repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
TaxIPP-Life/Til | til/data/archives/Patrimoine/test_matching.py | 2 | 6752 | # -*- coding:utf-8 -*-
'''
Created on 25 juil. 2013
@author: a.eidelman
'''
import pandas as pd
import pdb
import numpy as np
import time
# #TODO: J'aime bien l'idée de regarder sur les valeurs potentielles, de séléctionner la meilleure et de prendre quelqu'un dans cette case.
# # L'avantage c'est qu'au lieu de rega... | gpl-3.0 |
Srisai85/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 234 | 12267 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..ext... | bsd-3-clause |
anntzer/scikit-learn | sklearn/datasets/tests/test_openml.py | 1 | 52574 | """Test the openml loader.
"""
import gzip
import warnings
import json
import os
import re
from io import BytesIO
import numpy as np
import scipy.sparse
import sklearn
import pytest
from sklearn import config_context
from sklearn.datasets import fetch_openml
from sklearn.datasets._openml import (_open_openml_url,
... | bsd-3-clause |
jayflo/scikit-learn | sklearn/svm/tests/test_svm.py | 116 | 31653 | """
Testing for Support Vector Machine module (sklearn.svm)
TODO: remove hard coded numerical results when possible
"""
import numpy as np
import itertools
from numpy.testing import assert_array_equal, assert_array_almost_equal
from numpy.testing import assert_almost_equal
from scipy import sparse
from nose.tools im... | bsd-3-clause |
vigilv/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
Unidata/MetPy | v1.0/_downloads/651190fdc2d21b9d54206699c3284920/surface_declarative.py | 6 | 2177 | # Copyright (c) 2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
=========================================
Surface Analysis using Declarative Syntax
=========================================
The MetPy declarative syntax allows for a simplifie... | bsd-3-clause |
spectralDNS/shenfun | demo/laguerre_dirichlet_poisson1D.py | 1 | 1587 | r"""
Solve Poisson equation in 1D with homogeneous Dirichlet bcs on the domain [0, inf)
\nabla^2 u = f,
The equation to solve for a Laguerre basis is
(\nabla u, \nabla v) = -(f, v)
"""
import os
import sys
from sympy import symbols, sin, exp, lambdify
import numpy as np
from shenfun import inner, grad, Tes... | bsd-2-clause |
hehongliang/tensorflow | tensorflow/contrib/labeled_tensor/python/ops/ops.py | 27 | 46439 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
rishikksh20/scikit-learn | examples/neighbors/plot_classification.py | 58 | 1790 | """
================================
Nearest Neighbors Classification
================================
Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColorm... | bsd-3-clause |
grollins/orts | run.py | 1 | 2840 | import numpy as np
import pandas as pd
from timer import Timer
def bubblesort(x):
bound = len(x)-1
while 1:
t = 0
for j in range(bound):
if x[j] > x[j+1]:
x[j], x[j+1] = x[j+1], x[j]
t = j
if t == 0:
break
bound = t
re... | unlicense |
zorroblue/scikit-learn | sklearn/preprocessing/tests/test_label.py | 10 | 18657 | import numpy as np
from scipy.sparse import issparse
from scipy.sparse import coo_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import dok_matrix
from scipy.sparse import lil_matrix
from sklearn.utils.multiclass import type_of_target
from sklearn.utils.testing impor... | bsd-3-clause |
MohammedWasim/scikit-learn | sklearn/metrics/cluster/tests/test_supervised.py | 206 | 7643 | import numpy as np
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import homogeneity_score
from sklearn.metrics.cluster import completeness_score
from sklearn.metrics.cluster import v_measure_score
from sklearn.metrics.cluster import homogeneity_completeness_v_measure
from sklearn... | bsd-3-clause |
Kleptobismol/scikit-bio | skbio/stats/distance/tests/test_permanova.py | 1 | 8466 | # ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | bsd-3-clause |
pydata/vbench | vbench/db.py | 3 | 5573 | from pandas import DataFrame
from sqlalchemy import Table, Column, MetaData, create_engine, ForeignKey
from sqlalchemy import types as sqltypes
