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 |
|---|---|---|---|---|---|
CalvinNeo/PyGeo | countpca_segmentation_2.py | 1 | 7423 | #coding:utf8
import numpy as np, scipy
import pylab as pl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import math
from matplotlib import cm
from matplotlib import mlab
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from itertools import *
import collections
from multiproces... | apache-2.0 |
fmacias64/deap | examples/es/cma_plotting.py | 12 | 4326 | # This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed ... | lgpl-3.0 |
asnorkin/parapapapam | ensemble/_ensemble.py | 1 | 10880 | import numpy as np
from sklearn.model_selection import cross_val_predict, cross_val_score, StratifiedKFold
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.svm import SVC
from ..metrics import METRICS
class Blender:
def make_greedy_blend(self, X, y, models, scoring=None, cv=3,
... | mit |
jjx02230808/project0223 | examples/linear_model/plot_lasso_and_elasticnet.py | 73 | 2074 | """
========================================
Lasso and Elastic Net for Sparse Signals
========================================
Estimates Lasso and Elastic-Net regression models on a manually generated
sparse signal corrupted with an additive noise. Estimated coefficients are
compared with the ground-truth.
"""
print(... | bsd-3-clause |
skearnes/color-features | paper/code/analysis.py | 1 | 12160 | """Analyze results.
Use the saved model output to calculate AUC and other metrics.
"""
import collections
import cPickle as pickle
import gflags as flags
import gzip
import logging
import numpy as np
import os
import pandas as pd
from sklearn import metrics
from statsmodels.stats import proportion
import sys
flags.D... | bsd-3-clause |
manuelli/director | src/python/director/planplayback.py | 1 | 7857 | import os
import vtkAll as vtk
import math
import time
import re
import numpy as np
from director.timercallback import TimerCallback
from director import objectmodel as om
from director.simpletimer import SimpleTimer
from director.utime import getUtime
from director import robotstate
import copy
import pickle
import ... | bsd-3-clause |
nelango/ViralityAnalysis | model/lib/pandas/tests/test_msgpack/test_except.py | 15 | 1043 | #!/usr/bin/env python
# coding: utf-8
import unittest
import nose
import datetime
from pandas.msgpack import packb, unpackb
class DummyException(Exception):
pass
class TestExceptions(unittest.TestCase):
def test_raise_on_find_unsupported_value(self):
import datetime
self.assertRaises(TypeEr... | mit |
druce/safewithdrawal_tensorflow | lifetable.py | 1 | 8359 | import numpy as np
import pandas as pd
from pandas import DataFrame
############################################################
# Life tables
# https://www.ssa.gov/oact/STATS/table4c6.html
############################################################
############################################################
# Male... | mit |
ianctse/pvlib-python | pvlib/test/test_clearsky.py | 1 | 6604 | import logging
pvl_logger = logging.getLogger('pvlib')
import numpy as np
import pandas as pd
from nose.tools import raises
from numpy.testing import assert_almost_equal
from pandas.util.testing import assert_frame_equal, assert_series_equal
from pvlib.location import Location
from pvlib import clearsky
from pvlib i... | bsd-3-clause |
akloster/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 |
opendatadurban/scoda | scoda/public.py | 1 | 54121 | import itertools
import operator
from sqlalchemy_searchable import search
from scoda.app import app
from flask import request, url_for, redirect, flash, make_response, session, render_template, jsonify, Response, \
send_file
from flask_security import current_user
from itertools import zip_longest
from sqlalchemy... | apache-2.0 |
mwindau/praktikum | v351/dreieck.py | 1 | 1188 | import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import linregress
from scipy.optimize import curve_fit
oberwelle3, amplitude3 = np.genfromtxt('Rohdaten/dreieckspannung.txt',unpack=True)
plt.plot(oberwelle3, amplitude3,'k.',label="Messdaten")
#plt.legend(loc='best')
plt.grid()
#plt.xlim(0,1.5)
plt.x... | mit |
dhruv13J/scikit-learn | examples/svm/plot_iris.py | 62 | 3251 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
ilo10/scikit-learn | examples/plot_johnson_lindenstrauss_bound.py | 134 | 7452 | """
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected in... | bsd-3-clause |
imatge-upc/saliency-2016-cvpr | shallow/train.py | 2 | 3064 | # add to kfkd.py
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet,BatchIterator
import os
import numpy as np
from sklearn.utils import shuffle
import cPickle as pickle
import matplotlib.pyplot as plt
import Image
import ImageOps
from scipy import misc
import... | mit |
chankeypathak/pandas-matplotlib-examples | Lesson 9/export.py | 1 | 1179 | import pandas as pd
from sqlalchemy import create_engine, MetaData, Table, select
# Parameters
TableName = "data"
DB = {
'drivername': 'mssql+pyodbc',
'servername': 'DAVID-THINK',
#'port': '5432',
#'username': 'lynn',
#'password': '',
'database': 'BizIntel',
'driver': 'SQL Server Native Cl... | mit |
gpersistence/tstop | python/persistence/PartitionData.py | 1 | 8153 | #TSTOP
#
#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, or
#(at your option) any later version.
