repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
avistous/QSTK | qstkutil/bollinger.py | 2 | 1494 | '''
(c) 2011, 2012 Georgia Tech Research Corporation
This source code is released under the New BSD license. Please see
http://wiki.quantsoftware.org/index.php?title=QSTK_License
for license details.
Created on Jan 1, 2011
@author:Drew Bratcher
@contact: dbratcher@gatech.edu
@summary: Contains tutorial for backteste... | bsd-3-clause |
ZENGXH/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 |
RachitKansal/scikit-learn | sklearn/decomposition/pca.py | 192 | 23117 | """ Principal Component Analysis
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <d.engemann@fz-juelich.de>
# Michael Eickenberg <michael.eickenberg@inria.fr>
#
# Lice... | bsd-3-clause |
dvro/UnbalancedDataset | imblearn/under_sampling/tests/test_nearmiss_3.py | 2 | 7992 | """Test the module nearmiss."""
from __future__ import print_function
import os
import numpy as np
from numpy.testing import assert_raises
from numpy.testing import assert_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_warns
from sklearn.datasets import make_classification
from s... | mit |
ahoyosid/scikit-learn | sklearn/metrics/setup.py | 299 | 1024 | import os
import os.path
import numpy
from numpy.distutils.misc_util import Configuration
from sklearn._build_utils import get_blas_info
def configuration(parent_package="", top_path=None):
config = Configuration("metrics", parent_package, top_path)
cblas_libs, blas_info = get_blas_info()
if os.name ==... | bsd-3-clause |
CartoDB/crankshaft | src/py/crankshaft/test/test_clustering_kmeans.py | 6 | 2722 | import unittest
import numpy as np
from helper import fixture_file
from crankshaft.clustering import Kmeans
from crankshaft.analysis_data_provider import AnalysisDataProvider
import crankshaft.clustering as cc
from crankshaft import random_seeds
import json
from collections import OrderedDict
class FakeDataProvide... | bsd-3-clause |
nismod/energy_demand | energy_demand/plotting/fig_p2_weather_val.py | 1 | 14554 | """Fig 2 figure
"""
import numpy as np
import matplotlib.pyplot as plt
#from scipy.stats import mstats
import pandas as pd
import geopandas as gpd
from scipy import stats
from shapely.geometry import Point
import matplotlib.pyplot as plt
from collections import defaultdict
from matplotlib.colors import Normalize
from ... | mit |
cython-testbed/pandas | pandas/core/computation/pytables.py | 2 | 19478 | """ manage PyTables query interface via Expressions """
import ast
from functools import partial
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_list_like
import pandas.core.common as com
from pandas.compat import u, string_types, DeepChainMap
from pandas.core.base import StringMixin
f... | bsd-3-clause |
DSLituiev/scikit-learn | sklearn/cross_decomposition/cca_.py | 151 | 3192 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
open-austin/capture | distance.py | 1 | 4386 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import argparse
import glob
import os
import numpy as np
import pandas as pd
from geopy.distance import vincenty as point_distance
def ingest(fn, route_id, begin_latlng, end_latlng):
df = pd.read_csv(fn, parse_dates=[... | gpl-3.0 |
MohammedWasim/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 21 | 13171 | import numpy as np
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from scipy.special import psi
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
_dirichlet_expect... | bsd-3-clause |
xiaoxiamii/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 128 | 4761 | """
===================================
Compare cross decomposition methods
===================================
Simple usage of various cross decomposition algorithms:
- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
- CCA
Given 2 multivari... | bsd-3-clause |
rbalda/neural_ocr | env/lib/python2.7/site-packages/numpy/fft/fftpack.py | 72 | 45497 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1)
ifft(a, n=None, axis=-1)
rfft(a, n=None, axis=-1)
irfft(a, n=None, axis=-1)
hfft(a, n=None, axis=-1)
ihfft(a, n=None, axis=-1)
fftn(a, s=None, axes=None)
ifftn(a, s=None, axes=None)
rfftn(a, s=None, axes=None)
irfftn(a, s=None, axes=None... | mit |
e-baumer/sampling | sampling/stratified_rand.py | 1 | 5349 | from __future__ import division
from collections import defaultdict
import numpy as np
from base_sample import BaseSample
from sklearn.cluster import AffinityPropagation as AP
import pandas as pd
from collections import Counter
class StratifiedRandom(BaseSample):
def __init__(self, data_frame, number_arms=2):... | apache-2.0 |
CforED/Machine-Learning | sklearn/tests/test_cross_validation.py | 20 | 46586 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy import stats
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.test... | bsd-3-clause |
alvarofierroclavero/scikit-learn | sklearn/cross_decomposition/cca_.py | 209 | 3150 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
datalyze-solutions/pandas-qt | pandasqt/views/CSVDialogs.py | 1 | 23796 | # -*- coding: utf-8 -*-
import os
from encodings.aliases import aliases as _encodings
import pandas
from pandasqt.compat import Qt, QtCore, QtGui, Slot, Signal
from pandasqt.encoding import Detector
from pandasqt.models.DataFrameModel import DataFrameModel
from pandasqt.views.CustomDelegates import DtypeComboDelegat... | mit |
Diviyan-Kalainathan/causal-humans | Clustering/performance_evaluation.py | 1 | 3279 | '''
Computing the misclassification error distance between to 2 k-means clustering
according to Marina Meila, "The Uniqueness of a Good Optimum for K-Means", ICML 2006
Author : Diviyan Kalainathan
Date : 20/06/2016
'''
import csv,numpy,itertools
from sklearn import metrics
def Clustering_performance_evaluation(mode,... | mit |
sibis-platform/ncanda-datacore | scripts/reporting/xnat_scans_filter.py | 4 | 5552 | #!/usr/bin/env python
##
## See COPYING file distributed along with the ncanda-data-integration package
## for the copyright and license terms
##
"""
xnat_scans_filter.py
======================
This script filters the csv file generated using xnat_extractor.py. This filters
is based on records from XNAT where there i... | bsd-3-clause |
rohit21122012/DCASE2013 | runs/2016/baseline128/src/evaluation.py | 38 | 42838 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import numpy
import math
from sklearn import metrics
class DCASE2016_SceneClassification_Metrics():
"""DCASE 2016 scene classification metrics
Examples
--------
>>> dcase2016_scene_metric = DCASE2016_SceneClassification_Metrics(class_list=... | mit |
justincassidy/scikit-learn | sklearn/ensemble/tests/test_weight_boosting.py | 35 | 16763 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_array_equal, assert_array_less
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal, assert_true
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
wanggang3333/scikit-learn | sklearn/metrics/cluster/supervised.py | 207 | 27395 | """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 |
printedheart/h2o-3 | h2o-py/h2o/model/binomial.py | 5 | 24203 | """
Binomial Models
"""
from metrics_base import *
class H2OBinomialModel(ModelBase):
"""
Class for Binomial models.
"""
def __init__(self, dest_key, model_json):
"""
Create a new binomial model.
"""
super(H2OBinomialModel, self).__init__(dest_key, model_json,H2OBinomialModelMetrics)
def F... | apache-2.0 |
mikebenfield/scikit-learn | sklearn/covariance/tests/test_graph_lasso.py | 33 | 6157 | """ Test the graph_lasso module.
