repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
mmadsen/sklearn-mmadsen | sklearn_mmadsen/dnn/dnnestimators.py | 1 | 7032 | #!/usr/bin/env python
import numpy as np
import pprint as pp
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.metrics import accuracy_score
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam
from keras.regularizers im... | apache-2.0 |
SANBI-SA/tools-iuc | tools/cwpair2/cwpair2_util.py | 3 | 13741 | import bisect
import csv
import os
import sys
import traceback
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot # noqa: I202,E402
# Data outputs
DETAILS = 'D'
MATCHED_PAIRS = 'MP'
ORPHANS = 'O'
# Data output formats
GFF_EXT = 'gff'
TABULAR_EXT = 'tabular'
# Statistics historgrams output director... | mit |
EricSB/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/mlab.py | 69 | 104273 | """
Numerical python functions written for compatability with matlab(TM)
commands with the same names.
Matlab(TM) compatible functions
-------------------------------
:func:`cohere`
Coherence (normalized cross spectral density)
:func:`csd`
Cross spectral density uing Welch's average periodogram
:func:`detrend`... | agpl-3.0 |
crichardson17/starburst_atlas | Low_resolution_sims/Dusty_LowRes/Padova_inst/padova_inst_0/fullgrid/IR.py | 30 | 9364 | import csv
import matplotlib.pyplot as plt
from numpy import *
import scipy.interpolate
import math
from pylab import *
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.patches as patches
from matplotlib.path import Path
import os
# --------------------------------------------------... | gpl-2.0 |
niltonlk/nest-simulator | pynest/nest/tests/test_spatial/test_spatial_distributions.py | 7 | 30540 | # -*- coding: utf-8 -*-
#
# test_spatial_distributions.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of ... | gpl-2.0 |
nickgentoo/scikit-learn-graph | scripts/Keras_deep_calculate_cv_allkernels.py | 1 | 11280 | # -*- coding: utf-8 -*-
"""
Created on Fri Mar 13 13:02:41 2015
Copyright 2015 Nicolo' Navarin
This file is part of scikit-learn-graph.
scikit-learn-graph 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 ... | gpl-3.0 |
murphy214/berrl | berrl/pipewidgets.py | 2 | 31866 | '''
Module: pipehtml.py
A module to parse html for data in static html and for data to be updated in real time.
Created by: Bennett Murphy
email: murphy214@marshall.edu
'''
import json
import itertools
import os
from IPython.display import IFrame
import ipywidgets as widgets
from math import floor
import numpy as np... | apache-2.0 |
hsiaoyi0504/scikit-learn | sklearn/metrics/cluster/unsupervised.py | 230 | 8281 | """ Unsupervised evaluation metrics. """
# Authors: Robert Layton <robertlayton@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from ...utils import check_random_state
from ..pairwise import pairwise_distances
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random... | bsd-3-clause |
IDSIA/sacred | sacred/optional.py | 1 | 1570 | #!/usr/bin/env python
# coding=utf-8
import importlib
from sacred.utils import modules_exist
from sacred.utils import get_package_version, parse_version
def optional_import(*package_names):
try:
packages = [importlib.import_module(pn) for pn in package_names]
return True, packages[0]
except I... | mit |
ilyes14/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 213 | 3359 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
theoryno3/pylearn2 | pylearn2/cross_validation/tests/test_train_cv_extensions.py | 49 | 1681 | """
Tests for TrainCV extensions.
"""
import os
import tempfile
from pylearn2.config import yaml_parse
from pylearn2.testing.skip import skip_if_no_sklearn
def test_monitor_based_save_best_cv():
"""Test MonitorBasedSaveBestCV."""
