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 |
|---|---|---|---|---|---|
open-risk/concentration_library | examples/python/confidence_interval_example.py | 1 | 1598 | # encoding: utf-8
# (c) 2016-2020 Open Risk, all rights reserved
#
# ConcentrationMetrics is licensed under the MIT license a copy of which is included
# in the source distribution of concentrationMetrics. This is notwithstanding any licenses of
# third-party software included in this distribution. You may not use thi... | mit |
kchodorow/tensorflow | tensorflow/examples/learn/iris.py | 19 | 1651 | # 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 appl... | apache-2.0 |
dcwangmit01/options-screener | app/datareader.py | 1 | 7351 | import os
import time
import sys
import re
import json
import pandas as pd
from selenium import webdriver
import selenium.webdriver.chrome.service as service
from selenium.webdriver.support.ui import Select
from pandas_datareader.data import Options
import requests_cache
#############################################... | mit |
nkhuyu/data-science-from-scratch | code/visualizing_data.py | 58 | 5116 | import matplotlib.pyplot as plt
from collections import Counter
def make_chart_simple_line_chart(plt):
years = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
gdp = [300.2, 543.3, 1075.9, 2862.5, 5979.6, 10289.7, 14958.3]
# create a line chart, years on x-axis, gdp on y-axis
plt.plot(years, gdp, color='gr... | unlicense |
allinpaybusiness/ACS | TLSW_pred/fyzpred02/fyz_pred_02.py | 1 | 4127 | # -*- coding: utf-8 -*-
"""
Spyder Editor
生产模型：creditscore_TLSW_fyz.creditscore_randomforest
"""
import sys
import time
import pandas as pd
import numpy as np
from sklearn.externals import joblib
"""
parameters = {'idCard':'530125198606102726', 'mobileNum':'13619662783', 'education':'高中', 'maritalStatus':'22',
... | apache-2.0 |
shusenl/scikit-learn | sklearn/svm/tests/test_svm.py | 70 | 31674 | """
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 |
vighneshbirodkar/scikit-image | skimage/filters/_gabor.py | 23 | 6926 | import numpy as np
from scipy import ndimage as ndi
from .._shared.utils import assert_nD
__all__ = ['gabor_kernel', 'gabor']
def _sigma_prefactor(bandwidth):
b = bandwidth
# See http://www.cs.rug.nl/~imaging/simplecell.html
return 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * \
(2.0 ** b + 1) / (2.0... | bsd-3-clause |
cbertinato/pandas | pandas/tests/test_optional_dependency.py | 1 | 1460 | import sys
import types
import pytest
from pandas.compat._optional import VERSIONS, import_optional_dependency
import pandas.util.testing as tm
def test_import_optional():
match = "Missing .*notapackage.* pip .* conda .* notapackage"
with pytest.raises(ImportError, match=match):
import_optional_dep... | bsd-3-clause |
jvbalen/catchy | base_features.py | 1 | 5090 | from __future__ import division, print_function
import os
import numpy as np
import pandas as pd
import sys
import librosa
import vamp
import utils
""" This module provides an interface to several existing audio feature time
series extractors.
Requires Librosa to be installed, and optional Vamp plug-ins.
"... | mit |
hammerlab/immuno | immuno/mhc_random.py | 1 | 1775 | # Copyright (c) 2014. Mount Sinai School of Medicine
#
# 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... | apache-2.0 |
RayMick/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 |
talonchandler/dipsim | paper/figures/triple-arm.py | 1 | 6766 | from dipsim import multiframe, util
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import matplotlib.patches as patches
import os; import time; start = time.time(); print('Running...')