#
#This program is distributed in the hope that it will be useful,
... | gpl-3.0 |
noelevans/sandpit | kaggle/washington_bike_share/knn_normalising.py | 1 | 3166 | import datetime
import logging
import math
import random
import pandas as pd
logging.basicConfig(level=logging.INFO)
INPUT_FIELDS = ('holiday', 'workingday', 'temp', 'atemp', 'humidity',
'windspeed', 'hour', 'day_of_year', 'day_of_week')
PERIODICS = ('hour', 'day_of_year', 'day_of_week')
RESULT_FIE... | mit |
wangmiao1981/spark | python/pyspark/pandas/tests/test_stats.py | 6 | 18881 | #
# 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 |
jtmorgan/hostbot | top_1000_report.py | 1 | 10578 | #! /usr/bin/env python
from datetime import datetime, timedelta
import hb_config
import json
import pandas as pd
import requests
from requests_oauthlib import OAuth1
from urllib import parse
rt_header = """== Popular articles {date7} to {date1} ==
Last updated on ~~~~~
{{| class="wikitable sortable"
!Rank
!Article
!... | mit |
jpautom/scikit-learn | examples/text/document_clustering.py | 230 | 8356 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
akosyakov/intellij-community | python/helpers/pydev/pydev_ipython/matplotlibtools.py | 52 | 5401 |
import sys
backends = {'tk': 'TkAgg',
'gtk': 'GTKAgg',
'wx': 'WXAgg',
'qt': 'Qt4Agg', # qt3 not supported
'qt4': 'Qt4Agg',
'osx': 'MacOSX'}
# We also need a reverse backends2guis mapping that will properly choose which
# GUI support to activate based on the... | apache-2.0 |
thaumos/ansible | hacking/aws_config/build_iam_policy_framework.py | 25 | 11861 | # Requires pandas, bs4, html5lib, and lxml