handle, filename = tempfile.mkstemp()
skip_if_no_sklearn()
trainer = ya... | bsd-3-clause |
Mecanon/morphing_wing | experimental/comparison_model/actuator.py | 5 | 15329 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 15 17:27:40 2016
@author: Pedro Leal
"""
import math
from scipy.optimize import newton
import numpy as np
import matplotlib.pyplot as plt
class actuator():
"""
Actuator object where inputs:
- -(n): is a dictionary with keys 'x' and 'y', the coordinates of
... | mit |
mbkumar/pymatgen | pymatgen/util/tests/test_plotting.py | 4 | 1230 | import unittest
from pymatgen.util.plotting import periodic_table_heatmap, van_arkel_triangle
from pymatgen.util.testing import PymatgenTest
import matplotlib
class FuncTestCase(PymatgenTest):
def test_plot_periodic_heatmap(self):
random_data = {'Te': 0.11083818874391202, 'Au': 0.7575629917425387,
... | mit |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/sklearn/preprocessing/data.py | 1 | 67256 | # 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>
# Eric Martin <eric@ericmart.in>
# Giorgio Patrini <giorgio.patrini@anu.edu.au>
# Lic... | mit |
fbagirov/scikit-learn | examples/decomposition/plot_incremental_pca.py | 244 | 1878 | """
===============
Incremental PCA
===============
Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memo... | bsd-3-clause |
znes/HESYSOPT | hesysopt/restore_results.py | 1 | 1428 | # -*- coding: utf-8 -*-
"""
This module is used to configure the plotting. At the momemt it reads for
the default all results path and creates a multiindex dataframe. This is
used by the different plotting-modules. Also, colors are set here.
Note: This is rather ment to illustrate, how hesysopt results can be plotted... | gpl-3.0 |
ccauet/scikit-optimize | benchmarks/bench_ml.py | 1 | 15685 |
"""
This code implements benchmark for the black box optimization algorithms,
applied to a task of optimizing parameters of ML algorithms for the task
of supervised learning.
The code implements benchmark on 4 datasets where parameters for 6 classes
of supervised models are tuned to optimize performance on datasets. ... | bsd-3-clause |
datapythonista/pandas | pandas/tests/series/methods/test_dropna.py | 2 | 3488 | import numpy as np
import pytest
from pandas import (
DatetimeIndex,
IntervalIndex,
NaT,
Period,
Series,
Timestamp,
)
import pandas._testing as tm
class TestDropna:
def test_dropna_empty(self):
ser = Series([], dtype=object)
assert len(ser.dropna()) == 0
return_va... | bsd-3-clause |
ivastar/clear | grizli_reduction.py | 1 | 33227 | #!/home/rsimons/miniconda2/bin/python
import matplotlib
import time
import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
import drizzlepac
import grizli
import glob
from grizli import utils
import importlib
from grizli.prep import process_direct_grism_visit
#from hsaquery import quer... | mit |
mhue/scikit-learn | sklearn/utils/graph.py | 289 | 6239 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
xavierwu/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more impor... | bsd-3-clause |
ashtonwebster/tl_algs | tests/test_trbag.py | 1 | 2668 |
# coding: utf-8
import pandas as pd
from sklearn.datasets.samples_generator import make_blobs
from sklearn.ensemble import RandomForestClassifier
import random
from tl_algs import peters, weighted, trbag, tl_baseline, burak
RAND_SEED = 2016
random.seed(RAND_SEED) # change this to see new random data!
# randomly gen... | mit |
mbeyeler/pulse2percept | pulse2percept/datasets/nanduri2012.py | 1 | 3284 | """`load_nanduri2012`"""
from os.path import dirname, join
import numpy as np
try:
import pandas as pd
has_pandas = True
except ImportError:
has_pandas = False
def load_nanduri2012(electrodes=None, task=None, shuffle=False, random_state=0):
"""Load data from [Nanduri2012]_
Load the threshold dat... | bsd-3-clause |
CforED/Machine-Learning | examples/neighbors/plot_regression.py | 349 | 1402 | """
============================
Nearest Neighbors regression
============================
Demonstrate the resolution of a regression problem
using a k-Nearest Neighbor and the interpolation of the
target using both barycenter and constant weights.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@... | bsd-3-clause |
google-research/google-research | xirl/xirl/evaluators/emb_visualizer.py | 1 | 2628 | # coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# 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 applicab... | apache-2.0 |
zycdragonball/tensorflow | tensorflow/python/estimator/canned/dnn_test.py | 20 | 16058 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
calebfoss/tensorflow | tensorflow/contrib/learn/python/learn/estimators/__init__.py | 6 | 11427 | # 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 |
meduz/scikit-learn | benchmarks/bench_glmnet.py | 111 | 3890 | """
To run this, you'll need to have installed.