import matplotlib.gridspec as gridspec
# Main input parameters
col_labels = ['Geometry\n(NA${}_{\\textrm{upper}}... | mit |
alexsavio/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 6 | 35051 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# License: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | bsd-3-clause |
WangWenjun559/Weiss | summary/sumy/sklearn/feature_selection/__init__.py | 244 | 1088 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | apache-2.0 |
hjweide/cifar-10-uncertainty | plot.py | 1 | 2213 | #!/usr/bin/env python
import cPickle as pickle
import cv2
import matplotlib.pyplot as plt
import numpy as np
from collections import defaultdict
from mpl_toolkits.axes_grid1 import ImageGrid
from os import listdir
from os.path import join, splitext
def make_image_grid(img_list, probs_list, name):
max_per_row = ... | mit |
d-lee/airflow | airflow/hooks/base_hook.py | 18 | 2571 | # -*- coding: utf-8 -*-
#
# 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 writing, software
... | apache-2.0 |
musically-ut/statsmodels | statsmodels/regression/tests/test_regression.py | 18 | 38246 | """
Test functions for models.regression
"""
# TODO: Test for LM
from statsmodels.compat.python import long, lrange
import warnings
import pandas
import numpy as np
from numpy.testing import (assert_almost_equal, assert_approx_equal, assert_,
assert_raises, assert_equal, assert_allclose)
fro... | bsd-3-clause |
blab/antibody-response-pulse | bcell-array/code/Virus_Memory_Naive_Antibody_model.py | 1 | 14663 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <markdowncell>
# # Antibody Response Pulse
# https://github.com/blab/antibody-response-pulse
#
# ### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination
# ### Adaptive immune response for repeated infection
# <cod... | gpl-2.0 |
thientu/scikit-learn | examples/calibration/plot_compare_calibration.py | 241 | 5008 | """
========================================
Comparison of Calibration of Classifiers
========================================
Well calibrated classifiers are probabilistic classifiers for which the output
of the predict_proba method can be directly interpreted as a confidence level.
For instance a well calibrated (bi... | bsd-3-clause |
dschien/PyExcelModelingHelper | tests/test_growth_coefficients.py | 1 | 7189 | import unittest
from datetime import datetime
import numpy as np
import pandas as pd
from excel_helper import ParameterRepository, ExcelParameterLoader, Parameter
class MyTestCase(unittest.TestCase):
def test_negative_growth(self):
"""
If start and end are one month apart, we expect an array of ... | mit |
YinongLong/scikit-learn | sklearn/feature_selection/rfe.py | 10 | 16481 | # 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 ..utils.metaes... | bsd-3-clause |
CKehl/pylearn2 | pylearn2/sandbox/cuda_convnet/bench.py | 44 | 3589 | __authors__ = "Ian Goodfellow"
__copyright__ = "Copyright 2010-2012, Universite de Montreal"
__credits__ = ["Ian Goodfellow"]
__license__ = "3-clause BSD"
__maintainer__ = "LISA Lab"
__email__ = "pylearn-dev@googlegroups"
from pylearn2.testing.skip import skip_if_no_gpu
skip_if_no_gpu()
import numpy as np
from theano.... | bsd-3-clause |
Laurawly/tvm-1 | tutorials/frontend/from_tflite.py | 1 | 6325 | # 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 |
pianomania/scikit-learn | sklearn/mixture/tests/test_bayesian_mixture.py | 84 | 17929 | # Author: Wei Xue <xuewei4d@gmail.com>
# Thierry Guillemot <thierry.guillemot.work@gmail.com>
# License: BSD 3 clause
import numpy as np
from scipy.special import gammaln
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_almost_equal
from sklearn.mixture.bayesian... | bsd-3-clause |
jnez71/kalmaNN | demos/2d_classify.py | 1 | 1766 | #!/usr/bin/env python
"""
Training and using a KNN for classification of 2D data.
Comparison of training methods, EKF vs SGD.
"""
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
import kalmann
# Get some noisy training data classifications, spirals!
n = 100
stdev = 0.2
U = np.zeros(... | mit |
rhiever/sklearn-benchmarks | model_code/random_search/LogisticRegression.py | 1 | 1120 | import sys
import pandas as pd
import numpy as np
from sklearn.preprocessing import RobustScaler
from sklearn.linear_model import LogisticRegression
from evaluate_model import evaluate_model
dataset = sys.argv[1]
num_param_combinations = int(sys.argv[2])
random_seed = int(sys.argv[3])
np.random.seed(random_seed)
pip... | mit |
ajc158/HoneyBee-Angular-Velocity-Detection | AVDU_model/Paper_detector_behaviour/analyse_FigX.py | 1 | 2109 | #!/usr/bin/python
import xml.etree.ElementTree as ET
import sml_log_parser
from subprocess import call, Popen
import os.path
import os
import time, stat, random, math
import ctypes
import struct
import csv
import numpy
from os import listdir
import matplotlib.pyplot as plt
print 'Script to generate Figure for the pap... | gpl-3.0 |
techbureau/zaifbot | zaifbot/indicators/bollinger_bands.py | 1 | 1328 | import pandas as pd
from talib import MA_Type
from .indicator import Indicator
class BBANDS(Indicator):
_NAME = 'bbands'
def __init__(self, currency_pair='btc_jpy', period='1d', length=25, matype=MA_Type.EMA):
super().__init__(currency_pair, period)
self._length = self._bounded_length(length)... | mit |
lordkman/burnman | burnman/tools.py | 3 | 28641 | # This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2017 by the BurnMan team, released under the GNU