"""
import sys
import numpy as np
from scipy import linalg
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_less
from sklearn.utils.testing import assert_warns_message
from sklearn.covariance import (graph_lasso, GraphLasso, G... | bsd-3-clause |
wdurhamh/statsmodels | statsmodels/sandbox/examples/ex_cusum.py | 33 | 3219 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 02 11:41:25 2010
Author: josef-pktd
"""
import numpy as np
from scipy import stats
from numpy.testing import assert_almost_equal
import statsmodels.api as sm
from statsmodels.sandbox.regression.onewaygls import OneWayLS
from statsmodels.stats.diagnostic import recursive... | bsd-3-clause |
Alecardv/College-projects | Metodos Numericos 2012/trapecio.py | 1 | 1308 | import function
from matplotlib.pyplot import *
from pylab import *
import numpy as np
import math
class Trapecio:
def __init__(self, fun, xi, xf):
self.fun = function.Function(fun,'x')
self.a,self.b = xi,xf
self.fig, self.ax = subplots()
def relativeError(self):
f = self.fun.getDerivate()
Ea = ((self.b-... | gpl-3.0 |
voxlol/scikit-learn | examples/cluster/plot_dict_face_patches.py | 337 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | bsd-3-clause |
xyguo/scikit-learn | benchmarks/bench_plot_incremental_pca.py | 374 | 6430 | """
========================
IncrementalPCA benchmark
========================
Benchmarks for IncrementalPCA
"""
import numpy as np
import gc
from time import time
from collections import defaultdict
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import Incre... | bsd-3-clause |
henrykironde/scikit-learn | examples/manifold/plot_compare_methods.py | 259 | 4031 | """
=========================================
Comparison of Manifold Learning methods
=========================================
An illustration of dimensionality reduction on the S-curve dataset
with various manifold learning methods.
For a discussion and comparison of these algorithms, see the
:ref:`manifold module... | bsd-3-clause |
adamrp/qiime | scripts/categorized_dist_scatterplot.py | 15 | 6299 | #!/usr/bin/env python
# File created on 19 Jan 2011
from __future__ import division
__author__ = "Justin Kuczynski"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["Justin Kuczynski"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = "Justin Kuczynski"
__email__ = "justinak@gmail.com"
... | gpl-2.0 |
dssg/wikienergy | disaggregator/build/pandas/pandas/tests/test_frame.py | 1 | 552242 | # -*- coding: utf-8 -*-
from __future__ import print_function
# pylint: disable-msg=W0612,E1101
from copy import deepcopy
from datetime import datetime, timedelta, time
import sys
import operator
import re
import csv
import nose
import functools
import itertools
from itertools import product
from distutils.version imp... | mit |
AnasGhrab/scikit-learn | sklearn/metrics/metrics.py | 233 | 1262 | import warnings
warnings.warn("sklearn.metrics.metrics is deprecated and will be removed in "
"0.18. Please import from sklearn.metrics",
DeprecationWarning)
from .ranking import auc
from .ranking import average_precision_score
from .ranking import label_ranking_average_precision_score
fro... | bsd-3-clause |
iismd17/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingCla... | bsd-3-clause |
energyPATHWAYS/energyPATHWAYS | model_building_tools/create_map_keys_from_drivers/map_key_from_driver.py | 1 | 2834 | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 07 19:20:05 2016
@author: ryandrewjones
"""
import sys
import signal
import click
import os
import cPickle as pickle
import energyPATHWAYS.config as cfg
import energyPATHWAYS.util as util
from energyPATHWAYS.pathways_model import PathwaysModel
import energyPATHWAYS.shape... | mit |
hrjn/scikit-learn | sklearn/mixture/dpgmm.py | 25 | 35852 | """Bayesian Gaussian Mixture Models and
Dirichlet Process Gaussian Mixture Models"""
from __future__ import print_function
# Author: Alexandre Passos (alexandre.tp@gmail.com)
# Bertrand Thirion <bertrand.thirion@inria.fr>
#
# Based on mixture.py by:
# Ron Weiss <ronweiss@gmail.com>
# Fabian Ped... | bsd-3-clause |
petewarden/tensorflow | tensorflow/python/keras/engine/data_adapter.py | 1 | 57975 | # Copyright 2019 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 |