#
# Call script with the output from aws_resource_actions callback, e.g.
# python build_iam_policy_framework.py ['ec2:AuthorizeSecurityGroupEgress', 'ec2:AuthorizeSecurityGroupIngress', 'sts:GetCallerIdentity']
#
# The sample output:
# {
# "Version": "2012-10-17",
# "S... | gpl-3.0 |
evertonaleixo/tarp | DeepLearning/deep-belief-network-example.py | 1 | 1185 | # coding=utf-8
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.metrics.classification import accuracy_score
import numpy as np
from dbn import SupervisedDBNClassification
# Loading dataset
digits = load_digits()
X, Y = digits.data, digits.target
# Data sc... | apache-2.0 |
waterponey/scikit-learn | sklearn/datasets/tests/test_base.py | 13 | 8907 | import os
import shutil
import tempfile
import warnings
import numpy
from pickle import loads
from pickle import dumps
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn.datasets im... | bsd-3-clause |
hansonrobotics/chatbot | src/chatbot/stats.py | 1 | 3618 | import os
import logging
import pandas as pd
import glob
import re
import datetime as dt
from collections import Counter
logger = logging.getLogger('hr.chatbot.stats')
trace_pattern = re.compile(
r'../(?P<fname>.*), (?P<tloc>\(.*\)), (?P<pname>.*), (?P<ploc>\(.*\))')
def collect_history_data(history_dir, days):
... | mit |
uberdugo/mlia | Ch05/EXTRAS/plot2D.py | 7 | 1276 | '''
Created on Oct 6, 2010
@author: Peter
'''
from numpy import *
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import logRegres
dataMat,labelMat=logRegres.loadDataSet()
dataArr = array(dataMat)
weights = logRegres.stocGradAscent0(dataArr,labelMat)
n = shap... | gpl-3.0 |
potash/scikit-learn | examples/ensemble/plot_adaboost_regression.py | 311 | 1529 | """
======================================
Decision Tree Regression with AdaBoost
======================================
A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tr... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/doc/mpl_examples/api/scatter_piecharts.py | 6 | 1194 | """
This example makes custom 'pie charts' as the markers for a scatter plotqu
Thanks to Manuel Metz for the example
"""
import math
import numpy as np
import matplotlib.pyplot as plt
# first define the ratios
r1 = 0.2 # 20%
r2 = r1 + 0.4 # 40%
# define some sizes of the scatter marker
sizes = [60,80,120]
# ca... | mit |
mmottahedi/neuralnilm_prototype | scripts/e249.py | 2 | 3897 | from __future__ import print_function, division
import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import Net, RealApplianceSource, BLSTMLayer, DimshuffleLayer
from lasagne.nonlinearities import sigmoid, rectify
from lasagne.objectives import crossentropy, mse... | mit |
brodeau/aerobulk | python/plot_tests/plot_station_asf.py | 1 | 9926 | #!/usr/bin/env python
# -*- Mode: Python; coding: utf-8; indent-tabs-mode: nil; tab-width: 4 -*-
# Post-diagnostic of STATION_ASF / L. Brodeau, 2019
import sys
from os import path as path
#from string import replace
import math
import numpy as nmp
from netCDF4 import Dataset,num2date
import matplotlib as mpl
mpl.use... | gpl-3.0 |
CharlesShang/TFFRCNN | lib/roi_data_layer/minibatch.py | 5 | 8725 | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN network."""
impor... | mit |
robcarver17/pysystemtrade | sysproduction/reporting/trades_report.py | 1 | 16443 | from copy import copy
from collections import namedtuple
import datetime
import numpy as np
import pandas as pd
from syscore.genutils import transfer_object_attributes
from syscore.pdutils import make_df_from_list_of_named_tuple
from syscore.objects import header, table, body_text, arg_not_supplied, missing_data
fro... | gpl-3.0 |
alekz112/statsmodels | statsmodels/formula/tests/test_formula.py | 29 | 4647 | from statsmodels.compat.python import iteritems, StringIO
import warnings
from statsmodels.formula.api import ols
from statsmodels.formula.formulatools import make_hypotheses_matrices
from statsmodels.tools import add_constant
from statsmodels.datasets.longley import load, load_pandas
import numpy.testing as npt
from... | bsd-3-clause |
kubaszostak/gdal-dragndrop | osgeo/apps/Python27/Lib/site-packages/numpy/lib/twodim_base.py | 2 | 27339 | """ Basic functions for manipulating 2d arrays
"""
from __future__ import division, absolute_import, print_function
import functools
from numpy.core.numeric import (
absolute, asanyarray, arange, zeros, greater_equal, multiply, ones,
asarray, where, int8, int16, int32, int64, empty, promote_types, diagonal,
... | mit |
kaichogami/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
versae/DH2304 | data/arts1.py | 1 | 1038 | import numpy as np
import pandas as pd
arts = pd.DataFrame()