# GPL v2 or later.
from __future__ import absolute_import
from __future__ import print_function
import operator
import bisect
import o... | gpl-2.0 |
openEduConnect/eduextractor | eduextractor/sis/illuminate/illuminate_exporter.py | 1 | 1702 | import pandas as pd
from ...config import _load_secrets
import sqlalchemy
import os
from tqdm import tqdm
class IlluminateSQLInterface:
"""A class representing a SQL interface to
Illuminate
"""
def __init__(self, secrets=None):
if secrets is None:
secrets = _load_secrets()
... | mit |
LeeKamentsky/CellProfiler | cellprofiler/modules/calculateimageoverlap.py | 1 | 50378 | import cellprofiler.icons
from cellprofiler.gui.help import PROTIP_RECOMEND_ICON
__doc__ = '''
<b>Calculate Image Overlap </b> calculates how much overlap occurs between the white portions of two black and white images
<hr>
This module calculates overlap by determining a set of statistics that measure the closeness of... | gpl-2.0 |
abhishekkrthakur/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | 31 | 6002 | # Author: Vlad Niculae
# License: BSD 3 clause
import sys
import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import ass... | bsd-3-clause |
davidgbe/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 278 | 3402 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the `datasets.make_multilabel_classification` dataset
generator. Each sample consists of counts of two features (up to 50 in
total), which are differently distri... | bsd-3-clause |
MohammedWasim/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 |
IndraVikas/scikit-learn | examples/applications/plot_model_complexity_influence.py | 323 | 6372 | """
==========================
Model Complexity Influence
==========================
Demonstrate how model complexity influences both prediction accuracy and
computational performance.
The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for
regression (resp. classification).
For each class of models we m... | bsd-3-clause |
ndingwall/scikit-learn | benchmarks/bench_hist_gradient_boosting_higgsboson.py | 12 | 4210 | from urllib.request import urlretrieve
import os
from gzip import GzipFile
from time import time
import argparse
import numpy as np
import pandas as pd
from joblib import Memory
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, roc_auc_score
# To use this experimental fea... | bsd-3-clause |
huffpostdata/python-pollster | pollster/api_client.py | 1 | 25298 | # coding: utf-8
"""
Copyright 2016 SmartBear Software
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 l... | bsd-2-clause |
sanketloke/scikit-learn | examples/cluster/plot_segmentation_toy.py | 91 | 3522 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
jorik041/scikit-learn | sklearn/neural_network/tests/test_rbm.py | 142 | 6276 | import sys
import re
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
from sklearn.utils.testing import (assert_almost_equal, assert_array_equal,
assert_true)
from sklearn.datasets import load_digits
from sklearn.externals.six.moves import cStringIO as ... | bsd-3-clause |
iABC2XYZ/abc | Epics/testSpeed.py | 1 | 1081 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 27 16:19:01 2017
@author: p
"""
import matplotlib.pyplot as plt
import numpy as np
plt.close('all')
cHV=np.round(np.random.random((14))*300-150)/10.