* glmnet-python
* scikit-learn (of course)
Does two benchmarks
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of... | bsd-3-clause |
themrmax/scikit-learn | examples/feature_selection/plot_feature_selection_pipeline.py | 58 | 1049 | """
==================
Pipeline Anova SVM
==================
Simple usage of Pipeline that runs successively a univariate
feature selection with anova and then a C-SVM of the selected features.
"""
from sklearn import svm
from sklearn.datasets import samples_generator
from sklearn.feature_selection import SelectKBest,... | bsd-3-clause |
kirel/political-affiliation-prediction | partyprograms.py | 2 | 3428 | # -*- coding: utf-8 -*-
import re
import cPickle
from classifier import Classifier
import json
from scipy import ones,argmax
from sklearn.metrics import classification_report,confusion_matrix
def partyprograms(folder='model'):
clf = Classifier(folder=folder)
# converted with pdftotext
text = {}
bow = {... | mit |
yask123/scikit-learn | examples/cluster/plot_mean_shift.py | 351 | 1793 | """
=============================================
A demo of the mean-shift clustering algorithm
=============================================
Reference:
Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
feature space analysis". IEEE Transactions on Pattern Analysis and
Machine Intelligence. 2002. ... | bsd-3-clause |
wazeerzulfikar/scikit-learn | sklearn/learning_curve.py | 8 | 15418 | """Utilities to evaluate models with respect to a variable
"""
# Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import is_classifier, clone
from .cross_validation import check_cv
from .externals.joblib import Parallel, delayed
fro... | bsd-3-clause |
okadate/romspy | romspy/tplot/tplot_param.py | 1 | 4044 | # coding: utf-8
# (c) 2016-01-27 Teruhisa Okada
import netCDF4
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
from matplotlib.offsetbox import AnchoredText
import numpy as np
import pandas as pd
import glob
import romspy
def tplot_param(inifiles, vname, ax=plt.gca()):
for inifile in i... | mit |
sebalander/trilateration | trilatera.py | 1 | 11639 | '''
practicar trilateracion
'''
# %%
import numpy as np
import numpy.linalg as ln
import matplotlib.pyplot as plt
import numdifftools as ndf
from scipy.special import chdtri
# %%
kml_file = "/home/sebalander/Code/VisionUNQextra/trilateration/trilat.kml"
# %%
texto = open(kml_file, 'r').read()
names = list()
data... | gpl-2.0 |
mjgrav2001/scikit-learn | sklearn/metrics/ranking.py | 75 | 25426 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
ssaeger/scikit-learn | examples/ensemble/plot_adaboost_multiclass.py | 354 | 4124 | """
=====================================
Multi-class AdaBoosted Decision Trees
=====================================
This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can
improve prediction accuracy on a multi-class problem. The classification
dataset is constructed by taking a ten-dimensional ... | bsd-3-clause |
AdityaSoni19031997/Machine-Learning | Classifying_datasets/MNIST/neural_networks.py | 1 | 4282 | '''
Using ANN Classifying Handwritten Digits
My first attempt to build a neural network....for evaluating the famous MNIST Digit Classification"
-Aditya Soni
'''
#Import Statements
import numpy as np # for fast calculations
import matplotlib.pyplot as plt # for plotiing
import scipy.special # for sigmoid f... | mit |
godrayz/trading-with-python | lib/csvDatabase.py | 77 | 6045 | # -*- coding: utf-8 -*-
"""
intraday data handlers in csv format.