# Clean the dates so you only see numbers.
def clean_years(value):
result = value
chars_to_replace = ["c.", "©", ", CARCC", "no date", "n.d.", " SODRAC", ", CA", " CARCC", ""]
chars_to_split = ["-", "/"]
if isinstance(result, str):
... | mit |
john5223/airflow | airflow/hooks/hive_hooks.py | 17 | 14064 | from __future__ import print_function
from builtins import zip
from past.builtins import basestring
import csv
import logging
import subprocess
from tempfile import NamedTemporaryFile
from thrift.transport import TSocket
from thrift.transport import TTransport
from thrift.protocol import TBinaryProtocol
from hive_ser... | apache-2.0 |
ningchi/scikit-learn | sklearn/cluster/tests/test_spectral.py | 262 | 7954 | """Testing for Spectral Clustering methods"""
from sklearn.externals.six.moves import cPickle
dumps, loads = cPickle.dumps, cPickle.loads
import numpy as np
from scipy import sparse
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
JamesDickenson/aima-python | submissions/aartiste/myNN.py | 4 | 3659 | from sklearn import datasets
from sklearn.neural_network import MLPClassifier
import traceback
from submissions.aartiste import election
from submissions.aartiste import county_demographics
class DataFrame:
data = []
feature_names = []
target = []
target_names = []
trumpECHP = DataFrame()
'''
Extract... | mit |
breznak/NAB | nab/labeler.py | 8 | 16181 | # ----------------------------------------------------------------------
# Copyright (C) 2014-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/... | agpl-3.0 |
skoslowski/gnuradio | gr-filter/examples/fir_filter_fff.py | 3 | 3371 | #!/usr/bin/env python
#
# Copyright 2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# SPDX-License-Identifier: GPL-3.0-or-later
#
#
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from gnuradio import gr, filter
from gnuradio import... | gpl-3.0 |
kayak/fireant | fireant/queries/builder/dimension_latest_query_builder.py | 1 | 2026 | import pandas as pd
from fireant.dataset.fields import Field
from fireant.utils import (
alias_for_alias_selector,
immutable,
)
from fireant.queries.builder.query_builder import QueryBuilder, QueryException, add_hints
from fireant.queries.execution import fetch_data
class DimensionLatestQueryBuilder(QueryBui... | apache-2.0 |
anomam/pvlib-python | pvlib/tests/test_bifacial.py | 2 | 5958 | import pandas as pd
import numpy as np
from datetime import datetime
from pvlib.bifacial import pvfactors_timeseries, PVFactorsReportBuilder
from conftest import requires_pvfactors
import pytest
@requires_pvfactors
@pytest.mark.parametrize('run_parallel_calculations',
[False, True])
def test_... | bsd-3-clause |
sumspr/scikit-learn | benchmarks/bench_plot_ward.py | 290 | 1260 | """
Benchmark scikit-learn's Ward implement compared to SciPy's
"""
import time
import numpy as np
from scipy.cluster import hierarchy
import pylab as pl
from sklearn.cluster import AgglomerativeClustering
ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
n_samples = np.logspace(.5, 3, 9)
n_features = n... | bsd-3-clause |
cython-testbed/pandas | pandas/tests/groupby/test_function.py | 3 | 38905 | import pytest
import numpy as np
import pandas as pd
from pandas import (DataFrame, Index, compat, isna,
Series, MultiIndex, Timestamp, date_range)
from pandas.errors import UnsupportedFunctionCall
from pandas.util import testing as tm
import pandas.core.nanops as nanops
from string import ascii_lo... | bsd-3-clause |
ChanderG/scikit-learn | sklearn/neighbors/tests/test_kde.py | 208 | 5556 | import numpy as np
from sklearn.utils.testing import (assert_allclose, assert_raises,
assert_equal)
from sklearn.neighbors import KernelDensity, KDTree, NearestNeighbors
from sklearn.neighbors.ball_tree import kernel_norm
from sklearn.pipeline import make_pipeline