from sqlalchemy import sql
import logging
log = logging.getLogger('vb.db')
class BenchmarkDB(object):
"""
Persist vbench results in a sqlite3 database
"""
d... | mit |
herilalaina/scikit-learn | sklearn/gaussian_process/gpr.py | 9 | 20571 | """Gaussian processes regression. """
# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
from operator import itemgetter
import numpy as np
from scipy.linalg import cholesky, cho_solve, solve_triangular
from scipy.optimize import fmin_l_bfgs_b
from sklearn.base im... | bsd-3-clause |
fmfn/UnbalancedDataset | imblearn/over_sampling/_smote/tests/test_borderline_smote.py | 3 | 1920 | import pytest
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import assert_array_equal
from imblearn.over_sampling import BorderlineSMOTE
@pytest.fixture
def data():
X = np.array(
[
[0.11622591,... | mit |
jcatw/scnn | scnn/data.py | 1 | 6116 | __author__ = 'jatwood'
import numpy as np
import cPickle as cp
import inspect
import os
current_dir = os.path.dirname(os.path.abspath(inspect.stack()[0][1]))
def parse_cora(plot=False):
path = "%s/data/cora/" % (current_dir,)
id2index = {}
label2index = {
'Case_Based': 0,
'Genetic_Algo... | mit |
harsham05/image_space | flann_index/image_match.py | 12 | 4269 | # import the necessary packages
from optparse import OptionParser
from scipy.spatial import distance as dist
import matplotlib.pyplot as plt
import numpy as np
import argparse
import glob
import cv2
import sys
import pickle
###########################
def image_match_histogram( all_files, options ):
histograms = {... | apache-2.0 |
andaag/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 244 | 9986 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
nelango/ViralityAnalysis | model/lib/sklearn/preprocessing/__init__.py | 268 | 1319 | """
The :mod:`sklearn.preprocessing` module includes scaling, centering,
normalization, binarization and imputation methods.
"""
from ._function_transformer import FunctionTransformer
from .data import Binarizer
from .data import KernelCenterer
from .data import MinMaxScaler
from .data import MaxAbsScaler
from .data ... | mit |
GuessWhoSamFoo/pandas | pandas/core/strings.py | 1 | 100753 | # -*- coding: utf-8 -*-
import codecs
import re
import textwrap
import warnings
import numpy as np
import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas.compat as compat
from pandas.compat import zip
from pandas.util._decorators import Appender, deprecate_kwarg
from pandas.core.dtypes.common... | bsd-3-clause |
sekikn/incubator-airflow | tests/providers/salesforce/hooks/test_salesforce.py | 7 | 9659 | #
# 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... | apache-2.0 |
VulpesCorsac/Ire-Polus | Find PID/Find_PID.py | 2 | 5265 | # (c) VulpesCorsac
import matplotlib.pyplot as plt
import numpy
import random
T_in_0 = 30 # Starting internal temperature
T_out_0 = 20 # Starting external temperature
T_needed = 35 # Temperature, we should reach and stabilise
t_delay ... | gpl-2.0 |
aabadie/scikit-learn | sklearn/cross_decomposition/pls_.py | 35 | 30767 | """
The :mod:`sklearn.pls` module implements Partial Least Squares (PLS).
"""
# Author: Edouard Duchesnay <edouard.duchesnay@cea.fr>
# License: BSD 3 clause
from distutils.version import LooseVersion
from sklearn.utils.extmath import svd_flip
from ..base import BaseEstimator, RegressorMixin, TransformerMixin
from ..u... | bsd-3-clause |
jniediek/mne-python | examples/preprocessing/plot_rereference_eeg.py | 9 | 2271 | """
=============================
Re-referencing the EEG signal
=============================
Load raw data and apply some EEG referencing schemes.
"""
# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
fr... | bsd-3-clause |
jzt5132/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | bsd-3-clause |
ggventurini/dualscope123 | dualscope123/main.py | 1 | 40618 | #!/usr/bin/env python
"""
Oscilloscope + spectrum analyser in Python for the NIOS server.
Modified version from the original code by R. Fearick.
Giuseppe Venturini, July 2012-2013
Original copyright notice follows. The same license applies.