@author: jev
"""
from __future__ import division
import pandas as pd
import datetime as dt
import os
from extra import ProgressBar
dateFormat = "%Y%m%d" # date format for converting filenames to dates
dateTimeFormat = "%Y%m%d %H:%M:%S"... | bsd-3-clause |
madjelan/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 387 | 2060 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
cgre-aachen/gempy | gempy/utils/extract_geomodeller_data.py | 1 | 9895 |
from pylab import *
import copy
import pandas as pn
import gempy as gp
import numpy as np
try:
import xml.etree.cElementTree as ET
except ImportError:
import xml.etree.ElementTree as ET
class ReadGeoModellerXML:
def __init__(self, fp):
"""
Reads in and parses a GeoModeller XML file to ex... | lgpl-3.0 |
followthesheep/galpy | galpy/snapshot_src/Snapshot.py | 2 | 13530 | import numpy as nu
from galpy.orbit import Orbit
from galpy.potential_src.planarPotential import RZToplanarPotential
import galpy.util.bovy_plot as plot
from directnbody import direct_nbody
class Snapshot(object):
"""General snapshot = collection of particles class"""
def __init__(self,*args,**kwargs):
... | bsd-3-clause |
mjvakili/gambly | code/tests/test_data.py | 1 | 5063 | '''
testing how the model fits the data
'''
from __future__ import (division, print_function, absolute_import,
unicode_literals)
import numpy as np
import matplotlib.pyplot as plt
import os.path as path
import time
from Corrfunc import _countpairs
from Corrfunc.utils import read_catalog
# --- Lo... | mit |
nvoron23/scikit-learn | sklearn/grid_search.py | 61 | 37197 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
marcocaccin/scikit-learn | sklearn/svm/classes.py | 6 | 40597 | import warnings
import numpy as np
from .base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
LinearModel
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import check_X... | bsd-3-clause |
mikeireland/opticstools | playground/nuller_with_phase.py | 1 | 1258 | """
The CSV input files came from WebPlotDigitizer and Harry's plots.
"""
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as op
imbalance = np.genfromtxt('harry_imbalance.csv', delimiter=',')
phase_deg = np.genfromtxt('harry_phase.csv', delimiter=',')
#Wavelength range
wave = np.linspace(3.7,... | mit |
henrykironde/scikit-learn | sklearn/cluster/setup.py | 263 | 1449 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
cblas_libs, blas_info = ... | bsd-3-clause |
plissonf/scikit-learn | sklearn/cluster/bicluster.py | 211 | 19443 | """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 . import KMeans, MiniBatchKMeans
from ..base import BaseEstimator, BiclusterMixin
from ..external... | bsd-3-clause |
jmorton/yatsm | yatsm/classifiers/diagnostics.py | 1 | 7252 | import logging
import numpy as np
import scipy.ndimage
from sklearn.utils import check_random_state
# from sklearn.cross_validation import KFold, StratifiedKFold
logger = logging.getLogger('yatsm')
def kfold_scores(X, y, algo, kf_generator):
""" Performs KFold crossvalidation and reports mean/std of scores
... | mit |
certik/hermes1d | examples/system_neutronics_fixedsrc2/plot.py | 3 | 1210 | import matplotlib.pyplot as plt
import numpy as np
# material data
Q = [0.0, 1.5, 1.8, 1.5, 1.8, 1.8, 1.5]
D1 = 7*[1.2]
D2 = 7*[0.4]
S1 = 7*[0.03]
S2 = [0.1, 0.2, 0.25, 0.2, 0.25, 0.25, 0.2]
S12= [0.02] + 6*[0.015]
nSf1 = [0.005] + 6*[0.0075]
nSf2 = 7*[0.1]
fig = plt.figure()
# one axes for each group
ax1 = fig.ad... | bsd-3-clause |