bsipocz/scikit-image | doc/examples/plot_label.py | 23 | 1557 | """
===================
Label image regions
===================
This example shows how to segment an image with image labelling. The following
steps are applied:
1. Thresholding with automatic Otsu method
2. Close small holes with binary closing
3. Remove artifacts touching image border
4. Measure image regions to fi... | bsd-3-clause |
rs2/bokeh | examples/webgl/clustering.py | 6 | 2167 | """ Example inspired by an example from the scikit-learn project:
http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
"""
import numpy as np
try:
from sklearn import cluster, datasets
from sklearn.preprocessing import StandardScaler
except ImportError:
raise ImportError('This... | bsd-3-clause |
JonnaStalring/AZOrange | ConfPred/conformal-master/examples/icp_classification_tree.py | 2 | 2055 | #!/usr/bin/env python3
"""
Example: inductive conformal classification using DecisionTreeClassifier
"""
import numpy as np
from sklearn.tree import DecisionTreeClassifier
import Orange
from nonconformist.acp import CrossConformalClassifier, AggregatedCp, CrossSampler
from nonconformist.icp import IcpClassifier
from... | lgpl-3.0 |
AnasGhrab/scikit-learn | examples/plot_digits_pipe.py | 250 | 1809 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================
The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the predictio... | bsd-3-clause |
ml-lab/pylearn2 | pylearn2/models/tests/test_s3c_inference.py | 4 | 14275 | from pylearn2.models.s3c import S3C
from pylearn2.models.s3c import E_Step_Scan
from pylearn2.models.s3c import Grad_M_Step
from pylearn2.models.s3c import E_Step
from theano import function
import numpy as np
import theano.tensor as T
from theano import config
#from pylearn2.utils import serial
import warnings
def b... | bsd-3-clause |
miloharper/neural-network-animation | matplotlib/tests/test_colors.py | 9 | 9307 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from nose.tools import assert_raises, assert_equal
import numpy as np
from numpy.testing.utils import assert_array_equal, assert_array_almost_equal
import matplotlib.colors as mcolors
import matpl... | mit |
Leotrinos/agpy | agpy/showspec.py | 6 | 51670 | """
showspec is my homegrown spectrum plotter, meant to somewhat follow STARLINK's
SPLAT and have functionality similar to GAIA, but with an emphasis on producing
publication-quality plots (which, while splat may do, it does unreproducibly)
.. todo::
-add spectrum arithmetic tools
(as is, you can use nump... | mit |
MycChiu/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py | 7 | 12865 | # 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 |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/core/strings.py | 1 | 105891 | import codecs
from functools import wraps
import re
import textwrap
from typing import Dict, List
import warnings
import numpy as np
import pandas._libs.lib as lib
import pandas._libs.ops as libops
from pandas.util._decorators import Appender, deprecate_kwarg
from pandas.core.dtypes.common import (
ensure_object... | apache-2.0 |
mzl9039/spark | python/pyspark/sql/session.py | 14 | 25557 | #
# 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 |
lazywei/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
bachiraoun/fullrmc | Constraints/StructureFactorConstraints.py | 1 | 64342 | """
StructureFactorConstraints contains classes for all constraints related experimental static structure factor functions.
.. inheritance-diagram:: fullrmc.Constraints.StructureFactorConstraints
:parts: 1
"""
# standard libraries imports
from __future__ import print_function
import itertools, re
# external libra... | agpl-3.0 |
asdf123101/HDPG1D | hdpg1d/adaptation.py | 1 | 8070 | import numpy as np
from numpy import concatenate as cat
from scipy.sparse import csr_matrix
import scipy.sparse.linalg as spla
from copy import copy
import matplotlib.pyplot as plt
import warnings
from .preprocess import shape, discretization, boundaryCondition
plt.rc('text', usetex=True)
plt.rc('font', family='serif'... | mit |
apdjustino/DRCOG_Urbansim | src/opus_gui/results_manager/run/indicator_framework/visualizer/visualizers/matplotlib_lorenzcurve.py | 1 | 10890 | # Opus/UrbanSim urban simulation software.