------------------------------------------------------------
Copyright (C)... | gpl-3.0 |
eshavlyugin/Preferans | newalgo/tf_train_py2.py | 1 | 14069 | import csv
import argparse
import sklearn
import sklearn.ensemble
import numpy
from sklearn.metrics import accuracy_score
from itertools import count
batch_size = 512
num_steps = 200000
num_hidden1 = 50
num_hidden2 = 1
def prepare_train_data(data, label_names, label_name, feature_set, num_labels, use_regression):
... | gpl-2.0 |
solenoid-bandits/leviosa | controller.py | 1 | 1687 | import numpy as np
from matplotlib import pyplot as plt
from net import Net
class Controller(object):
def __init__(self):
pass
def current(self, pos, target, dt):
# t = time
return (target - pos) * 1.0 # proportional
class PIDController(Controller):
def __init__(self, k_p=1.0, k_i=... | mit |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/sklearn/utils/tests/test_linear_assignment.py | 421 | 1349 | # Author: Brian M. Clapper, G Varoquaux
# License: BSD
import numpy as np
# XXX we should be testing the public API here
from sklearn.utils.linear_assignment_ import _hungarian
def test_hungarian():
matrices = [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
... | mit |
devanshdalal/scikit-learn | sklearn/manifold/tests/test_t_sne.py | 28 | 24487 | import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import scipy.sparse as sp
from sklearn.neighbors import BallTree
from sklearn.utils.testing import assert_less_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from skle... | bsd-3-clause |
pymedusa/Medusa | ext/dateutil/parser/_parser.py | 8 | 58804 | # -*- coding: utf-8 -*-
"""
This module offers a generic date/time string parser which is able to parse
most known formats to represent a date and/or time.
This module attempts to be forgiving with regards to unlikely input formats,
returning a datetime object even for dates which are ambiguous. If an element
of a dat... | gpl-3.0 |
tengerye/orthogonal-denoising-autoencoder | TensorFlow/demo.py | 2 | 2335 | import matplotlib
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import scipy.io
from sklearn.cross_decomposition import CCA
from orthAE import OrthdAE
# generate toy data for multi-view learning from paper "Factorized Latent Spaces with Structured Sparsity"
t = np.arange(-1, 1, 0.02)
x = np.... | apache-2.0 |
zfrenchee/pandas | pandas/tests/indexes/period/test_period.py | 1 | 26492 | import pytest
import numpy as np
import pandas as pd
import pandas.util._test_decorators as td
from pandas.util import testing as tm
from pandas import (PeriodIndex, period_range, notna, DatetimeIndex, NaT,
Index, Period, Int64Index, Series, DataFrame, date_range,
offsets)
fro... | bsd-3-clause |
gweidner/incubator-systemml | scripts/perftest/python/google_docs/update.py | 15 | 4666 | #!/usr/bin/env python3
# -------------------------------------------------------------
#
# 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 ... | apache-2.0 |
rhyolight/nupic.research | htmresearch/support/sp_paper_utils.py | 4 | 12897 | #!/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 ... | gpl-3.0 |
altairpearl/scikit-learn | examples/model_selection/plot_validation_curve.py | 141 | 1931 | """
==========================
Plotting Validation Curves
==========================
In this plot you can see the training scores and validation scores of an SVM
for different values of the kernel parameter gamma. For very low values of
gamma, you can see that both the training score and the validation score are
low. ... | bsd-3-clause |
KAPPS-/vincent | tests/test_vega.py | 9 | 32992 | # -*- coding: utf-8 -*-
'''
Test Vincent.vega
-----------------
'''
from datetime import datetime, timedelta
from itertools import product
import time
import json
from vincent.charts import Line
from vincent.core import (grammar, GrammarClass, GrammarDict, KeyedList,
LoadError, ValidationErr... | mit |
sumspr/scikit-learn | sklearn/neighbors/tests/test_kde.py | 208 | 5556 | 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... | bsd-3-clause |
aditipawde/TimeTable1 | TimeTable1/main_rajeshree.py | 1 | 6024 | import dataAccessSQLAlchemy as da
import pandas as pd
import random
import numpy as np
def isSlotAvailable(req_all, timetable_np, c, r_day, r_slot, r_lecnumber, req_id):
#If slot is of duration 1
SlotsAvailable = 0
for i in range(req_all[req_id, 'eachSlot']): #Fetching how many lectures do we require to sl... | lgpl-3.0 |
hainm/statsmodels | statsmodels/sandbox/examples/example_gam.py | 33 | 2343 | '''original example for checking how far GAM works
Note: uncomment plt.show() to display graphs
'''
example = 2 # 1,2 or 3
import numpy as np
import numpy.random as R
import matplotlib.pyplot as plt
from statsmodels.sandbox.gam import AdditiveModel
from statsmodels.sandbox.gam import Model as GAM #?
