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 |
|---|---|---|---|---|---|
pianomania/scikit-learn | examples/classification/plot_classification_probability.py | 138 | 2871 | """
===============================
Plot classification probability
===============================
Plot the classification probability for different classifiers. We use a 3
class dataset, and we classify it with a Support Vector classifier, L1
and L2 penalized logistic regression with either a One-Vs-Rest or multinom... | bsd-3-clause |
luo66/scikit-learn | examples/applications/plot_stock_market.py | 227 | 8284 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
mrshu/scikit-learn | examples/ensemble/plot_forest_iris.py | 2 | 3013 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
... | bsd-3-clause |
vorasagar7/sp17-i524 | project/S17-IO-3012/code/bin/benchmark_version_find.py | 19 | 4572 | import matplotlib.pyplot as plt
import sys
import pandas as pd
def get_parm():
"""retrieves mandatory parameter to program
@param: none
@type: n/a
"""
try:
return sys.argv[1]
except:
print ('Must enter file name as parameter')
exit()
def read_file(filename):
"""... | apache-2.0 |
duthchao/kaggle-galaxies | try_convnet_cc_multirotflip_3x69r45_normconstraint.py | 7 | 17800 | import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime_augmentation as ra
import time
import csv
import os
import cPickle as pickle
from datetime import datetime, timedelta
# import matplotlib.pyplot as plt
# plt.i... | bsd-3-clause |
lnunno/big-data-uber-viz | nunno/process.py | 1 | 7271 | '''
Requires:
* python3
* pandas - pip3 install pandas
Created on Aug 28, 2014
@author: lnunno
'''
import json
import pandas as pd
import os
data_dir = 'json/'
def filter_uber_df(df, time_of_day=None, day_filters=None, night_begin_hour=17, morning_begin_hour=5):
'''
Filter the uber dataset with the given p... | mit |
anurag313/scikit-learn | sklearn/neural_network/rbm.py | 206 | 12292 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
Adai0808/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 |
FluidityStokes/fluidity | examples/tides_in_the_Mediterranean_Sea/Med-tides-probe.py | 2 | 6073 | #!/usr/bin/env python3
import vtktools
import math
from numpy import array
u=vtktools.vtu("tidesmedsea-flat.vtu")
g = open("Med-GEBCO-5m-gauges-fes2004-O1-102", "w")
pts=vtktools.arr([
#[-5.3500, 36.1333, -2.00],
#[-4.4500, 36.7000, 0.00],
#[-3.9167, 35.2500, 0.00],
#[-2.4500, 36.8333, 0.00],
#[-0.5833, 38.3333, 0.... | lgpl-2.1 |
abhishekgahlot/scikit-learn | sklearn/feature_selection/rfe.py | 10 | 14074 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Vincent Michel <vincent.michel@inria.fr>
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_X_y, safe_sqr
from ..base import ... | bsd-3-clause |
ashhher3/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
smartscheduling/scikit-learn-categorical-tree | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more impor... | bsd-3-clause |
olologin/scikit-learn | sklearn/datasets/__init__.py | 72 | 3807 | """
The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
from .base import load_diabetes
from .base import load_digits
from .base import load_files
from .base import load_iris
from .... | bsd-3-clause |
HIPS/pgmult | pgmult/internals/dirichlet.py | 1 | 4440 | """
From Matt's dirichlet-truncated-multinomial repo.
