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 |
|---|---|---|---|---|---|
zoebchhatriwala/MachineLearningBasics | MachineLearning#5/main.py | 1 | 1321 | from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
from scipy.spatial import distance
# Finds Euclidean Distance
def euc(a, b):
return distance.euclidean(a, b)
# New Classifier
class NewClassifier:
x_train = []
y_train = []
... | gpl-3.0 |
sniemi/SamPy | sandbox/src1/examples/major_minor_demo1.py | 4 | 1660 | #!/usr/bin/env python
"""
Demonstrate how to use major and minor tickers.
The two relevant userland classes are Locators and Formatters.
Locators determine where the ticks are and formatters control the
formatting of ticks.
Minor ticks are off by default (NullLocator and NullFormatter). You
can turn minor ticks on w... | bsd-2-clause |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/pandas/tests/io/parser/test_parsers.py | 7 | 3122 | # -*- coding: utf-8 -*-
import os
import pandas.util.testing as tm
from pandas import read_csv, read_table
from pandas.core.common import AbstractMethodError
from .common import ParserTests
from .header import HeaderTests
from .comment import CommentTests
from .dialect import DialectTests
from .quoting import Quoti... | mit |
Solthis/Fugen-2.0 | template_processor/base_template_processor.py | 1 | 8312 | # coding: utf-8
# Copyright 2017 Solthis.
#
# This file is part of Fugen 2.0.
#
# Fugen 2.0 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 ver... | gpl-3.0 |
LamaHamadeh/Microsoft-DAT210x | Module 5/assignment1.py | 1 | 3075 | #author Lama Hamadeh
# TOOD: Import whatever needs to be imported to make this work
#
# .. your code here ..
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
matplotlib.style.use('ggplot') # Look Pretty
#
# TODO: To procure the dataset, follow ... | mit |
mljar/mljar-examples | compare_Tensorflow_Sckit_Learn/benchmark_classification.py | 1 | 2149 | import os
import time
import json
import numpy as np
import pandas as pd
from supervised.automl import AutoML
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from pmlb import fetch_data, classification_dataset_names
results... | apache-2.0 |
xiaocanli/stochastic-parker | python/local_dist.py | 1 | 21364 | #!/usr/bin/env python3
"""
Analysis procedures for local distribution
"""
from __future__ import print_function
import argparse
import itertools
import json
import multiprocessing
import sys
import math
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from joblib import Parallel, delayed
fr... | gpl-3.0 |
victorbergelin/scikit-learn | sklearn/tests/test_grid_search.py | 68 | 28778 | """
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 numpy as np
import scipy.sparse as sp
... | bsd-3-clause |
kose-y/pylearn2 | pylearn2/cross_validation/tests/test_subset_iterators.py | 49 | 2411 | """
Test subset iterators.
"""
import numpy as np
from pylearn2.testing.skip import skip_if_no_sklearn
def test_validation_k_fold():
"""Test ValidationKFold."""
skip_if_no_sklearn()
from pylearn2.cross_validation.subset_iterators import ValidationKFold
n = 30
# test with indices
cv = Validat... | bsd-3-clause |
elkingtonmcb/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
MatthieuBizien/scikit-learn | sklearn/utils/validation.py | 15 | 25983 | """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... | bsd-3-clause |
nonhermitian/scipy | scipy/signal/_max_len_seq.py | 41 | 4942 | # Author: Eric Larson
# 2014
"""Tools for MLS generation"""
import numpy as np
from ._max_len_seq_inner import _max_len_seq_inner
__all__ = ['max_len_seq']
# These are definitions of linear shift register taps for use in max_len_seq()
_mls_taps = {2: [1], 3: [2], 4: [3], 5: [3], 6: [5], 7: [6], 8: [7, 6, 1],
... | bsd-3-clause |
raghavrv/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 22 | 20592 | from scipy import sparse
import numpy as np
from scipy import sparse
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_less
from s... | bsd-3-clause |
andaag/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
liangz0707/scikit-learn | examples/ensemble/plot_feature_transformation.py | 67 | 4285 | """
===============================================
Feature transformations with ensembles of trees
===============================================
Transform your features into a higher dimensional, sparse space. Then
train a linear model on these features.