from sklearn.dataset... | bsd-3-clause |
manahl/arctic | tests/integration/tickstore/test_ts_read.py | 1 | 30190 | # -*- coding: utf-8 -*-
from datetime import datetime as dt
import numpy as np
import pandas as pd
import pytest
import six
from mock import patch, call, Mock
from numpy.testing.utils import assert_array_equal
from pandas import DatetimeIndex
from pandas.util.testing import assert_frame_equal
from pymongo import ReadP... | lgpl-2.1 |
q1ang/scikit-learn | sklearn/linear_model/bayes.py | 220 | 15248 | """
Various bayesian regression
"""
from __future__ import print_function
# Authors: V. Michel, F. Pedregosa, A. Gramfort
# License: BSD 3 clause
from math import log
import numpy as np
from scipy import linalg
from .base import LinearModel
from ..base import RegressorMixin
from ..utils.extmath import fast_logdet, p... | bsd-3-clause |
glennlive/gnuradio-wg-grc | gr-filter/examples/fir_filter_ccc.py | 47 | 4019 | #!/usr/bin/env python
#
# Copyright 2013 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 option)
# ... | gpl-3.0 |
ml-lab/pylearn2 | pylearn2/testing/skip.py | 49 | 1363 | """
Helper functions for determining which tests to skip.
"""
__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 nose.plugins.skip import SkipT... | bsd-3-clause |
charlesll/RamPy | legacy_code/IR_dec_comb.py | 1 | 6585 | # -*- coding: utf-8 -*-
"""
Created on Tue Jul 22 07:54:05 2014
@author: charleslelosq
Carnegie Institution for Science
"""
import sys
sys.path.append("/Users/charleslelosq/Documents/RamPy/lib-charles/")
import csv
import numpy as np
import scipy
import matplotlib
import matplotlib.gridspec as gridspec
from pylab ... | gpl-2.0 |
vybstat/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
leesavide/pythonista-docs | Documentation/matplotlib/mpl_examples/pylab_examples/multi_image.py | 12 | 2201 | #!/usr/bin/env python
'''
Make a set of images with a single colormap, norm, and colorbar.
It also illustrates colorbar tick labelling with a multiplier.
'''
from matplotlib.pyplot import figure, show, axes, sci
from matplotlib import cm, colors
from matplotlib.font_manager import FontProperties
from numpy import ami... | apache-2.0 |
yask123/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 |
github4ry/pathomx | pathomx/kernel_helpers.py | 2 | 3634 | import os
import sys
import numpy as np
import pandas as pd
import re
import io
from matplotlib.figure import Figure, AxesStack
from matplotlib.axes import Subplot
from mplstyler import StylesManager
import warnings
from . import displayobjects
from .utils import scriptdir, basedir
from IPython.core import display
f... | gpl-3.0 |
ankurankan/scikit-learn | examples/bicluster/bicluster_newsgroups.py | 42 | 7098 | """
================================================================
Biclustering documents with the Spectral Co-clustering algorithm
================================================================
This example demonstrates the Spectral Co-clustering algorithm on the
twenty newsgroups dataset. The 'comp.os.ms-windows... | bsd-3-clause |
gautam1168/tardis | tardis/io/model_reader.py | 5 | 7787 | #reading different model files
import numpy as np
from numpy import recfromtxt, genfromtxt
import pandas as pd
from astropy import units as u
import logging
# Adding logging support
logger = logging.getLogger(__name__)
from tardis.util import parse_quantity
class ConfigurationError(Exception):
pass
def read_d... | bsd-3-clause |
kagayakidan/scikit-learn | sklearn/manifold/tests/test_isomap.py | 226 | 3941 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
energyPATHWAYS/energyPATHWAYS | energyPATHWAYS/dispatch_maintenance.py | 1 | 7610 |
from pyomo.environ import *
import numpy as np
import util
import config as cfg
import pdb
import pandas as pd
import copy
import dispatch_budget