cHV_BK=cHV
plt.figure('cHV')
plt.plot(cHV,'-r')
flagHV=np.ones((14))
Amp=3.5
nFresh=30
for i... | gpl-3.0 |
saketkc/statsmodels | statsmodels/tsa/ar_model.py | 20 | 34034 | from __future__ import division
from statsmodels.compat.python import iteritems, range, string_types, lmap
import numpy as np
from numpy import dot, identity
from numpy.linalg import inv, slogdet
from scipy.stats import norm
from statsmodels.regression.linear_model import OLS
from statsmodels.tsa.tsatools import (lagm... | bsd-3-clause |
OSSHealth/ghdata | augur/metrics/insight.py | 1 | 1339 | #SPDX-License-Identifier: MIT
"""
Metrics that provide data about with insight detection and reporting
"""
import sqlalchemy as s
import pandas as pd
from augur.util import register_metric
@register_metric(type="repo_group_only")
def top_insights(self, repo_group_id, num_repos=6):
"""
Timeseries of pull reque... | mit |
evgchz/scikit-learn | sklearn/metrics/tests/test_ranking.py | 7 | 33931 | from __future__ import division, print_function
import numpy as np
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn import ensemble
from sklearn.datasets import make_multilabel_classification
from sklearn.random_projection import sparse_random_matrix
fro... | bsd-3-clause |
YerevaNN/mimic3-benchmarks | mimic3benchmark/scripts/create_length_of_stay.py | 1 | 3792 | from __future__ import absolute_import
from __future__ import print_function
import os
import argparse
import numpy as np
import pandas as pd
import random
random.seed(49297)
from tqdm import tqdm
def process_partition(args, partition, sample_rate=1.0, shortest_length=4.0, eps=1e-6):
output_dir = os.path.join(ar... | mit |
JT5D/scikit-learn | sklearn/ensemble/partial_dependence.py | 5 | 14809 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
from sklearn.externals.six.moves import zip
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.jobl... | bsd-3-clause |
and2egg/philharmonic | philharmonic/energy_meter/simple_visualiser.py | 2 | 1414 | '''
Created on Jun 15, 2012
@author: kermit
'''
#from pylab import *
import matplotlib.pyplot as plt
import numpy as np
from haley_api import Wattmeter
from continuous_energy_meter import ContinuousEnergyMeter
from runner_til_keypressed import RunnerTilKeypressed
machines = ["snowwhite", "grumpy"]
metrics = ["active... | gpl-3.0 |
bavardage/statsmodels | statsmodels/nonparametric/_kernel_base.py | 3 | 17942 | """
Module containing the base object for multivariate kernel density and
regression, plus some utilities.
"""
import copy
import numpy as np
from scipy import optimize
from scipy.stats.mstats import mquantiles
try:
import joblib
has_joblib = True
except ImportError:
has_joblib = False
import kernels
... | bsd-3-clause |
EPFL-LCN/neuronaldynamics-exercises | neurodynex3/cable_equation/passive_cable.py | 1 | 6153 | """
Implements compartmental model of a passive cable. See Neuronal Dynamics
`Chapter 3 Section 2 <http://neuronaldynamics.epfl.ch/online/Ch3.S2.html>`_
"""
# This file is part of the exercise code repository accompanying
# the book: Neuronal Dynamics (see http://neuronaldynamics.epfl.ch)
# located at http://github.c... | gpl-2.0 |
johnh2o2/cuvarbase | docs/source/plots/bls_example.py | 1 | 3320 | import cuvarbase.bls as bls
import numpy as np
import matplotlib.pyplot as plt
def phase(t, freq, phi0=0.):
phi = (t * freq - phi0)
phi -= np.floor(phi)
return phi
def transit_model(t, freq, y0=0.0, delta=1., q=0.01, phi0=0.5):
phi = phase(t, freq, phi0=phi0)
transit = phi < q
y = y0 * np.... | gpl-3.0 |
amueller/pystruct | examples/multiclass_comparision_svm_struct.py | 4 | 6536 | """
=================================
Comparing PyStruct and SVM-Struct
=================================
This example compares the performance of pystruct and SVM^struct on a
multi-class problem.
For the example to work, you need to install SVM^multiclass and
set the path in this file.