from statsmode... | bsd-3-clause |
APMonitor/arduino | 0_Test_Device/Python/test_Second_Order.py | 1 | 5010 | import tclab
import numpy as np
import time
import matplotlib.pyplot as plt
from scipy.optimize import minimize
import random
# Second order model of TCLab
# initial parameter guesses
Kp = 0.2
taus = 50.0
zeta = 1.2
# magnitude of step
M = 80
# overdamped 2nd order step response
def model(y0,t,M,Kp,... | apache-2.0 |
Achuth17/scikit-learn | sklearn/tests/test_base.py | 216 | 7045 | # Author: Gael Varoquaux
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing impo... | bsd-3-clause |
MechCoder/scikit-learn | sklearn/ensemble/__init__.py | 153 | 1382 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification, regression and anomaly detection.
"""
from .base import BaseEnsemble
from .forest import RandomForestClassifier
from .forest import RandomForestRegressor
from .forest import RandomTreesEmbedding
from .forest import ExtraTreesCla... | bsd-3-clause |
henrykironde/scikit-learn | sklearn/mixture/gmm.py | 128 | 31069 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
import warnings
import numpy as... | bsd-3-clause |
brentp/goleft | indexcov/paper/plot-eiee-15.py | 1 | 1375 | from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style('white')
df = pd.read_table('eiee.15.bed.gz', compression='gzip')
print(df.head())
cols = list(df.columns)
cols[0] = "chrom"
cols = [c for c in cols[3:] if c != '15-0022964' and c != '15-0022989']
fig, ax = plt.subplots(1)
... | mit |
MohammedWasim/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 286 | 4353 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation me... | bsd-3-clause |
mmessick/Tax-Calculator | taxcalc/tests/test_decorators.py | 2 | 8784 | import os
import sys
cur_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(cur_path, "../../"))
sys.path.append(os.path.join(cur_path, "../"))
import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas.util.testing import assert_frame_equal
from numba import jit, vector... | mit |
DonBeo/statsmodels | statsmodels/nonparametric/_kernel_base.py | 29 | 18238 | """
Module containing the base object for multivariate kernel density and
regression, plus some utilities.
"""
from statsmodels.compat.python import range, string_types
import copy
import numpy as np
from scipy import optimize
from scipy.stats.mstats import mquantiles
try:
import joblib
has_joblib = True
exce... | bsd-3-clause |
Myasuka/scikit-learn | examples/bicluster/plot_spectral_coclustering.py | 276 | 1736 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` f... | bsd-3-clause |
jimboatarm/workload-automation | wlauto/instrumentation/energy_model/__init__.py | 2 | 42149 | # Copyright 2015 ARM Limited
#
# 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 writin... | apache-2.0 |
exepulveda/swfc | python/plot_cross_stats_bm.py | 1 | 7207 | import sys
import random
import logging
import collections
import math
import sys
from scipy.stats import gaussian_kde
import matplotlib as mpl
#mpl.use('agg')
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats
import pandas as pd
from case_study_bm import setup_case_study_ore, attributes
def c... | gpl-3.0 |
hsuantien/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 225 | 5719 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
themrmax/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 78 | 2702 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
Mohitsharma44/citibike-challenge | citibike_1.py | 2 | 3200 | import numpy as np
from datetime import datetime, timedelta
from sys import argv
import matplotlib.pyplot as plt
class CitiBikeChallenge():
def __init__(self):
pass
def load_file(self, path):
print 'Loading data ... '
self.data = np.genfromtxt(path, dtype=None,
... | mit |
vsjha18/finplots | candlestick.py | 1 | 7937 | """
Created on 05-Apr-2015
@author: vivejha
"""
#from . import log
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc
from finplots.overlays import plot_sma
from... | gpl-3.0 |
gem/oq-engine | openquake/hazardlib/tests/gsim/sgobba_2020_test.py | 1 | 10324 | # -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2020, GEM Foundation
#
# OpenQuake 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, either version 3 of the License, ... | agpl-3.0 |
ephes/scikit-learn | sklearn/grid_search.py | 103 | 36232 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
IndraVikas/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 |
andaag/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 225 | 5719 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
YudinYury/Python_Netology_homework | less_4_2_hw_1_visualization.py | 1 | 3030 | """lesson_4_2_Homework "Data visualization"
"""
import os
import pandas as pd
source_path = 'D:\Python_my\Python_Netology_homework\data_names'
source_dir_path = os.path.normpath(os.path.abspath(source_path))
def download_year_data(year):
source_file = os.path.normpath(os.path.join(source_dir_path, 'yob{}.txt'... | gpl-3.0 |
shusenl/scikit-learn | doc/sphinxext/numpy_ext/docscrape_sphinx.py | 408 | 8061 | import re
import inspect
import textwrap
import pydoc
from .docscrape import NumpyDocString
from .docscrape import FunctionDoc
from .docscrape import ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots... | bsd-3-clause |
alubbock/pysb | pysb/examples/cupsoda/run_michment_cupsoda.py | 5 | 1878 | from pysb.examples.michment import model
from pysb.simulator.cupsoda import run_cupsoda
import numpy as np
import matplotlib.pyplot as plt
import itertools
def run():
# factors to multiply the values of the initial conditions
multipliers = np.linspace(0.8, 1.2, 11)
# 2D array of initial concentrations
... | bsd-2-clause |
erdc/proteus | proteus/mprans/beamFEM.py | 1 | 16450 | from __future__ import division
from builtins import range
from builtins import object
from past.utils import old_div
import numpy as np
#import scipy as sp
#import matplotlib.pyplot as plt
import math
import numpy.linalg as linalg
class FEMTools(object):
def __init__(self,
L=1.0,
... | mit |
thehackerwithin/berkeley | code_examples/numpyVectorization/diffusion.py | 9 | 5427 | ''' Functions for comparing vectorization performance of simplified
diffusion in 1D and 2D.
http://en.wikipedia.org/wiki/Finite_difference_method#Example:_The_heat_equation
'''
import numpy as np
from matplotlib import pyplot as plt
plt.ion()
def diff1d_loop( n_iter=200, rate=.5, n_x=100, plotUpdate=True,
... | bsd-3-clause |
mayblue9/bokeh | examples/charts/file/boxplot.py | 37 | 1117 | from collections import OrderedDict
import pandas as pd
from bokeh.charts import BoxPlot, output_file, show
from bokeh.sampledata.olympics2014 import data
# create a DataFrame with the sample data
df = pd.io.json.json_normalize(data['data'])
# filter by countries with at least one medal and sort
df = df[df['medals.... | bsd-3-clause |
cragis/SimpleSpectrumAnalyzer | SAv9.py | 1 | 9409 | #by Craig Howald 2017 to remain free from restriction
#mods added by Kevin Thomas
from matplotlib.figure import Figure
import numpy as np
import matplotlib.pyplot as plt
import rtlsdr
from matplotlib.mlab import psd
import tkinter as Tk
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import rtlsdr
... | gpl-3.0 |
mutirri/bokeh | bokeh/session.py | 42 | 20253 | ''' The session module provides the Session class, which encapsulates a
connection to a Document that resides on a Bokeh server.
The Session class provides methods for creating, loading and storing
documents and objects, as well as methods for user-authentication. These
are useful when the server is run in multi-user ... | bsd-3-clause |
rayNymous/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/dviread.py | 69 | 29920 | """
An experimental module for reading dvi files output by TeX. Several
limitations make this not (currently) useful as a general-purpose dvi
preprocessor.