"""
import numpy as np
na = np.newaxis
import scipy.special
from .simplex import proj_to_2D, mesh
def _dirichlet_support_check(x,alpha):
x = np.array(x,ndmin=2)
alpha = np.array(alpha,ndmin=1)
assert alpha.ndim == 1
if len(alpha) == 1:
a... | mit |
msmbuilder/msmbuilder | msmbuilder/tests/test_agglomerative.py | 6 | 4311 | import numpy as np
from mdtraj.testing import eq
from sklearn.base import clone
from sklearn.metrics import adjusted_rand_score
from msmbuilder.cluster import LandmarkAgglomerative
from msmbuilder.example_datasets import AlanineDipeptide
random = np.random.RandomState(2)
def test_1():
x = [random.randn(10, 2), ... | lgpl-2.1 |
mne-tools/mne-tools.github.io | stable/_downloads/911f82913fdceaf692d2bd4584358dcd/50_decoding.py | 3 | 17267 | r"""
===============
Decoding (MVPA)
===============
.. include:: ../../links.inc
Design philosophy
=================
Decoding (a.k.a. MVPA) in MNE largely follows the machine
learning API of the scikit-learn package.
Each estimator implements ``fit``, ``transform``, ``fit_transform``, and
(optionally) ``inverse_tran... | bsd-3-clause |
AtsushiSakai/PythonRobotics | SLAM/GraphBasedSLAM/graphslam/edge/edge_odometry.py | 1 | 5422 | # Copyright (c) 2020 Jeff Irion and contributors
#
# This file originated from the `graphslam` package:
#
# https://github.com/JeffLIrion/python-graphslam
r"""A class for odometry edges.
"""
import numpy as np
import matplotlib.pyplot as plt
#: The difference that will be used for numerical differentiation
EPSI... | mit |
gengliangwang/spark | python/pyspark/sql/tests/test_pandas_udf_typehints.py | 22 | 9603 | #
# 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 |
rishikksh20/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 28 | 17934 | import numpy as np
import scipy.sparse as sp
import numbers
from scipy import linalg
from sklearn.decomposition import NMF, non_negative_factorization
from sklearn.decomposition import nmf # For testing internals
from scipy.sparse import csc_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.te... | bsd-3-clause |
huzq/scikit-learn | sklearn/metrics/_plot/confusion_matrix.py | 3 | 9459 | from itertools import product
import numpy as np
from .. import confusion_matrix
from ...utils import check_matplotlib_support
from ...utils.validation import _deprecate_positional_args
from ...base import is_classifier
class ConfusionMatrixDisplay:
"""Confusion Matrix visualization.
It is recommend to use... | bsd-3-clause |
yonglehou/scikit-learn | sklearn/cross_validation.py | 96 | 58309 | """
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# 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 |
nixingyang/Kaggle-Face-Verification | Claims Management/fine_tune.py | 1 | 5007 | from sklearn.cross_validation import StratifiedKFold
from xgboost.sklearn import XGBClassifier
import numpy as np
np.random.seed(666)
OBJECTIVE = "binary:logistic"
EVAL_METRIC = "logloss"
SCORING = "log_loss"
GET_BEST_SCORE_INDEX = np.argmin
CV_FOLD_NUM = 5
def evaluate_estimator(estimator, X_train, Y_train, early_s... | mit |
JosmanPS/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 297 | 1247 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
... | bsd-3-clause |
SeldonIO/seldon-server | python/seldon/pipeline/util.py | 2 | 6887 | import seldon.fileutil as fu
import json
import os.path
import logging
import shutil
import unicodecsv
import numpy as np
import pandas as pd
import random
import string
from sklearn.externals import joblib
import logging
from seldon.util import DeprecationHelper
logger = logging.getLogger(__name__)
class PipelineWr... | apache-2.0 |
kleinrap/policyemergence | mesa/batchrunner.py | 2 | 6206 | # -*- coding: utf-8 -*-
"""
Batchrunner
===========
A single class to manage a batch run or parameter sweep of a given model.