makelove/OpenCV-Python-Tutorial | ch15-图像阈值/15.简单阈值threshold.py | 1 | 1439 | # -*- coding: utf-8 -*-
'''
简单阈值
像素值高于阈值时 我们给这个像素 赋予一个新值， 可能是白色 ，
否则我们给它赋予另外一种颜色， 或是黑色 。
这个函数就是 cv2.threshhold()。
这个函数的第一个参数就是原图像
原图像应 是灰度图。
第二个参数就是用来对像素值进行分类的阈值。
第三个参数 就是当像素值高于， 有时是小于 阈值时应该被赋予的新的像素值。
OpenCV 提供了多种不同的阈值方法 ， 是由第四个参数来决定的。
些方法包括
• cv2.THRESH_BINARY
• cv2.THRESH_BINARY_INV • cv2.THRESH_TRUNC
• cv2... | mit |
nmayorov/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 65 | 2838 | # 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 |
pystockhub/book | ch18/day03/Kiwoom.py | 2 | 8383 | import sys
from PyQt5.QtWidgets import *
from PyQt5.QAxContainer import *
from PyQt5.QtCore import *
import time
import pandas as pd
import sqlite3
TR_REQ_TIME_INTERVAL = 0.2
class Kiwoom(QAxWidget):
def __init__(self):
super().__init__()
self._create_kiwoom_instance()
self._set_signal_sl... | mit |
mattjj/pyhsmm-factorial | example.py | 2 | 2924 | from __future__ import division
import numpy as np
np.seterr(divide='ignore')
from matplotlib import pyplot as plt
import pyhsmm
from pyhsmm.util.text import progprint_xrange
import models
import util as futil
T = 400
Nmax = 10
# observation distributions used to generate data
true_obsdistns_chain1 = [
pyhs... | mit |
wood-b/CompBook | project1/sectD/ete_hist.py | 2 | 1207 | import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from scipy import stats
__author__ = "Brandon Wood"
file = open(sys.argv[1])
HEADER_LINES = 0
for x in range(HEADER_LINES):
file.readline()
ete_list = []
for line in file:
tokens = line.split()
ete_list.append(f... | bsd-3-clause |
joshloyal/scikit-learn | sklearn/datasets/tests/test_mldata.py | 384 | 5221 | """Test functionality of mldata fetching utilities."""
import os
import shutil
import tempfile
import scipy as sp
from sklearn import datasets
from sklearn.datasets import mldata_filename, fetch_mldata
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.test... | bsd-3-clause |
moonbury/pythonanywhere | github/MasteringMLWithScikit-learn/8365OS_07_Codes/pca-3d-plot.py | 3 | 1392 | import matplotlib
matplotlib.use('Qt4Agg')
import numpy as np
import pylab as pl
from mpl_toolkits.mplot3d import Axes3D
from sklearn import decomposition
from sklearn import datasets
np.random.seed(5)
centers = [[1, 1], [-1, -1], [1, -1]]
iris = datasets.load_iris()
X = iris.data
y = iris.target
fig = pl.figure(... | gpl-3.0 |
meduz/scikit-learn | examples/ensemble/plot_forest_iris.py | 335 | 6271 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
... | bsd-3-clause |
ky822/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 |
heplesser/nest-simulator | pynest/examples/spatial/ctx_2n.py | 20 | 2192 | # -*- coding: utf-8 -*-
#
# ctx_2n.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of the License, or
# (a... | gpl-2.0 |
lilleswing/deepchem | deepchem/data/datasets.py | 1 | 99890 | """
Contains wrapper class for datasets.
"""
import json
import os
import math
import random
import logging
import tempfile
import time
import shutil
import multiprocessing
from ast import literal_eval as make_tuple
from typing import Any, Dict, Iterable, Iterator, List, Optional, Sequence, Tuple, Union
import numpy a... | mit |
tom-f-oconnell/multi_tracker | scripts/hdf5_to_csv.py | 1 | 1208 | #!/usr/bin/env python
from __future__ import print_function
from os.path import join, splitext
import glob
import pandas as pd
#import matplotlib.pyplot as plt
import multi_tracker_analysis as mta
def main():
#experiment_dir = 'choice_20210129_162648'
experiment_dir = '.'