Interface::
dvi = Dvi(filename, 72)
for page in dvi: # iterate over pages
w, h, d = page.width, page.height, page.descent
for x,y,font,gl... | agpl-3.0 |
gclenaghan/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 |
mverleg/paxsync_backups | cleanup.py | 2 | 3773 |
from argparse import ArgumentParser
from collections import OrderedDict
from datetime import datetime, timedelta
from os import listdir
from os.path import join
from random import randint
from re import findall
from shutil import rmtree
def find_backups(dir, dry=True):
for name in listdir(dir):
match = findall(r'... | mit |
Just-CJ/SketchRetrieval | python/GFHOG.py | 1 | 3062 | # coding=utf-8
import cv2
import numpy as np
import sklearn
SKETCH = 1
IMAGE = 2
test = np.array([])
class GFHOG:
__gradient = None
__hog = None
def __init__(self):
self.__hog = cv2.HOGDescriptor('hog.xml')
def compute(self, img, t=SKETCH):
if t == SKETCH:
... | mit |
harshaneelhg/scikit-learn | sklearn/utils/graph.py | 289 | 6239 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
CallaJun/hackprince | indico/matplotlib/tests/test_axes.py | 9 | 108913 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange
from nose.tools import assert_equal, assert_raises, assert_false, assert_true
import datetime
import numpy as np
from numpy import ma
import matplotlib
from matplotlib... | lgpl-3.0 |
quantopian/zipline | zipline/data/resample.py | 1 | 26927 | # 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 writ... | apache-2.0 |
devs1991/test_edx_docmode | venv/lib/python2.7/site-packages/sklearn/utils/tests/test_svd.py | 3 | 5384 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD
import numpy as np
from scipy import sparse
from scipy import linalg
from numpy.testing import assert_equal
from numpy.testing import assert_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.extmath import randomized_s... | agpl-3.0 |
Bulochkin/tensorflow_pack | tensorflow/contrib/learn/python/learn/tests/dataframe/arithmetic_transform_test.py | 62 | 2343 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
rajat1994/scikit-learn | sklearn/linear_model/tests/test_coordinate_descent.py | 114 | 25281 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from sys import version_info
import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.utils.testing ... | bsd-3-clause |
ChrisThoung/fsic | examples/godley-lavoie_2007/5_lp1.py | 1 | 8990 | # -*- coding: utf-8 -*-
"""
5_lp1
=====
FSIC implementation of Model *LP1*, a model of long-term bonds, capital gains
and liquidity preference, from Chapter 5 of Godley and Lavoie (2007). Parameter
values come from Zezza (2006).
Godley and Lavoie (2007) analyse Model *LP1* beginning from an initial
stationary state. T... | mit |
rimio/wifi-rc | BehavioralCloning/model.py | 1 | 5118 | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from keras.layers import Lambda, Conv2D, MaxPooling2D, Dropout, Dense, Flatten
from utils import INPUT_SHAPE, batc... | gpl-3.0 |
nhejazi/scikit-learn | examples/linear_model/plot_ols_3d.py | 53 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature... | bsd-3-clause |
jreback/pandas | pandas/tests/io/excel/test_openpyxl.py | 2 | 3933 | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame
import pandas._testing as tm
from pandas.io.excel import ExcelWriter, _OpenpyxlWriter
openpyxl = pytest.importorskip("openpyxl")
pytestmark = pytest.mark.parametrize("ext", [".xlsx"])
def test_to_excel_styleconverter(ext):
from ... | bsd-3-clause |
KellenSunderland/sockeye | setup.py | 1 | 3763 | import sys
import os
import re
import logging
import argparse
import subprocess
from setuptools import setup, find_packages
from contextlib import contextmanager
ROOT = os.path.dirname(__file__)
def get_long_description():
with open(os.path.join(ROOT, 'README.md'), encoding='utf-8') as f:
markdown_txt = ... | apache-2.0 |
mxjl620/scikit-learn | sklearn/utils/extmath.py | 70 | 21951 | """
Extended math utilities.