# Copyright (C) 2010-2011 University of California, Berkeley, 2005-2009 University of Washington
# See opus_core/LICENSE
import os, re, sys, time, traceback
from copy import copy
from opus_gui.results_manager.run.indicator_framework.visualizer.visualizers.abstract_visualizat... | agpl-3.0 |
NDKoehler/DataScienceBowl2017_7th_place | dsb3_networks/classification/resnet2D_0.7res_80/config_2Dfinal.py | 1 | 3207 | from collections import defaultdict
from datetime import datetime
import json
import tensorflow as tf
import os, sys
import pandas as pd
#config dic
H = defaultdict(lambda: None)
#All possible config options:
H['optimizer'] = 'MomentumOptimizer'#'RMSPropOptimizer'
H['learning_rate'] = 0.001
H['momentum'] = 0.9 #0.9... | mit |
IssamLaradji/scikit-learn | sklearn/tests/test_grid_search.py | 8 | 26766 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import sys
import warnings
import numpy as np
import sci... | bsd-3-clause |
IshankGulati/scikit-learn | benchmarks/bench_plot_svd.py | 72 | 2914 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
import six
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklear... | bsd-3-clause |
jdvelasq/cashflows | cashflows/inflation.py | 1 | 4674 | """
Constant dollar transformations
===============================================================================
Overview
-------------------------------------------------------------------------------
The function ``const2curr`` computes the equivalent generic cashflow in current
dollars from a generic cashflow ... | mit |
mjudsp/Tsallis | sklearn/datasets/species_distributions.py | 64 | 7917 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
cbertinato/pandas | pandas/tests/io/test_gcs.py | 1 | 2310 | from io import StringIO
import numpy as np
import pytest
from pandas import DataFrame, date_range, read_csv
from pandas.util import _test_decorators as td
from pandas.util.testing import assert_frame_equal
from pandas.io.common import is_gcs_url
def test_is_gcs_url():
assert is_gcs_url("gcs://pandas/somethinge... | bsd-3-clause |
yancz1989/cancer | utilities.py | 1 | 4491 | import SimpleITK as sitk
import numpy as np
import csv
import os
import json
from PIL import Image
import matplotlib.pyplot as plt
import SimpleITK as sitk
from cv2 import imread, imwrite
def load_itk_image(filename):
itkimage = sitk.ReadImage(filename)
numpyImage = sitk.GetArrayFromImage(itkimage)
numpyOrigin =... | mit |
mjudsp/Tsallis | examples/tree/plot_tree_regression.py | 95 | 1516 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... | bsd-3-clause |
thientu/scikit-learn | sklearn/metrics/scorer.py | 211 | 13141 | """
The :mod:`sklearn.metrics.scorer` submodule implements a flexible
interface for model selection and evaluation using
arbitrary score functions.
A scorer object is a callable that can be passed to
:class:`sklearn.grid_search.GridSearchCV` or
:func:`sklearn.cross_validation.cross_val_score` as the ``scoring`` parame... | bsd-3-clause |
legacysurvey/pipeline | py/obiwan/decals_sim_randoms.py | 2 | 11774 | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import os
import pickle
def add_scatter(ax,x,y,c='b',m='o',lab='',s=80,drawln=False,alpha=1):
ax.scatter(x,y, s=s, lw=2.,facecolors='none',edgecolors=c, marker=m,label=lab,alpha=alpha)
if drawln: ax.plot(x,y, c=c,ls='-'... | gpl-2.0 |
abinashpanda/pgmpy | pgmpy/tests/test_models/test_BayesianModel.py | 2 | 25284 | import unittest
import networkx as nx
import pandas as pd
import numpy as np
import numpy.testing as np_test
from pgmpy.models import BayesianModel, MarkovModel
import pgmpy.tests.help_functions as hf
from pgmpy.factors.discrete import TabularCPD, JointProbabilityDistribution, DiscreteFactor
from pgmpy.independencies... | mit |
joshuamorton/calc_three_proj | plot.py | 1 | 1969 | from matplotlib import pyplot as plt
import numpy as np
import iterative
import pascal
import power
plt.style.use('ggplot')
qr = []
lu = []
for i in range(2, 13):
q = pascal.solve_qr_b(pascal.pascal_matrix(i), pascal.harmonic_vector(i))
l = pascal.solve_lu_b(pascal.pascal_matrix(i), pascal.harmonic_vector(i))
... | mit |
jayflo/scikit-learn | examples/cluster/plot_birch_vs_minibatchkmeans.py | 333 | 3694 | """
=================================
Compare BIRCH and MiniBatchKMeans
=================================
This example compares the timing of Birch (with and without the global
clustering step) and MiniBatchKMeans on a synthetic dataset having
100,000 samples and 2 features generated using make_blobs.