import logging
def surplus_capacity(model):
return model.surplus_capacity + model.peak_penalty * model.weight_on_peak_penalty
def define_penalty_to_preference_high_cos... | mit |
GermanRuizMarcos/Classical-Composer-Classification | code_10_1/classification.py | 1 | 30838 | '''
AUDIO CLASSICAL COMPOSER IDENTIFICATION BASED ON:
A SPECTRAL BANDWISE FEATURE-BASED SYSTEM
'''
import essentia
from essentia.standard import *
import glob
import numpy as np
import arff
from scipy import stats
import collections
import cv2
import matplotlib
import matplotlib.pyplot as plt
#### gabor filters
d... | gpl-3.0 |
sgenoud/scikit-learn | sklearn/mixture/tests/test_gmm.py | 3 | 12260 | import itertools
import unittest
import numpy as np
from numpy.testing import assert_array_equal, assert_array_almost_equal, \
assert_raises
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_matrix
rng = np.random.RandomState(0)
def test_sample_gaussian... | bsd-3-clause |
vickyting0910/opengeocoding | 2reinter.py | 1 | 3991 | import pandas as pd
import glob
import time
import numpy as num
inter=sorted(glob.glob('*****.csv'))
w='*****.xlsx'
table1=pd.read_excel(w, '*****', index_col=None, na_values=['NA']).fillna(0)
w='*****.csv'
tab=pd.read_csv(w).fillna(0)
tab.is_copy = False
pd.options.mode.chained_assignment = None
t1=time.time()
... | bsd-2-clause |
waynenilsen/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 |
rvbelefonte/Rockfish2 | rockfish2/extensions/cps/model.py | 1 | 3390 | """
Tools for working with Computer Programs in Seismology velocity models
"""
import os
import numpy as np
import datetime
import pandas as pd
from scipy.interpolate import interp1d
import matplotlib.pyplot as plt
from rockfish2 import logging
from rockfish2.models.profile import Profile
class CPSModel1d(Profile):
... | gpl-2.0 |
qiwsir/vincent | examples/map_examples.py | 11 | 6721 | # -*- coding: utf-8 -*-
"""
Vincent Map Examples
"""
#Build a map from scratch
from vincent import *
world_topo = r'world-countries.topo.json'
state_topo = r'us_states.topo.json'
lake_topo = r'lakes_50m.topo.json'
county_geo = r'us_counties.geo.json'
county_topo = r'us_counties.topo.json'
or_topo = r'or_counties.t... | mit |
AlvinPH/StockTool | StockTool/core.py | 1 | 7480 |
from . import helpers
import pandas as pd
import numpy as np
from pandas import DataFrame, Series
from pandas_datareader import data
from datetime import datetime, timedelta
import re
import os
import requests
import time
class StockInfo():
def __init__(self, StockNumber):
if isinstance(StockNumber, str)... | bsd-2-clause |
CVML/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 243 | 7461 | """
============================================================
Sparse recovery: feature selection for sparse linear models
============================================================
Given a small number of observations, we want to recover which features
of X are relevant to explain y. For this :ref:`sparse linear ... | bsd-3-clause |
Aasmi/scikit-learn | sklearn/cluster/tests/test_birch.py | 342 | 5603 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.l... | bsd-3-clause |
jorik041/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 |
r-rathi/error-control-coding | perf/plot-pegd.py | 1 | 1496 | import numpy as np
import matplotlib.pyplot as plt
from errsim import *
def label(d, pe, pb, n):
if pb is None:
pb = pe
label = 'd={} pe={} n={} BSC'.format(d, pe, n)
else:
label = 'd={} pe={} n={} pb={}'.format(d, pe, n, pb)
return label
def plot(pe, fpath=None):
fig, ax = pl... | mit |
glorizen/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/dviread.py | 69 | 29920 | """
An experimental module for reading dvi files output by TeX. Several
limitations make this not (currently) useful as a general-purpose dvi
preprocessor.