First fit an ensemble of trees (totally random trees, a rand... | bsd-3-clause |
erikgrinaker/BOUT-dev | tools/pylib/post_bout/pb_present.py | 7 | 6244 | from __future__ import print_function
from __future__ import absolute_import
from builtins import str
from builtins import range
from .pb_draw import LinResDraw,subset
from .pb_corral import LinRes
from .pb_nonlinear import NLinResDraw
from pb_transport import Transport
import numpy as np
import matplotlib.pyplot as p... | gpl-3.0 |
sdsc/xsede_stats | tacc_stats/analysis/plot/plots.py | 1 | 6375 | # Plot generation tools for job analysis
from __future__ import print_function
import os
import abc
import numpy,traceback
import multiprocessing
from scipy.stats import scoreatpercentile as score
import matplotlib
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
from m... | lgpl-2.1 |
Ttl/scikit-rf | skrf/__init__.py | 2 | 1988 | '''
skrf is an object-oriented approach to microwave engineering,
implemented in Python.
'''
# Python 3 compatibility
from __future__ import absolute_import, print_function, division
from six.moves import xrange
__version__ = '0.14.5'
## Import all module names for coherent reference of name-space
#import io
from... | bsd-3-clause |
JoonasMelin/WeatherStation | station.py | 1 | 14760 | #!/usr/bin/python
# include RPi libraries in to Python code
import plotly.plotly as py
import plotly.graph_objs as go
import json
import datetime
import RPi.GPIO as GPIO
import time
import smbus
import time
from ctypes import c_short
import sys
import Adafruit_DHT
import cPickle as pickle
from collections import de... | gpl-3.0 |
NeuralEnsemble/elephant | elephant/sta.py | 2 | 13537 | # -*- coding: utf-8 -*-
"""
Functions to calculate spike-triggered average and spike-field coherence of
analog signals.
.. autosummary::
:toctree: _toctree/sta
spike_triggered_average
spike_field_coherence
:copyright: Copyright 2014-2020 by the Elephant team, see `doc/authors.rst`.
:license: Modified BSD... | bsd-3-clause |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/core/tools/datetimes.py | 1 | 35837 | from collections import abc
from datetime import datetime, time
from functools import partial
from typing import Optional, TypeVar, Union
import numpy as np
from pandas._libs import tslib, tslibs
from pandas._libs.tslibs import Timestamp, conversion, parsing
from pandas._libs.tslibs.parsing import ( # noqa
DateP... | apache-2.0 |
ChristophKirst/ClearMapUnstable | docs/_build/html/imageanalysis-11.py | 4 | 1303 | import os
import ClearMap.Settings as settings
filename = os.path.join(settings.ClearMapPath, 'Test/Data/ImageAnalysis/cfos-substack.tif');
import ClearMap.Visualization.Plot as plt
import ClearMap.IO as io
data = io.readData(filename, z = (0,26));
import ClearMap.ImageProcessing.BackgroundRemoval as bgr
dataBGR = bgr.... | gpl-3.0 |
B3AU/waveTree | examples/cluster/plot_dbscan.py | 6 | 2522 | # -*- coding: utf-8 -*-
"""
===================================
Demo of DBSCAN clustering algorithm
===================================
Finds core samples of high density and expands clusters from them.
"""
print(__doc__)
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn... | bsd-3-clause |
JohanComparat/pyEmerge | bin/lc_wedge_plot.py | 1 | 5144 | """
+ + + + + + + HEADER + + + + + + + + +
file_name lc_remaped_position_L3_.hdf5
HDF5_Version 1.8.18
h5py_version 2.7.1
+ + + + + + + DATA + + + + + + + + + +
========================================
agn_properties <HDF5 group "/agn_properties" (2 members)>
- - - - - - - - - - - - - - - - - - - -
agn_activity <H... | unlicense |
anomam/pvlib-python | setup.py | 1 | 3709 | #!/usr/bin/env python
import os
try:
from setuptools import setup
from setuptools.extension import Extension
except ImportError:
raise RuntimeError('setuptools is required')
import versioneer
DESCRIPTION = ('A set of functions and classes for simulating the ' +
'performance of photovolt... | bsd-3-clause |
kambysese/mne-python | mne/time_frequency/tfr.py | 1 | 103477 | """A module which implements the time-frequency estimation.