"""
from itertools import product
import pandas as pd
from tqdm import tqdm
class BatchRunner:
""" This class is instantiated with a model class, and model parameters
associated with... | gpl-3.0 |
mattilyra/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 37 | 12559 | # 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 |
Reagankm/KnockKnock | venv/lib/python3.4/site-packages/matplotlib/tests/test_text.py | 9 | 12004 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
import numpy as np
from numpy.testing import assert_almost_equal
from nose.tools import eq_
from matplotlib.transforms import Bbox
import matplotlib
import matplotlib.pyplot as plt
... | gpl-2.0 |
Intel-Corporation/tensorflow | tensorflow/contrib/eager/python/examples/rnn_colorbot/rnn_colorbot.py | 9 | 13792 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
coshx/portfolio_optimizer | backend/optimizer/utils.py | 1 | 1412 | """Given ticker symbols and dates, get stock data from Quandl."""
import Quandl
import os
import pandas as pd
def get_data(params):
"""Return a pandas data frame with adjusted close data.
Args:
params (dict): a portfolio of form:
{'symbols': ['AAPL', 'FB', 'GOOG'],
'start_da... | mit |
jayflo/scikit-learn | sklearn/tree/export.py | 75 | 15670 | """
This module defines export functions for decision trees.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Trevor... | bsd-3-clause |
grundgruen/powerline | powerline/data/loader_power.py | 2 | 1488 | import pandas as pd
from math import pow
import numpy as np
from zipline.data.loader import ensure_treasury_data
__author__ = "Warren"
INDEX_MAPPING = {
'^GSPC':
('treasuries', 'treasury_curves.csv', 'data.treasury.gov'),
'^GSPTSE':
('treasuries_can', 'treasury_curves_can.csv', 'bankofcanada.ca'),
... | apache-2.0 |
Midnighter/pyorganism | scripts/trn_multiprocessing.py | 1 | 12113 | #!/usr/bin/env python
# -*- coding: utf-8
from __future__ import (absolute_import, unicode_literals)
import os
import sys
import logging
import argparse
import multiprocessing
import random
from logging.config import dictConfig
from glob import glob
from random import choice
import numpy as np
import networkx as nx... | bsd-3-clause |
Ziqi-Li/bknqgis | pandas/asv_bench/benchmarks/packers.py | 6 | 9228 | from .pandas_vb_common import *
from numpy.random import randint
import pandas as pd
from collections import OrderedDict
from pandas.compat import BytesIO
import sqlite3
import os
from sqlalchemy import create_engine
import numpy as np
from random import randrange
class _Packers(object):
goal_time = 0.2
def _... | gpl-2.0 |
Akshay0724/scikit-learn | examples/linear_model/plot_lasso_lars.py | 363 | 1080 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regulariza... | bsd-3-clause |
vermouthmjl/scikit-learn | examples/svm/plot_svm_scale_c.py | 44 | 5405 | """
==============================================
Scaling the regularization parameter for SVCs
==============================================
The following example illustrates the effect of scaling the
regularization parameter when using :ref:`svm` for
:ref:`classification <svm_classification>`.
For SVC classificati... | bsd-3-clause |
jreback/pandas | pandas/tests/extension/base/missing.py | 4 | 4515 | import numpy as np
import pandas as pd
import pandas._testing as tm
from .base import BaseExtensionTests
class BaseMissingTests(BaseExtensionTests):
def test_isna(self, data_missing):
expected = np.array([True, False])
result = pd.isna(data_missing)
tm.assert_numpy_array_equal(result, e... | bsd-3-clause |
jwkvam/conex | bowtie/_component.py | 1 | 10278 | """Bowtie abstract component classes.
All visual and control components inherit these.
"""
from typing import Any, Callable, Optional, ClassVar, Tuple # pylint: disable=unused-import
from abc import ABCMeta, abstractmethod
import string
from functools import wraps
import json
from datetime import datetime, date, tim... | mit |
NumCosmo/NumCosmo | examples/example_Vexp.py | 1 | 4506 | #!/usr/bin/env python
try:
import gi
gi.require_version('NumCosmo', '1.0')
gi.require_version('NumCosmoMath', '1.0')
except:
pass
import math
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from gi.repository import NumCosmo as Nc
from gi.repository import NumCosmoMath as Ncm
#
# Initia... | gpl-3.0 |
hugobowne/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
wzbozon/scikit-learn | sklearn/utils/tests/test_utils.py | 215 | 8100 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from itertools import chain
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal,
SkipTest, ... | bsd-3-clause |
asoliveira/NumShip | scripts/plot/acel-u-cg-plt.py | 1 | 2317 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#É adimensional?