If ``n_clusters... | bsd-3-clause |
hitszxp/scikit-learn | examples/linear_model/plot_lasso_coordinate_descent_path.py | 254 | 2639 | """
=====================
Lasso and Elastic Net
=====================
Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.
The coefficients can be forced to be positive.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import num... | bsd-3-clause |
partofthething/laserComm | laserComm/receiver.py | 1 | 2975 | '''
receiver runs the ADC and photoresistor to receive an input signal.
USes MCP3008 ADC via the hardware SPI interface.
Connections are:
MCP3008 VDD -> 3.3V (red)
MCP3008 VREF -> 3.3V (red)
MCP3008 AGND -> GND (orange)
MCP3008 CLK -> SCLK (yellow)
MCP3008 DOUT -> MISO (green)
MCP3008 DIN -> ... | mit |
ageron/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/data_feeder_test.py | 25 | 13554 | # 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 |
raghavrv/scikit-learn | sklearn/cluster/tests/test_hierarchical.py | 33 | 20167 | """
Several basic tests for hierarchical clustering procedures
"""
# Authors: Vincent Michel, 2010, Gael Varoquaux 2012,
# Matteo Visconti di Oleggio Castello 2014
# License: BSD 3 clause
from tempfile import mkdtemp
import shutil
from functools import partial
import numpy as np
from scipy import sparse
from... | bsd-3-clause |
oreilly-japan/deep-learning-from-scratch | ch07/apply_filter.py | 4 | 1634 | # coding: utf-8
import sys, os
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
import numpy as np
import matplotlib.pyplot as plt
from simple_convnet import SimpleConvNet
from matplotlib.image import imread
from common.layers import Convolution
def filter_show(filters, nx=4, show_num=16):
"""
c.f. http... | mit |
jrleja/bsfh | prospect/io/read_results.py | 3 | 20576 | import sys, os
from copy import deepcopy
import warnings
import pickle, json
import numpy as np
try:
import h5py
except:
pass
try:
from sedpy.observate import load_filters
except:
pass
"""Convenience functions for reading and reconstructing results from a fitting
run, including reconstruction of the m... | mit |
bataeves/kaggle | instacart/imba/arboretum_cv.py | 2 | 18971 | import gc
from concurrent.futures import ThreadPoolExecutor
import pandas as pd
import numpy as np
import os
import arboretum
import json
import sklearn.metrics
from sklearn.metrics import f1_score, roc_auc_score
from sklearn.model_selection import train_test_split
from scipy.sparse import dok_matrix, coo_matrix
from ... | unlicense |
AOtools/soapy | doc/source/conf.py | 4 | 13074 | # -*- coding: utf-8 -*-
#
# Soapy documentation build configuration file, created by
# sphinx-quickstart on Tue Apr 28 11:49:44 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All... | gpl-3.0 |
JsNoNo/scikit-learn | sklearn/utils/validation.py | 29 | 24630 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals i... | bsd-3-clause |
poryfly/scikit-learn | sklearn/cross_decomposition/cca_.py | 209 | 3150 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
akshaybabloo/Car-ND | Project_5/laneline.py | 1 | 21371 | import cv2
import glob