Morlet code inspired by Matlab code from Sheraz Khan & Brainstorm & SPM
"""
# Authors : Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Hari Bharadwaj <hari@nmr.mgh.harvard.edu>
# Clement Moutard <clement.moutard@polytechnique.org>
# ... | bsd-3-clause |
yanboliang/spark | examples/src/main/python/sql/arrow.py | 16 | 5034 | #
# 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 |
r-mart/scikit-learn | sklearn/tests/test_isotonic.py | 230 | 11087 | import numpy as np
import pickle
from sklearn.isotonic import (check_increasing, isotonic_regression,
IsotonicRegression)
from sklearn.utils.testing import (assert_raises, assert_array_equal,
assert_true, assert_false, assert_equal,
... | bsd-3-clause |
aditiiyer/CERR | CERR_core/ModelImplementationLibrary/SegmentationModels/ModelDependencies/CT_HeartStructure_DeepLab/dataloaders/utils.py | 4 | 3279 | import matplotlib.pyplot as plt
import numpy as np
import torch
def decode_seg_map_sequence(label_masks, dataset='heart'):
rgb_masks = []
for label_mask in label_masks:
rgb_mask = decode_segmap(label_mask, dataset)
rgb_masks.append(rgb_mask)
rgb_masks = torch.from_numpy(np.array(rgb_masks).... | lgpl-2.1 |
siutanwong/scikit-learn | sklearn/utils/tests/test_testing.py | 144 | 4121 | import warnings
import unittest
import sys
from nose.tools import assert_raises
from sklearn.utils.testing import (
_assert_less,
_assert_greater,
assert_less_equal,
assert_greater_equal,
assert_warns,
assert_no_warnings,
assert_equal,
set_random_state,
assert_raise_message)
from ... | bsd-3-clause |
Natetempid/nearfield | analyze_nfdata_tk/analyze_nfdata_tk/data_interpreter.py | 1 | 8669 | from __future__ import print_function
import sys
import Tkinter as tk
import ttk
import numpy as np
import os
import datetime
from scipy import interpolate
import tkFileDialog
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.backends.backend_tkagg import NavigationToolbar2TkAgg
from matpl... | gpl-3.0 |
doctornerdis/index_translationum | visualization.py | 1 | 1451 | import pandas as pd
import numpy as np
import glob as glob
import matplotlib.pyplot as plt
import seaborn as sns
# Aggregating all the data
file_pattern = "/Users/nicholascifuentes-goodbody/Documents/GitHub/index_translationum/data/*.csv"
list_data = []
csv_files = glob.glob(file_pattern)
for filename in csv_files:... | mit |
chawins/aml | parameters_yolo.py | 1 | 1410 | # Import packages for all files
import os
import pickle
import random
import threading
import time
from os import listdir
import cv2
import keras
import keras.backend as K
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from keras.optimizers import SGD
from keras.preprocessing.image import I... | mit |
simonsfoundation/CaImAn | demos/obsolete/demo_caiman_patches.py | 2 | 10389 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 24 18:39:45 2016
@author: Andrea Giovannucci
For explanation consult at https://github.com/agiovann/Constrained_NMF/releases/download/v0.4-alpha/Patch_demo.zip
and https://github.com/agiovann/Constrained_NMF
"""
from __future__ import division
fr... | gpl-2.0 |
WEP11/NEXpy | level3.py | 1 | 6752 | #######################################################
# #
# PYTHON NEXRAD PLOT GENERATION #
# #
# Level-3, Single Site #
# ... | gpl-3.0 |
deepchem/deepchem | examples/low_data/toxcast_maml.py | 4 | 3781 | from __future__ import print_function
import deepchem as dc
import numpy as np
import tensorflow as tf
from sklearn.metrics import accuracy_score
# Load the data.
tasks, datasets, transformers = dc.molnet.load_toxcast()
(train_dataset, valid_dataset, test_dataset) = datasets
x = train_dataset.X
y = train_dataset.y
w... | mit |
Vvucinic/Wander | venv_2_7/lib/python2.7/site-packages/pandas/util/doctools.py | 11 | 6612 | import numpy as np
import pandas as pd
import pandas.compat as compat
class TablePlotter(object):
"""
Layout some DataFrames in vertical/horizontal layout for explanation.
Used in merging.rst
"""
def __init__(self, cell_width=0.37, cell_height=0.25, font_size=7.5):
self.cell_width = cell_... | artistic-2.0 |
shamrt/jsPASAT | scripts/compile_data.py | 1 | 17970 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""A script that parses raw data output by jsPASAT (jsPsych), compiles each
participant's data and creates/updates a file in jsPASAT's ``data``
directory.