adi = False
#É para salvar as figuras(True|False)?
save = True
#Caso seja para salvar, qual é o formato desejado?
formato = 'jpg'
#Caso seja para salvar, qual é o diretório que devo salvar?
dircg = 'fig-sen'
#Caso seja para salvar, qual é o nome do arquivo... | gpl-3.0 |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/tools/tests/test_merge_ordered.py | 7 | 3401 | import nose
import pandas as pd
from pandas import DataFrame, merge_ordered
from pandas.util import testing as tm
from pandas.util.testing import assert_frame_equal
from numpy import nan
class TestOrderedMerge(tm.TestCase):
def setUp(self):
self.left = DataFrame({'key': ['a', 'c', 'e'],
... | apache-2.0 |
WesleyAC/toybox | learning/mnist.py | 1 | 1356 | import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.optimizers import Adam
from keras.datasets import mnist
from matplotlib import pyplot as plt
np.random.s... | mit |
Milias/FinancialStuff | Homeworks/hw3/python/plot-data.py | 4 | 3103 | #!/bin/python
# -*- coding: utf-8 -*-
from numpy import *
import matplotlib.pyplot as plt
f_line = lambda x, a, b: a*(1-x)+b*x
def oP_X_plot(name, title, symbol):
Xcall = loadtxt('../data/%s_%s_x.txt' % (name, 'call'), delimiter=',')
Ycall = loadtxt('../data/%s_%s_mean.txt' % (name, 'call'), delimiter=',... | mit |
matthew-tucker/mne-python | mne/preprocessing/tests/test_ica.py | 4 | 23046 | from __future__ import print_function
# Author: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import os
import os.path as op
import warnings
from nose.tools import assert_true, assert_raises, assert_equal
from copy import ... | bsd-3-clause |
ronalcc/zipline | zipline/finance/risk/period.py | 17 | 11952 | #
# Copyright 2013 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 |
annayqho/TheCannon | code/lamost/mass_age/cn/xval.py | 1 | 5415 | import numpy as np
import glob
import matplotlib.pyplot as plt
import sys
import pyfits
from TheCannon import dataset
from TheCannon import model
from TheCannon import lamost
from astropy.table import Table
from matplotlib.colors import LogNorm
from matplotlib import rc
rc('font', family='serif')
rc('text', usetex=True... | mit |
xguse/bokeh | bokeh/util/serialization.py | 31 | 7419 | """ Functions for helping with serialization and deserialization of
Bokeh objects.
"""
from __future__ import absolute_import
from six import iterkeys
is_numpy = None
try:
import numpy as np
is_numpy = True
except ImportError:
is_numpy = False
try:
import pandas as pd
is_pandas = True
except Im... | bsd-3-clause |
KennethPierce/pylearnk | pylearn2/cross_validation/tests/test_cross_validation.py | 49 | 6767 | """
Tests for cross-validation module.
"""
import os
import tempfile
from pylearn2.config import yaml_parse
from pylearn2.testing.skip import skip_if_no_sklearn
def test_train_cv():
"""Test TrainCV class."""
skip_if_no_sklearn()
handle, layer0_filename = tempfile.mkstemp()
handle, layer1_filename = t... | bsd-3-clause |
tacaswell/bokeh | bokeh/charts/_data_adapter.py | 43 | 8802 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the ChartObject class, a minimal prototype class to build more chart
types on top of it. It provides the mechanisms to support the shared chained
methods.