"""
# Authors: Gael Varoquaux
# Alexandre Gramfort
# Alexandre T. Passos
# Olivier Grisel
# Lars Buitinck
# Stefan van der Walt
# Kyle Kastner
# License: BSD 3 clause
from __future__ import division
from functools import partial
import ... | bsd-3-clause |
pybrain2/pybrain2 | examples/rl/environments/shipsteer/shipbench_sde.py | 26 | 3454 | from __future__ import print_function
#!/usr/bin/env python
#########################################################################
# Reinforcement Learning with SPE on the ShipSteering Environment
#
# Requirements:
# pybrain (tested on rev. 1195, ship env rev. 1202)
# Synopsis:
# shipbenchm.py [<True|False> [lo... | bsd-3-clause |
robintw/scikit-image | doc/examples/plot_threshold_adaptive.py | 22 | 1307 | """
=====================
Adaptive Thresholding
=====================
Thresholding is the simplest way to segment objects from a background. If that
background is relatively uniform, then you can use a global threshold value to
binarize the image by pixel-intensity. If there's large variation in the
background intensi... | bsd-3-clause |
rosswhitfield/mantid | qt/applications/workbench/workbench/plotting/test/test_figureinteraction.py | 3 | 31775 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2019 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
# T... | gpl-3.0 |
jseabold/scikit-learn | examples/gaussian_process/plot_gpr_noisy.py | 104 | 3778 | """
=============================================================
Gaussian process regression (GPR) with noise-level estimation
=============================================================
This example illustrates that GPR with a sum-kernel including a WhiteKernel can
estimate the noise level of data. An illustration... | bsd-3-clause |
JessicaGarson/MovieSentiment | bagginglargerrange.py | 1 | 1537 | import pandas as pd
import numpy as np
from ggplot import *
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import cross_val_score
from sklearn.metrics import auc_score
train = pd.read_csv('/Users/jessicagarson/Downloads/Movi... | unlicense |
loli/semisupervisedforests | sklearn/ensemble/weight_boosting.py | 26 | 40570 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
expfactory/expfactory-docker | expdj/apps/experiments/views.py | 2 | 46466 | import csv
import datetime
import hashlib
import json
import os
import re
import shutil
import uuid
import numpy
import pandas
from django.contrib.auth.decorators import login_required
from django.core.exceptions import PermissionDenied, ValidationError
from django.forms.models import model_to_dict
from django.http im... | mit |
chetan51/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/colorbar.py | 69 | 27260 | '''
Colorbar toolkit with two classes and a function:
:class:`ColorbarBase`
the base class with full colorbar drawing functionality.
It can be used as-is to make a colorbar for a given colormap;
a mappable object (e.g., image) is not needed.
:class:`Colorbar`
the derived class ... | gpl-3.0 |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/scipy/stats/tests/test_morestats.py | 17 | 50896 | # Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.random import RandomState
from numpy.testing import (TestCase, run_module_suite, assert_array_equal,
... | mit |
cainiaocome/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
costypetrisor/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py | 221 | 5517 | """
Testing for the gradient boosting loss functions and initial estimators.
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.utils import check_random_state
from ... | bsd-3-clause |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/matplotlib/testing/decorators.py | 3 | 14097 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import functools
import gc
import os
import sys
import shutil
import warnings
import unittest
import nose
import numpy as np
import matplotlib as mpl
import matplotlib.st... | apache-2.0 |
sampathweb/cs109_twitterapp | app/twitterword_old.py | 1 | 3270 | #-------------------------------------------------------------------------------
# Name: twitter recommender
# Purpose: for cs109 call
#
# Author: bconnaughton
#
# Created: 08/12/2013
# Copyright: (c) bconnaughton 2013
# Licence: <your licence>
#--------------------------------------... | mit |
1iyiwei/pyml | code/ch03/share.py | 2 | 1904 | import numpy as np
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import warnings
def versiontuple(v):
return tuple(map(int, (v.split("."))))
def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02, xlabel='', ylabel='', title=''):
# setup marker generator and... | mit |
cbertinato/pandas | pandas/io/json/json.py | 1 | 33725 | from io import StringIO
from itertools import islice
import os
import numpy as np
import pandas._libs.json as json
from pandas._libs.tslibs import iNaT
from pandas.errors import AbstractMethodError
from pandas.core.dtypes.common import ensure_str, is_period_dtype
from pandas import DataFrame, MultiIndex, Series, is... | bsd-3-clause |
qPCR4vir/orange3 | Orange/tests/test_tree.py | 1 | 3871 | # Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import unittest
import numpy as np
import sklearn.tree as skl_tree
from sklearn.tree._tree import TREE_LEAF
from Orange.data import Table
from Orange.classification import TreeLearner
from Orange.regression import Tree... | bsd-2-clause |
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