"""
import os
import glob
import json
import pandas as pd
import numpy as np
from scipy import stats
PROJECT_DIR = o... | mit |
VladimirTyrin/urbansim | urbansim/urbanchoice/tests/test_interaction.py | 7 | 1734 | import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
from .. import interaction as inter
@pytest.fixture
def choosers():
return pd.DataFrame(
{'var1': range(5, 10),
'thing_id': ['a', 'c', 'e', 'g', 'i']})
@pytest.fixture
def alternatives():
return pd.DataFrame(
... | bsd-3-clause |
VasLem/KinectPainting | construct_actions_table.py | 1 | 8161 | import os
from matplotlib import pyplot as plt
import numpy as np
import matplotlib.gridspec as gridspec
import class_objects as co
import cv2
import action_recognition_alg as ara
from textwrap import wrap
def extract_valid_action_utterance(action, testing=False, *args, **kwargs):
'''
Visualizes action ... | bsd-3-clause |
yyjiang/scikit-learn | sklearn/linear_model/tests/test_perceptron.py | 378 | 1815 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_raises
from sklearn.utils import check_random_state
from sklearn.datasets import load_iris
from sklearn.linear_model import Pe... | bsd-3-clause |
zorroblue/scikit-learn | examples/linear_model/plot_logistic_multinomial.py | 81 | 2525 | """
====================================================
Plot multinomial and One-vs-Rest Logistic Regression
====================================================
Plot decision surface of multinomial and One-vs-Rest Logistic Regression.
The hyperplanes corresponding to the three One-vs-Rest (OVR) classifiers
are repre... | bsd-3-clause |
poine/rosmip | rosmip/rosmip_control/scripts/test_sfb_ctl_on_ann.py | 1 | 6165 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np, matplotlib.pyplot as plt
import scipy.signal, scipy.optimize, control, control.matlab
import keras
import pdb
import julie_misc.plot_utils as jpu
import ident_plant
# https://programtalk.com/vs2/?source=python/10610/python-control/external/yottalab.py... | gpl-3.0 |
graphistry/pygraphistry | graphistry/tests/test_hypergraph.py | 1 | 9926 | # -*- coding: utf-8 -*-
import datetime as dt, logging, pandas as pd, pyarrow as pa
import graphistry, graphistry.plotter
from common import NoAuthTestCase
logger = logging.getLogger(__name__)
nid = graphistry.plotter.Plotter._defaultNodeId
triangleNodesDict = {
'id': ['a', 'b', 'c'],
'a1': [1, 2, 3],
... | bsd-3-clause |
yonglehou/scikit-learn | examples/neighbors/plot_nearest_centroid.py | 264 | 1804 | """
===============================
Nearest Centroid Classification
===============================
Sample usage of Nearest Centroid classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
f... | bsd-3-clause |
dpaiton/OpenPV | pv-core/analysis/python/rate_estimation.py | 1 | 2913 | #!/usr/bin/python
# script to estimate the firing rate in L1 versus the background rate in retina
# copy this script to the location of your sandbox (STDP, marian, etc)
import os
import re # regular expression module
import time
import sys
import numpy as np
#import matplotlib.pyplot as plt
#import matplotlib.ml... | epl-1.0 |
snnn/tensorflow | tensorflow/contrib/learn/python/learn/estimators/multioutput_test.py | 136 | 1696 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
uwescience/pulse2percept | pulse2percept/implants/base.py | 1 | 8549 | """`ProsthesisSystem`"""
import numpy as np
from copy import deepcopy
from .electrodes import Electrode
from .electrode_arrays import ElectrodeArray
from ..stimuli import Stimulus
from ..utils import PrettyPrint
class ProsthesisSystem(PrettyPrint):
"""Visual prosthesis system
A visual prosthesis combines an... | bsd-3-clause |
kambysese/mne-python | tutorials/machine-learning/plot_sensors_decoding.py | 4 | 17548 | r"""
===============
Decoding (MVPA)
===============
.. include:: ../../links.inc
Design philosophy
=================
Decoding (a.k.a. MVPA) in MNE largely follows the machine
learning API of the scikit-learn package.