def find_file_via_suffix(suf... | mit |
ml-lab/neuralnilm | neuralnilm/data/stridesource.py | 4 | 6512 | from __future__ import print_function, division
from copy import copy
from datetime import timedelta
import numpy as np
import pandas as pd
import nilmtk
from nilmtk.timeframegroup import TimeFrameGroup
from nilmtk.timeframe import TimeFrame
from neuralnilm.data.source import Sequence
from neuralnilm.utils import check... | apache-2.0 |
cduvedi/CS229-project | feature_extraction/eigen_faces_refactored.py | 1 | 5401 | import os
import csv
import numpy
import scipy
import random
import pylab as pl
from scipy import linalg
from scipy.misc import toimage
from sklearn.cross_validation import train_test_split
from sklearn.datasets import fetch_lfw_people
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classifica... | gpl-2.0 |
sgrieve/LH_Paper_Plotting | Plotting_Code/Figure_8_revision.py | 1 | 5620 | # -*- coding: utf-8 -*-
"""
Copyright (C) 2015 Stuart W.D Grieve 2015
Developer can be contacted by s.grieve _at_ ed.ac.uk
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 2 of the Lic... | gpl-2.0 |
arcyfelix/Machine-Learning-For-Trading | 34_correlation.py | 1 | 2422 | import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
''' Read: http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-stats '''
def symbol_to_path(symbol, base_dir = 'data'):
return os.path.join(base_dir, "{}.csv".format(str(symbol)))
def dates_creator():
... | apache-2.0 |
nhejazi/scikit-learn | sklearn/manifold/t_sne.py | 3 | 35216 | # Author: Alexander Fabisch -- <afabisch@informatik.uni-bremen.de>
# Author: Christopher Moody <chrisemoody@gmail.com>
# Author: Nick Travers <nickt@squareup.com>
# License: BSD 3 clause (C) 2014
# This is the exact and Barnes-Hut t-SNE implementation. There are other
# modifications of the algorithm:
# * Fast Optimi... | bsd-3-clause |
ilyes14/scikit-learn | sklearn/cluster/tests/test_hierarchical.py | 230 | 19795 | """
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 |
julienmalard/Tinamit | tinamit/geog/mapa.py | 1 | 14476 | import os
import matplotlib.pyplot as dib
import numpy as np
import shapefile as sf
from matplotlib import colors, cm
from matplotlib.axes import Axes
from matplotlib.backends.backend_agg import FigureCanvasAgg as TelaFigura
from matplotlib.figure import Figure as Figura
from tinamit.config import _
from ..mod import... | gpl-3.0 |
IndraVikas/scikit-learn | examples/cluster/plot_ward_structured_vs_unstructured.py | 320 | 3369 | """
===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================
Example builds a swiss roll dataset and runs
hierarchical clustering on their position.
For more information, see :ref:`hierarchical_clus... | bsd-3-clause |
fnielsen/dasem | dasem/data.py | 1 | 1750 | """Data.
Functions to read datasets from the data subdirectory.
"""
from os.path import join, split
from pandas import read_csv
def four_words():
"""Read and return four words odd-one-out dataset.
Returns
-------
>>> df = four_words()
>>> df.ix[0, 'word4'] == 'stol'
True
"""
file... | apache-2.0 |
chrisburr/scikit-learn | sklearn/tests/test_kernel_approximation.py | 244 | 7588 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn.utils.testing import assert_array_equal, assert_equal, assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_array_almost_equal, assert_raises
from sklearn.utils.testing import assert_less_equal
from ... | bsd-3-clause |
hugobowne/scikit-learn | examples/cluster/plot_face_segmentation.py | 71 | 2839 | """
===================================================
Segmenting the picture of a raccoon face in regions
===================================================
This example uses :ref:`spectral_clustering` on a graph created from
voxel-to-voxel difference on an image to break this image into multiple
partly-homogeneous... | bsd-3-clause |
ulisespereira/LearningSequences | Plasticity/popModel/sequence_norm_bf_adaptation.py | 1 | 5722 | import numpy as np
from scipy import sparse
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import math as mt
from stimulus import *
from myintegrator import *
import cProfile
import json
# this is the transfer function
def phi(x,theta,uc):
myresult=nu*(x-theta)
myresult[x<theta]=0.