Interface::
dvi = Dvi(filename, 72)
for page in dvi: # iterate over pages
w, h, d = page.width, page.height, page.descent
for x,y,font,gl... | agpl-3.0 |
theoryno3/scikit-learn | examples/linear_model/plot_bayesian_ridge.py | 248 | 2588 | """
=========================
Bayesian Ridge Regression
=========================
Computes a Bayesian Ridge Regression on a synthetic dataset.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the coefficient
weights are slightly shift... | bsd-3-clause |
timqian/sms-tools | lectures/8-Sound-transformations/plots-code/sineModelFreqScale-orchestra.py | 21 | 2666 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
sys.path.append(os.path.join(os.path.dirname... | agpl-3.0 |
ssh0/growing-string | triangular_lattice/diecutting/result_count_on_edge.py | 1 | 9360 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
#
# written by Shotaro Fujimoto
# 2016-12-16
import matplotlib.pyplot as plt
# from mpl_toolkits.mplot3d.axes3d import Axes3D
import matplotlib.cm as cm
import numpy as np
import set_data_path
class Visualizer(object):
def __init__(self, subjects):
self.data_... | mit |
zhester/hzpy | examples/parseriff.py | 1 | 2368 | #!/usr/bin/env python
"""
Example RIFF (WAV contents) Data Parser
Sample data is written to a CSV file for analysis.
If matplotlib and numpy are available, signal plots (DFTs) are generated.
"""
import math
import os
import struct
import wave
try:
import matplotlib.pyplot as plot
import numpy
import nu... | bsd-2-clause |
ThomasSweijen/TPF | doc/sphinx/conf.py | 1 | 28022 | # -*- coding: utf-8 -*-
#
# Yade documentation build configuration file, created by
# sphinx-quickstart on Mon Nov 16 21:49:34 2009.
#
# 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 co... | gpl-2.0 |
xray/xray | xarray/core/options.py | 1 | 5201 | import warnings
DISPLAY_WIDTH = "display_width"
ARITHMETIC_JOIN = "arithmetic_join"
ENABLE_CFTIMEINDEX = "enable_cftimeindex"
FILE_CACHE_MAXSIZE = "file_cache_maxsize"
WARN_FOR_UNCLOSED_FILES = "warn_for_unclosed_files"
CMAP_SEQUENTIAL = "cmap_sequential"
CMAP_DIVERGENT = "cmap_divergent"
KEEP_ATTRS = "keep_attrs"
DIS... | apache-2.0 |
cloud-fan/spark | python/pyspark/pandas/tests/data_type_ops/test_binary_ops.py | 1 | 6682 | #
# 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 |
jvkersch/hsmmlearn | docs/conf.py | 1 | 9948 | # -*- coding: utf-8 -*-
#
# hsmmlearn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 1 17:33:24 2016.
#
# 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.
#
#... | gpl-3.0 |
panmari/tensorflow | tensorflow/examples/skflow/boston.py | 1 | 1485 | # 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 |
jmrozanec/white-bkg-classification | scripts/preprocessing.py | 1 | 1441 | #https://github.com/tflearn/tflearn/issues/180
from __future__ import division, print_function, absolute_import
import tflearn
from tflearn.data_utils import shuffle, to_categorical
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.la... | apache-2.0 |
dimonaks/siman | siman/functions.py | 1 | 29689 |
from __future__ import division, unicode_literals, absolute_import
import os, tempfile, copy, math, itertools, sys
import numpy as np
from operator import itemgetter
from itertools import product
try:
import scipy
except:
print('functions.py: no scipy, smoother() will not work()')
from siman import header
... | gpl-2.0 |
yuvrajsingh86/DeepLearning_Udacity | weight-initialization/helper.py | 153 | 3649 | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def hist_dist(title, distribution_tensor, hist_range=(-4, 4)):
"""
Display histogram of a TF distribution
"""
with tf.Session() as sess:
values = sess.run(distribution_tensor)
plt.title(title)
plt.hist(values, ... | mit |
ravenshooter/BA_Analysis | Preprocess.py | 1 | 5604 |
import numpy
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import scipy
import mdp
import csv
from thread import start_new_thread
import DataSet
from DataAnalysis import plot
from Main import getProjectPath
def readFileToNumpy(fileName):
reader=csv.reader(open(fileName,"rb"),delimiter=... | mit |
JosmanPS/scikit-learn | sklearn/linear_model/least_angle.py | 37 | 53448 | """
Least Angle Regression algorithm. See the documentation on the
Generalized Linear Model for a complete discussion.