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from sklearn.metrics import mean_squared_error
x_cor = 9 #Number of corners to find
y_cor = 6
# Prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((y_cor*x_cor,3), np... | mit |
trankmichael/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 |
CalebBell/ht | ht/conv_free_immersed.py | 1 | 58245 | # -*- coding: utf-8 -*-
'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
Copyright (C) 2016, Caleb Bell <Caleb.Andrew.Bell@gmail.com>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal... | mit |
jayflo/scikit-learn | sklearn/cluster/tests/test_mean_shift.py | 121 | 3429 | """
Testing for mean shift clustering methods
"""
import numpy as np
import warnings
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import asser... | bsd-3-clause |
alexpeattie/wethepeopletoolkit | wethepeopletoolkit/clusterer.py | 1 | 2828 | import pandas
import numpy as np
import click
from bitstring import BitArray
from base58 import b58encode_int, b58decode_int
class Clusterer:
def __init__(self):
pass
def cluster(self, n, state_processor, pca = False, model_type = 'kmeans', z_score_exclude = 0.0, seed = None, quiet = False):
from sklearn.... | mit |
pbvarga1/qimage2ndarray | qimage2ndarray/__init__.py | 1 | 14973 | import sys as _sys
import numpy as _np
from .dynqt import QtGui as _qt
from .dynqt import qt as _qt_driver
if _qt_driver.name() == 'PythonQt':
from .qimageview import QImage2ndarray as _temp
_qimageview = _temp.qimageview
else:
from .qimageview_python import qimageview as _qimageview
__version__ = "1.5"
... | bsd-3-clause |
CDSFinance/zipline | zipline/utils/factory.py | 6 | 11228 | #
# 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 |
mortonjt/American-Gut | scripts/mod2_pcoa.py | 1 | 14487 | #!/usr/bin/env python
import os
import click
from matplotlib import use
use('Agg') # noqa
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from skbio import read, DistanceMatrix
from skbio.stats import isubsample
from skbio.stats.ordination import OrdinationResults
from col... | bsd-3-clause |
KarlTDebiec/Ramaplot | PDistDataset.py | 1 | 20943 | # -*- coding: utf-8 -*-
# ramaplot.PDistDataset.py
#
# Copyright (C) 2015 Karl T Debiec
# All rights reserved.
#
# This software may be modified and distributed under the terms of the
# BSD license. See the LICENSE file for details.
"""
Manages probability distribution datasets.
"""
##########################... | bsd-3-clause |
bikong2/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | 160 | 6028 | # 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 |
hhj0325/pystock | com/hhj/sogou/countByTime.py | 1 | 1082 | """
选取发布时间为2018年的文章，并对其进行月份统计
"""
import numpy as np
import pandas as pd
from pyecharts import Bar
df = pd.read_csv('sg_articles.csv', header=None, names=["title", "article", "name", "date"])
list1 = []
list2 = []
for j in df['date']:
# 获取文章发布年份及月份
time_1 = j.split('-')[0]
time_2 = j.split('-')[1]
lis... | apache-2.0 |
szarvas/anc-field | examples/ie224-impulse.py | 1 | 5152 | # -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal, stats
import sys
sys.path.append('..')