"""
#-------------------------------------------... | bsd-3-clause |
bzamecnik/sms-tools | lectures/05-Sinusoidal-model/plots-code/spectral-peaks-interpolation.py | 1 | 1107 | # matplotlib without any blocking GUI
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from smst.utils import audio, peaks
from smst.models import dft
(fs, x) = audio.read_wav('../../../sounds/oboe-A4.wav')
N = 512 * 2
M = 511
t = -60
w = np.hamming(M)
start = .8 * fs
hN = N... | agpl-3.0 |
aleju/ImageAugmenter | setup.py | 2 | 3821 | # pylint: disable=missing-module-docstring
import re
from pkg_resources import get_distribution, DistributionNotFound
from setuptools import setup, find_packages
long_description = """A library for image augmentation in machine learning experiments, particularly convolutional
neural networks. Supports the augmentatio... | mit |
sinhrks/expandas | pandas_ml/skaccessors/test/test_tree.py | 2 | 2127 | #!/usr/bin/env python
import pytest
import sklearn.datasets as datasets
import sklearn.tree as tree
import pandas_ml as pdml
import pandas_ml.util.testing as tm
class TestTree(tm.TestCase):
def test_objectmapper(self):
df = pdml.ModelFrame([])
self.assertIs(df.tree.DecisionTreeCla... | bsd-3-clause |
sheabrown/faraday_complexity | final/tmp/cutoff_plots.py | 2 | 8108 | from __future__ import print_function
from keras.models import Model
from keras.layers import Activation, Dense, Dropout, Flatten, Input
from keras.layers import concatenate
from keras.layers import Conv1D, MaxPooling1D
from keras.utils import plot_model
from loadData import *
import sys
from keras.models import model_... | mit |
rmatam/Deep-Learning | 01.Python/14.pandas.py | 1 | 1044 | # -*- coding: utf-8 -*-
"""
Created on Mon Mar 27 13:59:06 2017
@author: rmatam
"""
import numpy as np
import pandas as pd
import os
import seaborn as sns
s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
s2=pd.Series(np.random.randn(10),index=['a','ab','abc','abcd','abcde','ax','axb'... | apache-2.0 |
yunque/sms-tools | lectures/05-Sinusoidal-model/plots-code/sine-analysis-synthesis.py | 22 | 1543 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
import sys, os, functools, time
from scipy.fftpack import fft, ifft, fftshift
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import ... | agpl-3.0 |
davidgbe/scikit-learn | sklearn/ensemble/tests/test_forest.py | 5 | 35292 | """
Testing for the forest module (sklearn.ensemble.forest).
"""
# Authors: Gilles Louppe,
# Brian Holt,
# Andreas Mueller,
# Arnaud Joly
# License: BSD 3 clause
import pickle
from collections import defaultdict
from itertools import product
import numpy as np
from scipy.sparse import csr_... | bsd-3-clause |
yipenggao/moose | modules/porous_flow/doc/tests/radialinjection.py | 5 | 4190 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
#
# The two phase radial injection problem has a similarity solution (r^2/t)
#
# Read MOOSE simulation data for constant time (tdata) and constant
# radial distance (rdata)
tdata = np.genfromtxt('../../tests/dirackernels/theis3_line_0016.csv', d... | lgpl-2.1 |
andaag/scikit-learn | sklearn/calibration.py | 137 | 18876 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
from __future__ impo... | bsd-3-clause |
gfyoung/pandas | pandas/tests/groupby/test_nth.py | 3 | 20252 | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, isna
import pandas._testing as tm
def test_first_last_nth(df):
# tests for first / last / nth
grouped = df.groupby("A")
first = grouped.first()
expected = df.loc[[1, 0], ["B", "C",... | bsd-3-clause |
Windy-Ground/scikit-learn | examples/svm/plot_oneclass.py | 249 | 2302 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