"""
from __future__ import print_function
# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux
#
# License: BSD 3 ... | bsd-3-clause |
junwoo091400/MyCODES | Projects/FootPad_Logger/logged_data_analyzer_LSTM/RNN_LSTM.py | 1 | 2131 |
from __future__ import print_function, division
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import ipdb
def RNN_LSTM(batch_size_in = 5, total_len_in = 30000, pad_len_in = 5, backprop_len_in = 50, state_size_in = 10, num_class_in = 32):
# total_len_in = (backprop_len_in) * (num_batches... | gpl-3.0 |
AIML/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
a... | bsd-3-clause |
rohanp/scikit-learn | sklearn/model_selection/tests/test_validation.py | 20 | 27961 | """Test the validation module"""
from __future__ import division
import sys
import warnings
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.utils... | bsd-3-clause |
nmartensen/pandas | pandas/io/formats/common.py | 16 | 1094 | # -*- coding: utf-8 -*-
"""
Common helper methods used in different submodules of pandas.io.formats
"""
def get_level_lengths(levels, sentinel=''):
"""For each index in each level the function returns lengths of indexes.
Parameters
----------
levels : list of lists
List of values on for level... | bsd-3-clause |
phdowling/scikit-learn | sklearn/preprocessing/label.py | 137 | 27165 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Joel Nothman <joel.nothman@gmail.com>
# Hamzeh Alsalhi <ha258@cornell.edu>
# Licens... | bsd-3-clause |
xwolf12/scikit-learn | sklearn/linear_model/randomized_l1.py | 95 | 23365 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy.sparse import issparse
from scipy import spar... | bsd-3-clause |
joernhees/scikit-learn | sklearn/metrics/cluster/supervised.py | 25 | 31477 | """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>
# Arnaud Fouchet ... | bsd-3-clause |
MartinDelzant/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
eistre91/ThinkStats2 | code/timeseries.py | 66 | 18035 | """This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import pandas
import numpy as np
import statsmodels.formula.api as smf
import st... | gpl-3.0 |
hrjn/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 |
funbaker/astropy | astropy/table/tests/test_table.py | 2 | 69207 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
import gc
import sys
import copy
from io import StringIO
from collections import OrderedDict
import pytest
import numpy as np
from numpy.testing import assert_allclose
from ...io import fits
from ...tests.helper import (assert_fo... | bsd-3-clause |
boada/planckClusters | MOSAICpipe/bpz-1.99.3/bpz.py | 1 | 52171 | """
bpz: Bayesian Photo-Z estimation
Reference: Benitez 2000, ApJ, 536, p.571
Usage:
python bpz.py catalog.cat
Needs a catalog.columns file which describes the contents of catalog.cat
"""
from __future__ import print_function
from __future__ import division
from builtins import str
from builtins import ... | mit |
kylerbrown/scikit-learn | sklearn/feature_selection/tests/test_rfe.py | 209 | 11733 | """
Testing Recursive feature elimination
"""
import warnings
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_equal, assert_true
from scipy import sparse
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris,... | bsd-3-clause |
larsmans/scipy | scipy/stats/_discrete_distns.py | 6 | 21338 | #
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
from __future__ import division, print_function, absolute_import
from scipy import special
from scipy.special import entr, gammaln as gamln
from scipy.misc import logsumexp
from numpy import floor, ceil, log, exp, ... | bsd-3-clause |
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