from anc_field_py.ancfield import *
from anc_field_py.ancutil import *
def add_microphones(ancObject):
# error_mic
ancObject.AddMic([4,1... | gpl-3.0 |
aerosara/thesis | notebooks_archive_10112014/pycse Examples.py | 1 | 2176 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <headingcell level=3>
# Example from pycse 1
# <codecell>
# copied from http://kitchingroup.cheme.cmu.edu/blog/tag/events/
from pycse import odelay
import matplotlib.pyplot as plt
import numpy as np
def ode(Y,x):
y1, y2 = Y
dy1dx = y2
dy2dx = -y1
... | mit |
victorpoughon/master-thesis | python/outlier_analysis.py | 1 | 1365 | #!/usr/bin/env python3
import os
import os.path
import sys
import numpy as np
import matplotlib.pyplot as plt
from features_common import match_angle, base_plot
def outlier_frequency_plot(path, angles, threshold):
f, ax = base_plot()
ax.plot(100 * np.cumsum(np.abs(angles) > threshold) / angles.size)
ax.s... | mit |
SamHames/scikit-image | skimage/viewer/canvastools/base.py | 1 | 5472 | import numpy as np
try:
from matplotlib import lines
except ImportError:
pass
__all__ = ['CanvasToolBase', 'ToolHandles']
def _pass(*args):
pass
class CanvasToolBase(object):
"""Base canvas tool for matplotlib axes.
Parameters
----------
ax : :class:`matplotlib.axes.Axes`
Matpl... | bsd-3-clause |
abele/bokeh | bokeh/charts/tests/test_data_adapter.py | 37 | 3285 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
kaichogami/sympy | sympy/physics/quantum/state.py | 58 | 29186 | """Dirac notation for states."""
from __future__ import print_function, division
from sympy import (cacheit, conjugate, Expr, Function, integrate, oo, sqrt,
Tuple)
from sympy.core.compatibility import u, range
from sympy.printing.pretty.stringpict import stringPict
from sympy.physics.quantum.qexpr ... | bsd-3-clause |
acimmarusti/isl_exercises | chap4/chap4ex13.py | 1 | 5743 | from __future__ import print_function, division
import matplotlib.pyplot as plt
import numpy as np
import scipy
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_mod... | gpl-3.0 |
hlin117/statsmodels | statsmodels/examples/ex_kernel_regression_dgp.py | 34 | 1202 | # -*- coding: utf-8 -*-
"""
Created on Sun Jan 06 09:50:54 2013
Author: Josef Perktold
"""
from __future__ import print_function
if __name__ == '__main__':
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.nonparametric.api import KernelReg
import statsmodels.sandbox.nonparametric... | bsd-3-clause |
eteq/bokeh | examples/interactions/interactive_bubble/data.py | 49 | 1265 | import numpy as np
from bokeh.palettes import Spectral6
def process_data():
from bokeh.sampledata.gapminder import fertility, life_expectancy, population, regions
# Make the column names ints not strings for handling
columns = list(fertility.columns)
years = list(range(int(columns[0]), int(columns[-... | bsd-3-clause |
bnaul/scikit-learn | sklearn/neighbors/_classification.py | 2 | 23284 | """Nearest Neighbor Classification"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck
# Multi-output support by Arnaud Joly <a.joly@ul... | bsd-3-clause |
eickenberg/scikit-learn | sklearn/cluster/bicluster/spectral.py | 1 | 19540 | """Implements spectral biclustering algorithms.
Authors : Kemal Eren
License: BSD 3 clause
"""
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.sparse import dia_matrix
from scipy.sparse import issparse
from sklearn.base import BaseEstimator, BiclusterMixin
from sklearn.externals import six
fr... | bsd-3-clause |
Adai0808/BuildingMachineLearningSystemsWithPython | ch07/predict10k_en.py | 22 | 2428 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
import numpy as np
from sklearn.datasets import load_svmlight_file
from sklearn.cross_validation import... | mit |
kernc/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 43 | 24671 | """
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 numpy.testing import run_module_suite
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from... | bsd-3-clause |
rexshihaoren/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 |
zhoulingjun/zipline | tests/test_algorithm_gen.py | 18 | 7339 | #
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
dean0x7d/pybinding | pybinding/support/collections.py | 1 | 3143 | import numpy as np
from matplotlib.collections import Collection
from matplotlib.artist import allow_rasterization
# noinspection PyAbstractClass
class CircleCollection(Collection):
"""Custom circle collection
The default matplotlib `CircleCollection` creates circles based on their
area in screen units. ... | bsd-2-clause |
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