... | bsd-3-clause |
nesterione/scikit-learn | examples/model_selection/plot_validation_curve.py | 229 | 1823 | """
==========================
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 |
nomadcube/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 |
Don86/microscopium | tests/test_metrics.py | 1 | 1784 | from microscopium import metrics
import time
import numpy as np
import os
import pandas as pd
from pymongo import MongoClient
import subprocess as sp
abspath = os.path.dirname(__file__)
def string2tuple(string_tuple):
# TODO add docstring
string_values = string_tuple.split(', ')
coords = (int(string_value... | bsd-3-clause |
ehocchen/trading-with-python | lib/vixFutures.py | 79 | 4157 | # -*- coding: utf-8 -*-
"""
set of tools for working with VIX futures
@author: Jev Kuznetsov
Licence: GPL v2
"""
import datetime as dt
from pandas import *
import os
import urllib2
#from csvDatabase import HistDataCsv
m_codes = dict(zip(range(1,13),['F','G','H','J','K','M','N','Q','U','V','X','Z'])) #m... | bsd-3-clause |
nhuntwalker/astroML | book_figures/chapter10/fig_LINEAR_clustering.py | 3 | 9133 | """
Clustering of LINEAR data
-------------------------
Figure 10.20
~~~~~~~~~~~~
Unsupervised clustering analysis of periodic variable stars from the LINEAR
data set. The top row shows clusters derived using two attributes (g - i and
log P) and a mixture of 12 Gaussians. The colorized symbols mark the five most
signif... | bsd-2-clause |
mblondel/scikit-learn | examples/decomposition/plot_ica_vs_pca.py | 43 | 3343 | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | bsd-3-clause |
poryfly/scikit-learn | examples/model_selection/plot_roc.py | 146 | 3697 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
tmills/neural-assertion | scripts/keras/singletask/assertion_predict.py | 1 | 2097 | from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
#from sklearn.datasets import load_svmlight_file
import pickle
import sklearn as sk
import sklearn.cross_validation
import numpy as np
import cleartk_io a... | apache-2.0 |
Aurore54F/MalwareClustering | clustering/utility.py | 1 | 10130 |
"""
Additional functions to cluster/classify JS files, print the predictions, their accuracy…
"""
import os
import logging
import pickle
# import graphviz
# from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
import __init__
def classifier_... | gpl-3.0 |
dansbecker/skflow | examples/multiple_gpu.py | 6 | 1527 | # Copyright 2015-present Scikit Flow 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... | apache-2.0 |
umuzungu/zipline | zipline/data/treasuries_can.py | 15 | 5257 | #
# Copyright 2013 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 |
PyroIgnus/ECE1747-A1-SimMud | tools/parse_log.py | 1 | 3306 | #!/usr/bin/env python
import sys
import numpy as np
import matplotlib.pyplot as plt
# Flag to save graphs automatically or not
SAVE_GRAPHS = False
def moving_average(data, thread_ids, min_time, max_time, window=5):
# Compute moving average
running_average_window = window * 1000
time_splits = np.arange(0... | mit |
eickenberg/scikit-learn | sklearn/utils/tests/test_validation.py | 4 | 7105 | """Tests for input validation functions"""
from tempfile import NamedTemporaryFile
import numpy as np
from numpy.testing import assert_array_equal
import scipy.sparse as sp
from nose.tools import assert_raises, assert_true, assert_false, assert_equal
from itertools import product
from sklearn.utils import as_float_ar... | bsd-3-clause |
clawpack/adjoint | paper2_examples/acoustics_2d_ex3/generate_tolplots.py | 1 | 7318 | from numpy import *
from matplotlib.pyplot import *