Each estimator implements ``fit``, ``transform``, ``fit_transform``, and
(optionally) ``inverse_tran... | bsd-3-clause |
andyh616/mne-python | tutorials/plot_raw_objects.py | 15 | 5335 | """
.. _tut_raw_objects
The :class:`Raw <mne.io.RawFIF>` data structure: continuous data
================================================================
"""
from __future__ import print_function
import mne
import os.path as op
from matplotlib import pyplot as plt
###################################################... | bsd-3-clause |
mMPI-MRI/TractLAS | utils.py | 1 | 74376 | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 23, 2017
Compute large connectomes using mrtrix and sparse matrices
@author: Christopher Steele
"""
from __future__ import division # to allow floating point calcs of number of voxels
from pandas import read_csv
def natural_sort(l):
"""
Returns alphanumerical... | gpl-3.0 |
ray-project/ray | python/ray/tune/examples/xgboost_example.py | 1 | 3306 | import sklearn.datasets
import sklearn.metrics
import os
from ray.tune.schedulers import ASHAScheduler
from sklearn.model_selection import train_test_split
import xgboost as xgb
from ray import tune
from ray.tune.integration.xgboost import TuneReportCheckpointCallback
def train_breast_cancer(config: dict):
# Thi... | apache-2.0 |
wujinjun/TFbook | chapter5/models-master/autoencoder/VariationalAutoencoderRunner.py | 12 | 1653 | import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from autoencoder_models.VariationalAutoencoder import VariationalAutoencoder
mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
def min_max_scale(X_train, X_test)... | gpl-3.0 |
loli/semisupervisedforests | examples/cluster/plot_cluster_iris.py | 350 | 2593 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
AnasGhrab/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 |
joshbohde/scikit-learn | examples/linear_model/plot_ridge_path.py | 2 | 1436 | """
===========================================================
Plot Ridge coefficients as a function of the regularization
===========================================================
.. currentmodule:: sklearn.linear_model
Shows the effect of collinearity in the coefficients or the
:class:`Ridge`. At the end of the ... | bsd-3-clause |
shanot/imp | modules/pmi/pyext/src/analysis.py | 1 | 106818 | #!/usr/bin/env python
"""@namespace IMP.pmi.analysis
Tools for clustering and cluster analysis
"""
from __future__ import print_function
import IMP
import IMP.algebra
import IMP.em
import IMP.pmi
import IMP.pmi.tools
import IMP.pmi.output
import IMP.rmf
import RMF
import IMP.pmi.analysis
from operator import itemge... | gpl-3.0 |
dereknewman/cancer_detection | extract_cubes_by_label.py | 1 | 7790 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 10 12:51:14 2017
@author: derek
"""
import pandas as pd
import SimpleITK
import numpy as np
import tensorflow as tf
import cv2
TARGET_VOXEL_MM = 0.682
BASE_DIR = "/media/derek/disk1/kaggle_ndsb2017/"
def normalize(image):
""" Normalize image -... | mit |
nhejazi/scikit-learn | examples/gaussian_process/plot_gpc_isoprobability.py | 64 | 3049 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Iso-probability lines for Gaussian Processes classification (GPC)
=================================================================
A two-dimensional classification example showing iso-probability lines for... | bsd-3-clause |
maxalbert/bokeh | examples/plotting/file/elements.py | 2 | 1491 | import pandas as pd
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata import periodic_table
elements = periodic_table.elements
elements = elements[elements["atomic number"] <= 82]
elements = elements[~pd.isnull(elements["melting point"])]
mass = [float(x.strip("[]")) for x in elements["atomic... | bsd-3-clause |
dpinney/omf | omf/scratch/transients/04_Classical_Nine_Bus/test_nine_bus.py | 1 | 6094 | #!python3
#
# Copyright (C) 2014-2015 Julius Susanto. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
"""
PYPOWER-Dynamics
Classical Stability Test
"""
# Dynamic model classes
from pydyn.ext_grid import ext_grid
from pydyn.sym_order6a import s... | gpl-2.0 |
YinongLong/scikit-learn | setup.py | 4 | 11924 | #! /usr/bin/env python
#
# Copyright (C) 2007-2009 Cournapeau David <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# License: 3-clause BSD
import subprocess
descr = """A set of python modules for machine learning and data mining"""
import sys
import os
import shutil
from distut... | bsd-3-clause |
xyguo/scikit-learn | sklearn/ensemble/__init__.py | 153 | 1382 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification, regression and anomaly detection.