We are not using SVM^python, as ... | bsd-2-clause |
peret/visualize-bovw | svm_visualization.py | 1 | 1912 | from visualization import Visualization
from sklearn.svm import LinearSVC
from vcd import VisualConceptDetection
import numpy as np
from datamanagers.CaltechManager import CaltechManager
from itertools import izip
import sys
import os
def get_image_title(prediction, real):
"""Returns a string that describes whe... | gpl-2.0 |
Remper/sociallink | align-train/pairwise_models/model.py | 1 | 8491 | import json
from os import path
from sklearn.utils import shuffle
import tensorflow as tf
import time
import numpy as np
class Model:
def __init__(self, name):
self._name = name
self._desc = name
self._graph = None
self._session = None
self._saver = None
self._rea... | apache-2.0 |
ceholden/yatsm | bench/benchmarks/algorithms/ccdc.py | 3 | 6321 | import inspect
import os
import numpy as np
import sklearn.linear_model
from yatsm.algorithms import CCDCesque
from ..bench_utils.example_timeseries import PixelTimeseries
n = 50
# Hack for goof up in API previous to v0.6.0
def version_kwargs(d):
""" Fix API calls for kwargs dict ``d`` that should have key ``... | mit |
ocefpaf/iris | lib/iris/tests/test_pandas.py | 5 | 18054 | # Copyright Iris contributors
#
# This file is part of Iris and is released under the LGPL license.
# See COPYING and COPYING.LESSER in the root of the repository for full
# licensing details.
# import iris tests first so that some things can be initialised before
# importing anything else
import iris.tests as tests
... | lgpl-3.0 |
Roboticmechart22/sms-tools | lectures/07-Sinusoidal-plus-residual-model/plots-code/hpsModelFrame.py | 22 | 2075 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris, resample
import math
from scipy.fftpack import fft, ifft, fftshift
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import ... | agpl-3.0 |
srjit/fakenewschallange | code/modelsv2/train.py | 1 | 8348 | from tqdm import tqdm, tqdm_pandas
import pandas as pd
import string
import numpy as np
import datetime
import os
from functools import reduce
import gensim.models.keyedvectors as word2vec
from sklearn.model_selection import train_test_split
tqdm.pandas(tqdm())
__author__ = "Sreejith Sreekumar"
__email__ = "sreekuma... | gpl-3.0 |
zihua/scikit-learn | examples/gaussian_process/plot_compare_gpr_krr.py | 67 | 5191 | """
==========================================================
Comparison of kernel ridge and Gaussian process regression
==========================================================
Both kernel ridge regression (KRR) and Gaussian process regression (GPR) learn
a target function by employing internally the "kernel trick... | bsd-3-clause |
raoulbq/scipy | doc/source/tutorial/stats/plots/kde_plot3.py | 132 | 1229 | import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
np.random.seed(12456)
x1 = np.random.normal(size=200) # random data, normal distribution
xs = np.linspace(x1.min()-1, x1.max()+1, 200)
kde1 = stats.gaussian_kde(x1)
kde2 = stats.gaussian_kde(x1, bw_method='silverman')
fig = plt.figure(figsi... | bsd-3-clause |
justinbois/bebi103_utils | bebi103/deprecated/emcee.py | 2 | 15081 | import collections
import warnings
import numpy as np
import pandas as pd
import emcee
import ptemcee
def generic_log_posterior(log_prior, log_likelihood, params, logpargs=(),
loglargs=()):
"""
Generic log posterior for MCMC calculations
Parameters
----------
log_prior ... | mit |
alekz112/statsmodels | examples/python/robust_models_0.py | 33 | 2992 |
## Robust Linear Models
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
# ## Estimation
#
# Load data:
data = sm.datasets.stackloss.load()
data.exog = sm.add_constant(data.ex... | bsd-3-clause |
rohanp/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_hyperparameters.py | 102 | 5177 | """
=================================================
Hyper-parameters of Approximate Nearest Neighbors
=================================================
This example demonstrates the behaviour of the
accuracy of the nearest neighbor queries of Locality Sensitive Hashing
Forest as the number of candidates and the numb... | bsd-3-clause |
IshankGulati/scikit-learn | benchmarks/bench_mnist.py | 38 | 6799 | """
=======================
MNIST dataset benchmark
=======================
Benchmark on the MNIST dataset. The dataset comprises 70,000 samples
and 784 features. Here, we consider the task of predicting
10 classes - digits from 0 to 9 from their raw images. By contrast to the
covertype dataset, the feature space is... | bsd-3-clause |
gdementen/larray | larray/inout/pandas.py | 2 | 15308 | from __future__ import absolute_import, print_function
from itertools import product
import numpy as np
import pandas as pd
from larray.core.array import Array
from larray.core.axis import Axis, AxisCollection
from larray.core.constants import nan
from larray.util.misc import unique
from larray.util.compat import ba... | gpl-3.0 |
ambikeshwar1991/gnuradio | gr-filter/examples/decimate.py | 13 | 5841 | #!/usr/bin/env python
#
# Copyright 2009,2012 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your optio... | gpl-3.0 |
datapythonista/pandas | pandas/tests/indexes/test_indexing.py | 1 | 8571 | """
test_indexing tests the following Index methods:
__getitem__
get_loc
get_value
__contains__
take
where
get_indexer
slice_locs
asof_locs
The corresponding tests.indexes.[index_type].test_indexing files
contain tests for the corresponding methods specific to those Index subclasses... | bsd-3-clause |
rdipietro/pyrvm | rvm.py | 1 | 8538 | import itertools
import numpy as np
from sklearn.metrics import pairwise
import pulp
class RVM(object):
"""Ranking Vector Machine
Learn and predict orderings of vectors using large-margin criteria.