from pylab import *
# Setting up local variables
tols = ['1e-0','6e-1','3e-1',
'1e-1','6e-2','3e-2',
'1e-2','6e-3','3e-3',
'1e-3','6e-4','3e-4',
'1e-4','6e-5','3e-5',
'1e-5']
## ---------------------------------
## Setting up ... | bsd-2-clause |
rspavel/spack | var/spack/repos/builtin/packages/py-opppy/package.py | 3 | 1387 | # Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class PyOpppy(PythonPackage):
"""The Output Parse-Plot Python (OPPPY) library is a python based ... | lgpl-2.1 |
lorenzo-desantis/mne-python | mne/decoding/base.py | 6 | 26547 | """Base class copy from sklearn.base"""
# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Romain Trachel <trachelr@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import inspect
import warnings
import six
import numpy as np
class BaseE... | bsd-3-clause |
michaelhuang/QuantSoftwareToolkit | Legacy/Legacy/names.py | 5 | 1114 | import matplotlib.pyplot as plt
from pylab import *
from qstkutil import DataAccess as da
from qstkutil import timeutil as tu
from qstkutil import pseries as ps
import pandas
# Set the list of stocks for us to look at
# symbols= list()
# symtoplot = 'VZ'
# symbols.append(symtoplot)
# symbols.append('IBM')
... | bsd-3-clause |
joequant/zipline | zipline/examples/dual_moving_average.py | 18 | 2054 | #!/usr/bin/env python
#
# 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 ... | apache-2.0 |
kjung/scikit-learn | sklearn/cluster/tests/test_k_means.py | 41 | 27789 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
frank-tancf/scikit-learn | sklearn/cluster/spectral.py | 25 | 18522 | # -*- coding: utf-8 -*-
"""Algorithms for spectral clustering"""
# Author: Gael Varoquaux gael.varoquaux@normalesup.org
# Brian Cheung
# Wei LI <kuantkid@gmail.com>
# License: BSD 3 clause
import warnings
import numpy as np
from ..base import BaseEstimator, ClusterMixin
from ..utils import check_rand... | bsd-3-clause |
hitszxp/scikit-learn | sklearn/metrics/cluster/supervised.py | 17 | 26843 | """Utilities to evaluate the clustering performance of models
Functions named as *_score return a scalar value to maximize: the higher the
better.
"""
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Wei LI <kuantkid@gmail.com>
# Diego Molla <dmolla-aliod@gmail.com>
# License: BSD 3 clause
fr... | bsd-3-clause |
ywcui1990/htmresearch | projects/neural_correlations/EXP5-Bar/NeuCorr_Exp5.py | 10 | 8590 | #!/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 |
arahuja/scikit-learn | examples/manifold/plot_lle_digits.py | 181 | 8510 | """
=============================================================================
Manifold learning on handwritten digits: Locally Linear Embedding, Isomap...
=============================================================================
An illustration of various embeddings on the digits dataset.
The RandomTreesEmbed... | bsd-3-clause |
0asa/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 |
mne-tools/mne-tools.github.io | 0.17/_downloads/afd72e067412c390ceccc64f99255ba2/plot_compute_raw_data_spectrum.py | 11 | 4858 | """
==================================================
Compute the power spectral density of raw data
==================================================
This script shows how to compute the power spectral density (PSD)
of measurements on a raw dataset. It also show the effect of applying SSP
to the data to reduce ECG ... | bsd-3-clause |
padilha/biclustlib | biclustlib/algorithms/las.py | 1 | 7689 | """
biclustlib: A Python library of biclustering algorithms and evaluation measures.
Copyright (C) 2017 Victor Alexandre Padilha
This file is part of biclustlib.