"""
from .base import BaseEnsemble
from .forest import RandomForestClassifier
from .forest import RandomForestRegressor
from .forest import RandomTreesEmbedding
from .forest import ExtraTreesCla... | bsd-3-clause |
DLR-SC/tigl | misc/math-scripts/ComponentSegmentNew.py | 2 | 6822 | #
# Copyright (C) 2007-2011 German Aerospace Center (DLR/SC)
#
# Created: 2013-04-19 Martin Siggel <Martin.Siggel@dlr.de>
#
# 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.ap... | apache-2.0 |
ischwabacher/seaborn | seaborn/miscplot.py | 34 | 1498 | from __future__ import division
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
def palplot(pal, size=1):
"""Plot the values in a color palette as a horizontal array.
Parameters
----------
pal : sequence of matplotlib colors
colors, i.e. as returned by seaborn.colo... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/doc/mpl_examples/statistics/boxplot_demo.py | 3 | 2674 | """
Demo of the new boxplot functionality
"""
import numpy as np
import matplotlib.pyplot as plt
# fake data
np.random.seed(937)
data = np.random.lognormal(size=(37, 4), mean=1.5, sigma=1.75)
labels = list('ABCD')
fs = 10 # fontsize
# demonstrate how to toggle the display of different elements:
fig, axes = plt.subpl... | mit |
abhisg/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 272 | 6972 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, wher... | bsd-3-clause |
0asa/scikit-learn | sklearn/cross_validation.py | 2 | 63023 | """
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from... | bsd-3-clause |
Myasuka/scikit-learn | benchmarks/bench_plot_neighbors.py | 287 | 6433 | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import pylab as pl
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
return np.... | bsd-3-clause |
jjx02230808/project0223 | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 157 | 2409 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
jonathandunn/c2xg | c2xg/modules/rdrpos_tagger/pSCRDRtagger/RDRPOSTagger.py | 1 | 5379 | # -*- coding: utf-8 -*-
import os
import sys
import cytoolz as ct
from sklearn.utils import murmurhash3_32
from multiprocessing import Pool
from ..SCRDRlearner.SCRDRTree import SCRDRTree
from ..InitialTagger.InitialTagger import initializeCorpus, initializeSentence
from ..SCRDRlearner.Object import FWObject
from ..Uti... | gpl-3.0 |
mikebenfield/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 |
yangautumn/turing_pattern | amorphous_pattern/test.py | 1 | 1800 |
"""
# demo of plotting squared subfigures
"""
# import matplotlib.pyplot as plt
# fig, ax = plt.subplots()
# x = [0, 0.2, 0.4, 0.6, 0.8]
# y = [0, 0.5, 1, 1.5, 2.0]
# colors = ['k']*len(x)
# ax.scatter(x, y, c=colors, alpha=0.5)
# ax.set_xlim((0,2))
# ax.set_ylim((0,2))
# x0,x1 = ax.get_xlim()
# y0,y1 = ax.get_ylim()... | gpl-3.0 |
GaryKriebel/osf.io | scripts/annotate_rsvps.py | 60 | 2256 | """Utilities for annotating workshop RSVP data.
Example ::
import pandas as pd
from scripts import annotate_rsvps
frame = pd.read_csv('workshop.csv')
annotated = annotate_rsvps.process(frame)
annotated.to_csv('workshop-annotated.csv')
"""
import re
import logging
from dateutil.parser import par... | apache-2.0 |
bikong2/scikit-learn | sklearn/datasets/mldata.py | 309 | 7838 | """Automatically download MLdata datasets."""