myresult[x>uc... | gpl-2.0 |
justincassidy/scikit-learn | examples/cluster/plot_kmeans_assumptions.py | 270 | 2040 | """
====================================
Demonstration of k-means assumptions
====================================
This example is meant to illustrate situations where k-means will produce
unintuitive and possibly unexpected clusters. In the first three plots, the
input data does not conform to some implicit assumptio... | bsd-3-clause |
0todd0000/spm1d | spm1d/examples/nonparam/1d/roi/ex_anova2.py | 1 | 1477 |
import numpy as np
from matplotlib import pyplot
import spm1d
#(0) Load dataset:
dataset = spm1d.data.uv1d.anova2.SPM1D_ANOVA2_2x2()
# dataset = spm1d.data.uv1d.anova2.SPM1D_ANOVA2_2x3()
# dataset = spm1d.data.uv1d.anova2.SPM1D_ANOVA2_3x3()
# dataset = spm1d.data.uv1d.anova2.SPM1D_ANOVA2_3x4()
... | gpl-3.0 |
ZenDevelopmentSystems/scikit-learn | examples/linear_model/plot_ridge_path.py | 254 | 1655 | """
===========================================================
Plot Ridge coefficients as a function of the regularization
===========================================================
Shows the effect of collinearity in the coefficients of an estimator.
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regressi... | bsd-3-clause |
Ghalko/osf.io | scripts/analytics/utils.py | 30 | 1244 | # -*- coding: utf-8 -*-
import os
import unicodecsv as csv
from bson import ObjectId
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import requests
from website import util
def oid_to_datetime(oid):
return ObjectId(oid).generation_time
def mkdirp(path):
try:
os.makedirs(path)
... | apache-2.0 |
vovojh/gem5 | util/stats/barchart.py | 90 | 12472 | # Copyright (c) 2005-2006 The Regents of The University of Michigan
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above copyright
# notice, this ... | bsd-3-clause |
caidongyun/pylearn2 | pylearn2/scripts/datasets/browse_norb.py | 44 | 15741 | #!/usr/bin/env python
"""
A browser for the NORB and small NORB datasets. Navigate the images by
choosing the values for the label vector. Note that for the 'big' NORB
dataset, you can only set the first 5 label dimensions. You can then cycle
through the 3-12 images that fit those labels.
"""
import sys
import os
imp... | bsd-3-clause |
ChanderG/scikit-learn | benchmarks/bench_plot_fastkmeans.py | 294 | 4676 | from __future__ import print_function
from collections import defaultdict
from time import time
import numpy as np
from numpy import random as nr
from sklearn.cluster.k_means_ import KMeans, MiniBatchKMeans
def compute_bench(samples_range, features_range):
it = 0
results = defaultdict(lambda: [])
chun... | bsd-3-clause |
happyx2/asspy | asspy/lexrank.py | 1 | 6324 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import getopt
import codecs
import collections
import numpy
import networkx
import re
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import pairwise_distances
import tools
from misc.divrank import divrank, divrank_scipy
from nltk.tok... | mit |
manpen/hypergen | libs/NetworKit/scripts/DynamicBetweennessExperiments_fixed_batch.py | 3 | 4514 | from networkit import *
from networkit.dynamic import *
from networkit.centrality import *
import pandas as pd
import random
def isConnected(G):
cc = properties.ConnectedComponents(G)
cc.run()
return (cc.numberOfComponents() == 1)
def removeAndAddEdges(G, nEdges, tabu=None):
if nEdges > G.numberOfEdges() - tabu.... | gpl-3.0 |
rahlk/RAAT | src/tools/Discretize.py | 2 | 4964 | """
An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by Fayyad & Irani's MDL method (the default).
For more information, see:
Usama M. Fayyad, Keki B. Irani: Multi-interval discretization of continuous valued attributes for classification lear... | mit |
theoryno3/scikit-learn | 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_... | bsd-3-clause |
hadmack/pyoscope | tests/display_rigol.py | 1 | 1116 | #!/usr/bin/env python
#
# PyUSBtmc
# display_channel.py
#
# Copyright (c) 2011 Mike Hadmack
# Copyright (c) 2010 Matt Mets
# This code is distributed under the MIT license
#
# This script is just to test rigolscope functionality as a module
#
import numpy
from matplotlib import pyplot
import sys
import os
sys.path.app... | mit |
dorianprill/CBIRjpg | plot.py | 1 | 5140 | #!/usr/bin/python3
import pickle
import matplotlib
import argparse
from itertools import product
matplotlib.use("Agg")
import matplotlib.pyplot as plt
matplotlib.style.use("ggplot")
matplotlib.rcParams.update({"font.size" : 10})
def getAvailableValues(parameter):
return sorted(list(set(r[parameter] for r in res... | gpl-3.0 |
jasonfrowe/Kepler | example/transitfit5.py | 1 | 11384 | import numpy as np
from numpy import zeros
from numpy import ones
import tfit5
import fittransitmodel as ftf
import matplotlib #ploting
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import math #used for floor command
def readtt(*files):
"reading in TT files"
nmax=0 #we will first scan through the... | gpl-3.0 |
nwjs/chromium.src | tools/perf/core/external_modules.py | 10 | 1614 | # Copyright 2019 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Allow importing external modules which may be missing in some platforms.