biclustlib is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by... | gpl-3.0 |
lthurlow/Network-Grapher | proj/external/networkx-1.7/doc/make_gallery.py | 12 | 2477 | #!/usr/bin/env python
# generate a thumbnail gallery of examples
template = """\
{%% extends "layout.html" %%}
{%% set title = "Gallery" %%}
{%% block body %%}
<h3>Click on any image to see source code</h3>
<br/>
%s
{%% endblock %%}
"""
link_template = """\
<a href="%s"><img src="%s" border="0" alt="%s"/></a>
"""
... | mit |
dumbringer/ns-3-dev-ndnSIM | src/flow-monitor/examples/wifi-olsr-flowmon.py | 108 | 7439 | # -*- Mode: Python; -*-
# Copyright (c) 2009 INESC Porto
#
# 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,
#... | gpl-2.0 |
gsalvatori/tredify | lib/Pie.py | 1 | 1477 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import random
from mpl_toolkits.mplot3d import Axes3D
from pylab import *
class Pie:
def __init__(self, dict_, title):
figure(1, figsize=(6,6))
self.ax = axes([0.1, 0.1, 0.8, 0.8])
self.dict = dict_
self... | gpl-3.0 |
bikong2/scikit-learn | examples/cluster/plot_ward_structured_vs_unstructured.py | 320 | 3369 | """
===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================
Example builds a swiss roll dataset and runs
hierarchical clustering on their position.
For more information, see :ref:`hierarchical_clus... | bsd-3-clause |
srio/minishadow | minishadow/undulator/source_undulator_input_output.py | 1 | 13362 | __authors__ = ["M Sanchez del Rio - ESRF ISDD Advanced Analysis and Modelling"]
__license__ = "MIT"
__date__ = "12/01/2017"
#
# load/write files and plot facilities for the undul_phot and undul_cdf shadow3/undulator preprocessors
#
import numpy
import h5py
import time
class SourceUndulatorInputOutput(object):
... | mit |
e-koch/pyspeckit | pyspeckit/spectrum/models/n2hp.py | 1 | 6325 | """
===========
N2H+ fitter
===========
Reference for line params:
Daniel, F., Dubernet, M.-L., Meuwly, M., Cernicharo, J., Pagani, L. 2005, MNRAS 363, 1083
http://www.strw.leidenuniv.nl/~moldata/N2H+.html
http://adsabs.harvard.edu/abs/2005MNRAS.363.1083D
Does not yet implement: http://adsabs.harvard.edu/abs/2010ApJ... | mit |
hsiaoyi0504/scikit-learn | sklearn/feature_extraction/hashing.py | 183 | 6155 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
from . import _hashing
from ..base import BaseEstimator, TransformerMixin
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if... | bsd-3-clause |
fyffyt/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 256 | 2406 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
theoryno3/scikit-learn | sklearn/cluster/__init__.py | 364 | 1228 | """
The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
algorithms.
"""
from .spectral import spectral_clustering, SpectralClustering
from .mean_shift_ import (mean_shift, MeanShift,
estimate_bandwidth, get_bin_seeds)
from .affinity_propagation_ import affinity_propagati... | bsd-3-clause |
datacommonsorg/data | scripts/india_udise/udise_school_dropout_rate/preprocess.py | 1 | 3271 | # Copyright 2021 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 writi... | apache-2.0 |
ryscet/pyseries | pyseries/Preprocessing/PrepareData.py | 2 | 23819 | """
PrepareData
===========
Filter signals using PCA decomposition of their fast-fourier transform
#. Identify outliers in fft-transformed signal
#. Identify clusters in fft - i.e. signals with simmilar features
"""
import sys
sys.path.insert(0, '/Users/ryszardcetnarski/Desktop/pySeries')
import numpy as n... | mit |
rbiswas4/simlib | tests/test_simlibCalculation.py | 1 | 4235 | """
Test file for calculating simlibs
This file reads a couple of lines of opsim outputs and produces a simlib file
which is compared for values of skysig and zptavg against a simlib file
produced by a different code.
"""
from __future__ import absolute_import, print_function
import os
import opsimsummary as oss
import... | mit |
FilWisher/distributed-project | icarus/examples/offpath-vs-onpath-caching/plotresults.py | 4 | 14558 | #!/usr/bin/env python
"""Plot results read from a result set
"""
from __future__ import division
import os
import argparse
import collections
import logging
import numpy as np
import matplotlib.pyplot as plt
from icarus.util import Settings, Tree, config_logging, step_cdf
from icarus.tools import means_confidence_int... | mit |
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