# Copyright (c) 2011 Pietro Berkes
# License: BSD 3 clause
import os
from os.path import join, exists
import re
import numbers
try:
# Python 2
from urllib2 import HTTPError
from urllib2 import quote
from urllib2 import urlopen
except ImportError:
# Pyt... | bsd-3-clause |
ndingwall/scikit-learn | sklearn/semi_supervised/_self_training.py | 4 | 12700 | import warnings
import numpy as np
from ..base import MetaEstimatorMixin, clone, BaseEstimator
from ..utils.validation import check_is_fitted
from ..utils.metaestimators import if_delegate_has_method
from ..utils import safe_mask
__all__ = ["SelfTrainingClassifier"]
# Authors: Oliver Rausch <rauscho@ethz.ch>
# ... | bsd-3-clause |
cshallue/models | research/tcn/generate_videos.py | 5 | 15889 | # 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 applicab... | apache-2.0 |
asm666/sympy | sympy/interactive/tests/test_ipythonprinting.py | 27 | 5891 | """Tests that the IPython printing module is properly loaded. """
from sympy.core.compatibility import u
from sympy.interactive.session import init_ipython_session
from sympy.external import import_module
from sympy.utilities.pytest import raises
# run_cell was added in IPython 0.11
ipython = import_module("IPython",... | bsd-3-clause |
aiguofer/bokeh | examples/charts/file/timeseries.py | 4 | 2104 | import pandas as pd
from bokeh.charts import TimeSeries, show, output_file
from bokeh.layouts import column
# read in some stock data from the Yahoo Finance API
AAPL = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=AAPL&a=0&b=1&c=2000&d=0&e=1&f=2010",
parse_dates=['Date'])
MSFT = pd.read_csv(
"http://i... | bsd-3-clause |
amenonsen/ansible | hacking/cgroup_perf_recap_graph.py | 54 | 4384 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# (c) 2018, Matt Martz <matt@sivel.net>
#
# This file is part of Ansible
#
# Ansible 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 Licens... | gpl-3.0 |
guthemberg/yanoama | yanoama/monitoring/rtt_matrix/reload.py | 1 | 3023 | import pickle,sys,socket,subprocess,os,random
from datetime import datetime
from time import time
#from planetlab import Monitor
from configobj import ConfigObj
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MiniBatchKMeans
def save_object_to_file(myobject,output_file):
f = open(o... | bsd-3-clause |
aidiary/keras_examples | cnn/cifar10/plot_results.py | 1 | 1468 | import os
import matplotlib.pyplot as plt
result_file1 = os.path.join('result_without_datagen', 'history.txt')
result_file2 = os.path.join('result_with_datagen', 'history.txt')
def load_results(filename):
epoch_list = []
val_loss_list = []
val_acc_list = []
with open(filename) as fp:
fp.readl... | mit |
nanophotonics/nplab | nplab/instrument/electronics/SLM/pattern_generators.py | 1 | 15649 | # -*- coding: utf-8 -*-
from __future__ import division
from builtins import zip
from builtins import range
from past.utils import old_div
import numpy as np
# import pyfftw
from scipy import misc
import matplotlib.pyplot as plt
from matplotlib import gridspec
# TODO: performance quantifiers for IFT algorithms (smoo... | gpl-3.0 |
kevin-intel/scikit-learn | examples/linear_model/plot_logistic_path.py | 19 | 2352 | #!/usr/bin/env python
"""
==============================================
Regularization path of L1- Logistic Regression
==============================================
Train l1-penalized logistic regression models on a binary classification
problem derived from the Iris dataset.
The models are ordered from strongest ... | bsd-3-clause |
rjferrier/fluidity | tests/wetting_and_drying_balzano3_cg_parallel/plotfs_detec.py | 5 | 5095 | #!/usr/bin/env python
import vtktools
import sys
import math
import re
import commands
import matplotlib.pyplot as plt
import getopt
from scipy.special import erf
from numpy import poly1d
from matplotlib.pyplot import figure, show
from numpy import pi, sin, linspace
from matplotlib.mlab import stineman_interp
from n... | lgpl-2.1 |
xmnlab/minilab | gui/plotter/async_chart.py | 1 | 2771 | from __future__ import print_function, division
from collections import defaultdict
from matplotlib import pyplot as plt
from matplotlib.ticker import EngFormatter
from random import random, randint
from copy import deepcopy
import time
import numpy as np
import traceback
class DaqMultiPlotter(object):
"""
"... | gpl-3.0 |
pompiduskus/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 |
james-pack/ml | pack/ml/nodes/training_visualization.py | 1 | 4326 | import numpy as np
from matplotlib import pyplot as plt
class TrainingVisualization(object):
def __init__(self):
self.training_result = None
self.figure = None
self.loss_subplot = None
self.aggregate_loss_line = None
self.step_size_subplot = None
self.step_size_l... | mit |
stuart-knock/tvb-library | tvb/simulator/plot/power_spectra_interactive.py | 3 | 18840 | # -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Scientific Package. This package holds all simulators, and
# analysers necessary to run brain-simulations. You can use it stand alone or
# in conjunction with TheVirtualBrain-Framework Package. See content of the
# documentation-folder for more details. See also http://ww... | gpl-2.0 |
manipopopo/tensorflow | tensorflow/python/estimator/inputs/pandas_io_test.py | 7 | 11057 | # Copyright 2015 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 |
rs2/pandas | pandas/tests/reshape/test_util.py | 3 | 2846 | import numpy as np
import pytest
from pandas import Index, date_range
import pandas._testing as tm
from pandas.core.reshape.util import cartesian_product
class TestCartesianProduct:
def test_simple(self):
x, y = list("ABC"), [1, 22]
result1, result2 = cartesian_product([x, y])
expected1 =... | bsd-3-clause |
JustinWingChungHui/MyFamilyRoot | facial_recognition/models.py | 2 | 1355 | from django.db import models
