option('display.width', 1000)
n_steps = 1000
platform_name = "CUDA"
precision = "mi... | gpl-2.0 |
shenzebang/scikit-learn | sklearn/tree/tests/test_export.py | 130 | 9950 | """
Testing for export functions of decision trees (sklearn.tree.export).
"""
from re import finditer
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingClassifier
from sklearn... | bsd-3-clause |
hrjn/scikit-learn | examples/linear_model/plot_ransac.py | 103 | 1797 | """
===========================================
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 |
prashanthpai/swift | swift/common/middleware/xprofile.py | 8 | 9623 | # Copyright (c) 2010-2012 OpenStack, 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to ... | apache-2.0 |
zehpunktbarron/iOSMAnalyzer | scripts/c3_car_routing_high_net.py | 1 | 7565 | # -*- coding: utf-8 -*-
#!/usr/bin/python2.7
#description :This file creates a plot: Calculates the development of the OSM road network length [km] by street-category
#author :Christopher Barron @ http://giscience.uni-hd.de/
#date :19.01.2013
#version :0.1
#usage :python pyscr... | gpl-3.0 |
bartosh/zipline | tests/pipeline/test_slice.py | 6 | 20007 | """
Tests for slicing pipeline terms.
"""
from numpy import where
from pandas import Int64Index, Timestamp
from pandas.util.testing import assert_frame_equal
from zipline.assets import Asset
from zipline.errors import (
NonExistentAssetInTimeFrame,
NonSliceableTerm,
NonWindowSafeInput,
UnsupportedPipel... | apache-2.0 |
florian-f/sklearn | sklearn/tests/test_cross_validation.py | 1 | 21366 | """Test the cross_validation module"""
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn... | bsd-3-clause |
alexis-jacq/Story_CoWriting | tools/log_analysis/pred.py | 1 | 1487 | import pandas as pd
import seaborn as sns
import numpy as np
import os
from scipy.stats import ttest_ind,ttest_rel
p = "LOG_P_CSV"
s = "LOG_S_CSV"
r = "LOG_R_CSV"
file_subject = "interface-12-stdout.log"
file_robot = "main_activity-13-stdout.log"
counters_p = []
counters_s = []
counters_r = []
def counts(condition... | isc |
JeanKossaifi/scikit-learn | examples/cluster/plot_digits_agglomeration.py | 377 | 1694 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Feature agglomeration
=========================================================
These images how similar features are merged together using
feature agglomeration.
"""
print(__doc__)
# Code source: Gaël Varoquaux
#... | bsd-3-clause |
aflaxman/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 168 | 2088 | #!/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 |
zorroblue/scikit-learn | benchmarks/bench_plot_lasso_path.py | 84 | 4005 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
vighneshbirodkar/scikit-image | doc/examples/features_detection/plot_shape_index.py | 5 | 4382 | """
===========
Shape Index
===========
The shape index is a single valued measure of local curvature,
derived from the eigen values of the Hessian,
defined by Koenderink & van Doorn [1]_.
It can be used to find structures based on their apparent local shape.
The shape index maps to values from -1 to 1,
representing... | bsd-3-clause |
dingocuster/scikit-learn | sklearn/ensemble/tests/test_voting_classifier.py | 140 | 6926 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestCl... | bsd-3-clause |
nkmk/python-snippets | notebook/sklearn_train_test_split_iris.py | 1 | 1326 | from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
data = load_iris()
X = data['data']
y = data['target']
print(X.shape)
# (150, 4)
print(X[:5])
# [[5.1 3.5 1.4 0.2]
# [4.9 3. 1.4 0.2]
# [4.7 3.2 1.3 0.2]
# [4.6 3.1 1.5 0.2]
# [5. 3.6 1.4 0.2]]
print(y.shape)
# (150,)... | mit |
mojoboss/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
zihua/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 244 | 6011 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neigh... | bsd-3-clause |
kshedstrom/pyroms | pyroms_toolbox/pyroms_toolbox/twoDview.py | 2 | 7579 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from mpl_toolkits.basemap import Basemap
import pyroms
import pyroms_toolbox
def twoDview(var, tindex, grid, filename=None, \
cmin=None, cmax=None, clev=None, fill=False, \
contour=False, d=4, range=None, fts=Non... | bsd-3-clause |
viswimmer1/PythonGenerator | data/python_files/30585325/dot.py | 1 | 1176 | import os, sys
up_path = os.path.abspath('..')
sys.path.append(up_path)
from numpy import *
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib import rc
from objects import SimObject
from utils import scalar
from covar import draw_ellipsoid, vec2cov, cov2vec,\
pr... | gpl-2.0 |
ywcui1990/nupic.research | htmresearch/support/sp_paper_utils.py | 6 | 11432 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | agpl-3.0 |
mreineck/healpy | doc/create_images.py | 7 | 1087 | import healpy as hp
import numpy as np
import matplotlib.pyplot as plt
SIZE = 400
DPI = 60
m = np.arange(hp.nside2npix(32))
hp.mollview(m, nest = True, xsize=SIZE, title='Mollview image NESTED')
plt.savefig('static/moll_nside32_nest.png', dpi=DPI)
hp.mollview(m, nest = False, xsize=SIZE, title='Mollview image RING')... | gpl-2.0 |
Lynn-015/NJU_DMRG | homework/Homework_01.py | 1 | 1112 | #!/usr/bin/env python
"""
Tight-binding chain
e0:on-site energy
t:hopping t
N:chain length N.
"""
import numpy as np
import matplotlib.pyplot as plt
def band_energy(k,t=1.0,e0=0.2,a=1.0):
"""The function of energy with respect to k."""
return e0-t*np.exp(1j*k*a)-t*np.exp(-1j*k*a)
def band_plot(N=400,a=1.0):
... | mit |
ZENGXH/scikit-learn | examples/applications/plot_tomography_l1_reconstruction.py | 204 | 5442 | """
======================================================================
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
======================================================================
This example shows the reconstruction of an image from a set of parallel
projections, acquired along dif... | bsd-3-clause |
pratapvardhan/pandas | pandas/tests/indexes/multi/test_equivalence.py | 2 | 7275 | # -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import Index, MultiIndex, RangeIndex, Series, compat
from pandas.compat import lrange, lzip, range
def test_equals(idx):
# TODO: Remove or Refactor. MultiIndex not tested.
for name, idx in compat.iter... | bsd-3-clause |
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