These modules are normally provided by the vpython environment manager. But
some... | bsd-3-clause |
mbkumar/pymatgen | dev_scripts/chemenv/strategies/multi_weights_strategy_parameters.py | 5 | 15844 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
Script to visualize the model coordination environments
"""
__author__ = "David Waroquiers"
__copyright__ = "Copyright 2012, The Materials Project"
__version__ = "2.0"
__maintainer__ = "David Waroquiers"
_... | mit |
nojero/pod | src/neg/units.py | 1 | 7695 | #!/usr/bin/python
from net import *
from z3 import *
from time import time
from matrix import *
import pandas as pd
def z3Pair(x,y):
assert(isinstance(x, Place) and isinstance(y, Place))
return Int(str(repr(x)) + "-" + str(repr(y)))
def z3Int(x):
assert(isinstance(x, Place))
return Int(... | gpl-3.0 |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/tests/series/test_replace.py | 8 | 7896 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import numpy as np
import pandas as pd
import pandas.lib as lib
import pandas.util.testing as tm
from .common import TestData
class TestSeriesReplace(TestData, tm.TestCase):
_multiprocess_can_split_ = True
def test_replace(self):
N = 100
ser... | gpl-3.0 |
castelao/CoTeDe | tests/qctests/test_qc_gradient.py | 1 | 1641 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
"""
import numpy as np
from numpy import ma
from cotede.qctests.gradient import curvature, _curvature_pandas
from cotede.qctests import Gradient
from ..data import DummyData
from .compare import compare_feature_input_types, ... | bsd-3-clause |
dreadjesus/MachineLearning | NaturalLanguageProcessing/ham_spam.py | 1 | 2700 | import nltk
# nltk.download_shell()
import pandas as pd
import string
from nltk.corpus import stopwords # words like: the, me, our
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics... | mit |
fabioticconi/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 70 | 7486 | """
============================================================
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 |
ssamot/ce888 | labs/lab2/salaries.py | 1 | 1518 | import matplotlib
matplotlib.use('Agg')
import pandas as pd
import random
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
# def permutation(statistic, error):
def mad(arr):
""" Median Absolute Deviation: a "Robust" version of standard deviation.
Indices variabililty of the ... | gpl-3.0 |
dancingdan/tensorflow | tensorflow/examples/get_started/regression/imports85.py | 41 | 6589 | # 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 |
huzq/scikit-learn | examples/model_selection/plot_nested_cross_validation_iris.py | 23 | 4413 | """
=========================================
Nested versus non-nested cross-validation
=========================================
This example compares non-nested and nested cross-validation strategies on a
classifier of the iris data set. Nested cross-validation (CV) is often used to
train a model in which hyperparam... | bsd-3-clause |
xhqu1981/pymatgen | pymatgen/analysis/eos.py | 5 | 17394 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import unicode_literals, division, print_function
"""
This module implements various equation of states.
Note: Most of the code were initially adapted from ASE and deltafactor by
@gmatteo but ... | mit |
huongttlan/statsmodels | statsmodels/examples/ex_kde_confint.py | 34 | 1973 | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 16 11:02:59 2013
Author: Josef Perktold
"""
from __future__ import print_function
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.nonparametric.api as npar
from statsmodels.sandbox.nonparametric import kernels
from statsmode... | bsd-3-clause |
davidam/python-examples | scikit/lda.py | 2 | 4035 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (C) 2018 David Arroyo Menéndez
# Author: David Arroyo Menéndez <davidam@gnu.org>
# Maintainer: David Arroyo Menéndez <davidam@gnu.org>
# This file is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as p... | gpl-3.0 |
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