This is an implementation of the ranking-vector-machine algorithm from
Yu, Hwanjo and Kim, Sung... | mit |
lbeltrame/bcbio-nextgen | bcbio/qc/qualimap.py | 1 | 18318 | """Quality control using Qualimap.
http://qualimap.bioinfo.cipf.es/
"""
import glob
import os
import shutil
import pandas as pd
import pybedtools
import toolz as tz
import toolz.dicttoolz as dtz
from bcbio.log import logger
from bcbio import bam, utils
from bcbio.bam import readstats
from bcbio.ngsalign import posta... | mit |
iaklampanos/bde-pilot-2 | backend/api_methods.py | 1 | 39958 | """
CLASS INFO
---------------------------------------------------------------------------
This class acts as the model (from MVC framework) for the SC5 #3 pilot.
---------------------------------------------------------------------------
"""
from Dataset_transformations import Dataset_transformations
from... | apache-2.0 |
jstoxrocky/statsmodels | statsmodels/examples/ex_univar_kde.py | 34 | 5127 | """
This example tests the nonparametric estimator
for several popular univariate distributions with the different
bandwidth selction methods - CV-ML; CV-LS; Scott's rule of thumb.
Produces six different plots for each distribution
1) Beta
2) f
3) Pareto
4) Laplace
5) Weibull
6) Poisson
"""
from __future__ import p... | bsd-3-clause |
imaculate/scikit-learn | sklearn/metrics/tests/test_classification.py | 2 | 54750 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import la... | bsd-3-clause |
kfogel/batman | batman/tests.py | 1 | 1757 | # The batman package: fast computation of exoplanet transit light curves
# Copyright (C) 2015 Laura Kreidberg
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License... | gpl-3.0 |
smartscheduling/scikit-learn-categorical-tree | examples/model_selection/plot_learning_curve.py | 250 | 4171 | """