from sklearn import neighbors
from family_tree.models import Family
import math
import pickle
# Create your models here.
class FaceModel(models.Model):
family = models.OneToOneField(
Family,
on_delete=models.CASCADE,
primary_key=True,
)
fit_data_faces... | gpl-2.0 |
untom/scikit-learn | examples/covariance/plot_mahalanobis_distances.py | 348 | 6232 | r"""
================================================================
Robust covariance estimation and Mahalanobis distances relevance
================================================================
An example to show covariance estimation with the Mahalanobis
distances on Gaussian distributed data.
For Gaussian dis... | bsd-3-clause |
UpSea/midProjects | histdataUI/Widgets/pgCrossAddition.py | 1 | 6756 | # -*- coding: utf-8 -*-
import sys,os
xpower = os.path.abspath(os.path.join(os.path.dirname(__file__),os.pardir,os.pardir,'thirdParty','pyqtgraph-0.9.10'))
sys.path.append(xpower)
xpower = os.path.abspath(os.path.join(os.path.dirname(__file__),os.pardir))
sys.path.append(xpower)
from PyQt4 import QtCore, QtGui
import p... | mit |
jblackburne/scikit-learn | sklearn/utils/tests/test_utils.py | 47 | 9089 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from itertools import chain
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal,
SkipTest, ... | bsd-3-clause |
BigDataforYou/movie_recommendation_workshop_1 | big_data_4_you_demo_1/venv/lib/python2.7/site-packages/pandas/tests/frame/test_analytics.py | 1 | 76158 | # -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import timedelta, datetime
from distutils.version import LooseVersion
import sys
import nose
from numpy import nan
from numpy.random import randn
import numpy as np
from pandas.compat import lrange
from pandas import (compat, isnull, notnul... | mit |
PiscesDream/Ideas | Cog_neusci/ex1/main.py | 1 | 4431 | from keras.layers import Input, Dense
from keras.models import Model
import keras
from keras import regularizers
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
keras.backend.theano_backend._set_device('dev1')
# this is the size of our encoded representations
encoding_dim = 16 # 32 floats -> ... | apache-2.0 |
cerebis/i3seqdb | app/objects.py | 1 | 6004 | from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
from sqlalchemy import Column, Integer, String, Date, Float, ForeignKey, Table, CheckConstraint
import pandas
Base = declarative_base()
class Sample(Base):
"""
Defined by the minimum required for submission at NC... | gpl-3.0 |
jadsonjs/DataScience | MachineLearning/print_ensemble_precisions.py | 1 | 2523 | #
# This program is distributed without any warranty and it
# can be freely redistributed for research, classes or private studies,
# since the copyright notices are not removed.
#
# This file just read the data to calculate the statistical test
#
# Jadson Santos - jadsonjs@gmail.com
#
# to run this exemple install py... | apache-2.0 |
wangqingbaidu/aliMusic | gen_X_features/user_clustering_artist_taset.py | 1 | 1195 | # -*- coding: UTF-8 -*-
'''
Authorized by vlon Jang
Created on May 25, 2016
Email:zhangzhiwei@ict.ac.cn
From Institute of Computing Technology
All Rights Reserved.
'''
from sklearn.cluster import MiniBatchKMeans, KMeans
import pandas as pd
import numpy as np
import pymysql
mysql_cn= pymysql.connect(host... | gpl-3.0 |
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