========================
Plotting Learning Curves
========================
On the left side the learning curve of a naive Bayes classifier is shown for
the digits dataset. Note that the training score and the cross-validation score
are both not very good at the end. However, the shape of the curve can be found
in ... | bsd-3-clause |
amirchohan/HDR | img_hist.py | 1 | 3159 | #!/usr/bin/python
# RGB Hitogram
# This script will create a histogram image based on the RGB content of
# an image. It uses PIL to do most of the donkey work but then we just
# draw a pretty graph out of it.
#
# May 2009, Scott McDonough, www.scottmcdonough.co.uk
#
import os
import sys
import Image, ImageDraw, Image... | bsd-3-clause |
amozie/amozie | studzie/time_series_feature_select.py | 1 | 1046 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf
from statsmodels.graphics.tsaplots import plot_pacf
from sklearn.ensemble import RandomForestRegressor as rfr
from sklearn.feature_selection import RFE
# 加载数据
series = pd.Series.from_csv('./dataset... | apache-2.0 |
planetarymike/IDL-Colorbars | IDL_py_test/011_BLUE-RED.py | 1 | 5973 | from matplotlib.colors import LinearSegmentedColormap
from numpy import nan, inf
cm_data = [[0., 0., 0.],
[0., 0.00392157, 0.00392157],
[0., 0.00784314, 0.00784314],
[0., 0.0117647, 0.0117647],
[0., 0.0156863, 0.0156863],
[0., 0.0313725, 0.0313725],
[0., 0.0470588, 0.0470588],
[0., 0.0627451, 0.0627451],
[0., 0.0823529... | gpl-2.0 |
hunering/demo-code | python/books/DLWP/5.4.3-cam.py | 1 | 1670 | from keras import models
from keras.preprocessing import image
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
import cv2
from utils import init_keras
init_keras()
img_path = r"C:\Users\huxiaomi\Download... | gpl-3.0 |
rbrecheisen/pyminer | pyminer/network/classifiers.py | 1 | 9893 | __author__ = 'Ralph'
import os
import numpy as np
import pandas as pd
from sklearn.svm import SVC
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import StratifiedKFold
from sklearn.externals import joblib
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_sco... | apache-2.0 |
robwarm/gpaw-symm | gpaw/atom/generator2.py | 1 | 40677 | # -*- coding: utf-8 -*-
import sys
from math import pi, exp, sqrt, log
import numpy as np
from scipy.optimize import fsolve
from scipy import __version__ as scipy_version
from ase.utils import prnt, devnull
from ase.units import Hartree
from ase.data import atomic_numbers, chemical_symbols
from gpaw.spline import Spl... | gpl-3.0 |
macronucleus/chromagnon | setup.py | 1 | 4536 | #!/usr/bin/env python
"""
Usage:
python setup.py [py2app/sdist/install]
on Mac 20180202:
modify python3.6.sysconfig._get_sysconfigdata_name(check_exists=None)
ln -s /Users/USER/miniconda3/lib/libpython3.6m.dylib libpython3.6.dylib
then pythonw setup.py py2app to import javabridge
install numpy wit... | mit |
TomAugspurger/pandas | pandas/tests/scalar/interval/test_arithmetic.py | 1 | 1477 | from datetime import timedelta
import numpy as np
import pytest
from pandas import Interval, Timedelta, Timestamp
@pytest.mark.parametrize("method", ["__add__", "__sub__"])
@pytest.mark.parametrize(
"interval",
[
Interval(Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")),
I... | bsd-3-clause |
bert9bert/statsmodels | examples/python/predict.py | 33 | 1580 |
## Prediction (out of sample)
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
# ## Artificial data
nsample = 50
sig = 0.25
x1 = np.linspace(0, 20, nsample)
X = np.column_stack((x1, np.sin(x1), (x1-5)**2))
X = sm.add_constant(X)
beta = [5., 0.5, 0.5, -0.02]
y_true = np.dot(X, b... | bsd-3-clause |
kylerbrown/scikit-learn | sklearn/decomposition/tests/test_truncated_svd.py | 240 | 6055 | """Test truncated SVD transformer."""
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_raises, assert_greater,
... | bsd-3-clause |
mahak/spark | python/pyspark/pandas/series.py | 9 | 197528 | #
# 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 |
xwolf12/scikit-learn | sklearn/feature_extraction/text.py | 110 | 50157 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gma... | bsd-3-clause |
xhqu1981/pymatgen | pymatgen/analysis/diffraction/xrd.py | 2 | 14977 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import division, unicode_literals
from math import sin, cos, asin, pi, degrees, radians
import os
import collections
import numpy as np
import json
from pymatgen.symmetry.analyzer import Spac... | mit |
adamgreenhall/scikit-learn | examples/classification/plot_classification_probability.py | 242 | 2624 | """
===============================
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 |
iver56/cross-adaptive-audio | plot.py | 1 | 6325 | import argparse
import json
import numpy as np
import matplotlib
from matplotlib.font_manager import FontProperties
import os
import settings
from itertools import cycle
class Plot(object):
"""
Plot cumulative max similarity for all experiments
"""
def __init__(self):
# Python 2 and 3 compati... | gpl-3.0 |
zak-k/cartopy | lib/cartopy/examples/wmts.py | 3 | 1039 | __tags__ = ['Web services']
"""
Interactive WMTS (Web Map Tile Service)
---------------------------------------
This example demonstrates the interactive pan and zoom capability
supported by an OGC web services Web Map Tile Service (WMTS) aware axes.
The example WMTS layer is a single composite of data sampled over n... | lgpl-3.0 |
quantopian/research_public | research/ml_algo.py | 2 | 17465 | # https://www.quantopian.com/posts/machine-learning-alpha-with-risk-constraints
# https://www.quantopian.com/posts/machine-learning-on-quantopian-part-3-building-an-algorithm?utm_campaign=machine-learning-on-quantopian-part-3-building-an-algorithm&utm_medium=email&utm_source=forums
from collections import OrderedDict
f... | apache-2.0 |
philrosenfield/TPAGB-calib | tpagb_calibration/plotting/interactive_match_cmdlimits.py | 1 | 7262 | #!/usr/bin/env python
import argparse
import matplotlib.pylab as plt
import numpy as np
import os
import sys
import time
from astropy.io import fits
from ResolvedStellarPops.tpagb_path_config import tpagb_path
def move_on(ok, msg='0 to move on: '):
ok = int(raw_input(msg))
time.sleep(1)
return ok
def fin... | bsd-3-clause |
modflowpy/flopydoc | docs/pysrc/ex_lake.py | 1 | 3491 | # ExampleLake - Flopy example of multi-layer steady model with one fixed head cell
# This is an example of steady flow in a multi-layer model.
# Heads are fixed along all outer boundaries to 100 meters.
# In the center cell in the top layer, the head is fixed to 90 meters.
# Import the Python packages that w... | bsd-3-clause |
seanli9jan/tensorflow | tensorflow/contrib/learn/python/learn/estimators/multioutput_test.py | 136 | 1696 | # 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 |
Curly-Mo/sample-recognition | ann.py | 1 | 4427 | import logging
import numpy as np
logger = logging.getLogger(__name__)
FLANN_ALGS = ['kdtree', 'kmeans', 'composite', 'lsh', 'autotuned']
CV_ALGS = ['kdtree', 'kmeans', 'composite', 'lsh', 'autotuned']
SKLEARN_ALGS = ['kd_tree', 'ball_tree', 'brute', 'auto']
def train_matcher(data, algorithm='kdtree'):
# if algo... | apache-2.0 |
jlegendary/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 143 | 22295 | """
Todo: cross-check the F-value with stats model
"""
from __future__ import division
import itertools
import warnings
import numpy as np
from scipy import stats, sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
AlexRobson/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 261 | 2836 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from nose.tools import assert_equa... | bsd-3-clause |
choldgraf/ecogtools | ecogtools/tfr.py | 1 | 2902 | import mne
from glob import glob
import pandas as pd
import numpy as np
import sys
from tqdm import tqdm
__all__ = ['extract_amplitude']
def extract_amplitude(inst, freqs, n_cycles=7, normalize=False, n_hilbert=None,
picks=None, n_jobs=1, ):
"""Extract the time-varying amplitude for a frequ... | bsd-2-clause |
jszymon/pacal | pacal/depvars/copulas.py | 1 | 34667 | """Set of copulas different types"""
from __future__ import print_function
from pacal.integration import *
from pacal.interpolation import *
from matplotlib.collections import PolyCollection
import pacal.distr
#from pacal import *
from pacal.segments import PiecewiseDistribution, MInfSegment, PInfSegment, Segment, _... | gpl-3.0 |
BhallaLab/benchmarks | neuro_morpho/buildCA1Pyr.py | 1 | 5403 | import moogli
import numpy
import pylab
import moose
from moose import neuroml
from PyQt4 import Qt, QtCore, QtGui
import matplotlib.pyplot as plt
import sys
import os
from moose.neuroml.ChannelML import ChannelML
PI = 3.14159265359
frameRunTime = 0.001
runtime = 1.0
inject = 25e-10
simdt = 5e-5
FaradayConst = 96845.3... | gpl-2.0 |
bthirion/scikit-learn | sklearn/utils/estimator_checks.py | 16 | 64623 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import pickle
from copy import deepcopy
import numpy as np
from scipy import sparse
from scipy.stats import rankdata
import struct
from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash, Memor... | bsd-3-clause |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/pandas/sandbox/qtpandas.py | 13 | 4347 | '''
Easy integration of DataFrame into pyqt framework
@author: Jev Kuznetsov
'''
# GH9615
import warnings
warnings.warn("The pandas.sandbox.qtpandas module is deprecated and will be "
"removed in a future version. We refer users to the external package "
"here: https://github.com/datalyze... | apache-2.0 |
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