repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
wqferr/AniMathors | core/anim.py | 1 | 2063 | from math import ceil
import matplotlib.pyplot as plt
import matplotlib.animation as anim
class Animation(object):
def __init__(self, *args, **kwargs):
self._fig, self._ax = plt.subplots()
self._fig.set_facecolor(kwargs.get('facecolor', 'black'))
self._ax.set_xlim(*kwargs.get('xlim', (-1,... | mit |
lmallin/coverage_test | python_venv/lib/python2.7/site-packages/numpy/lib/function_base.py | 19 | 164441 | from __future__ import division, absolute_import, print_function
import collections
import operator
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import linspace, atleast_1d, atleast_2d, transpose
from numpy.core.numeric import (
ones, zeros, arange, conc... | mit |
codein/poc | sql_loader/sql_loader.py | 1 | 7982 | import os
import logging
import sqlite3
import pandas as pd
SQL_LOADER_HOME = '~/temp/sql_loader_home'
DB_NAME = 'sql_loader_1.db'
db_path = os.path.expanduser('{0}/{1}'.format(SQL_LOADER_HOME, DB_NAME))
select_command_template = """
select count(*) from {table_name}
"""
select_by_primary_key_command_template = """
... | mit |
julienr/vispy | vispy/visuals/axis.py | 13 | 18105 | # -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) 2014, Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -------------------------------------------------------------------------... | bsd-3-clause |
DTMilodowski/SPA_tools | field_data/collate_leaf_traits_and_environmental_data.py | 1 | 38181 | import numpy as np
from matplotlib import pyplot as plt
from scipy import stats
import sys
sys.path.append('/home/dmilodow/DataStore_DTM/BALI/MetDataProcessing/UtilityTools/')
#import statistics_tools as stats2
import load_field_data as field
sys.path.append('/home/dmilodow/DataStore_DTM/BALI/LiDAR/src/')
import LiDAR... | gpl-3.0 |
S2H-Mobile/RoboND-Perception-Project | scripts/features.py | 1 | 2031 | import matplotlib.colors
import matplotlib.pyplot as plt
import numpy as np
from pcl_helper import *
def rgb_to_hsv(rgb_list):
rgb_normalized = [1.0*rgb_list[0]/255, 1.0*rgb_list[1]/255, 1.0*rgb_list[2]/255]
hsv_normalized = matplotlib.colors.rgb_to_hsv([[rgb_normalized]])[0][0]
return hsv_normalized
de... | mit |
toobaz/pandas | pandas/tests/dtypes/cast/test_find_common_type.py | 3 | 3956 | import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.dtypes import CategoricalDtype, DatetimeTZDtype, PeriodDtype
@pytest.mark.parametrize(
"source_dtypes,expected_common_dtype",
[
((np.int64,), np.int64),
((np.uint64,), np.uint64),
... | bsd-3-clause |
vasilvv/SSIM | tools/visualize.py | 2 | 1033 | #!/usr/bin/python
import matplotlib.pyplot as plt
import sys
if len(sys.argv) < 2:
print "Usage: vizualize.py file1[:label1] file2[:label2] ..."
colors = ['g', 'b', 'r', '#F800F0', '#00E8CC', '#E8E800']
markers = { 'I' : '*', 'P' : 's', 'B' : 'o' }
if len(sys.argv) - 1 > len(colors):
print "Too many files s... | mit |
justincassidy/scikit-learn | examples/cluster/plot_mini_batch_kmeans.py | 265 | 4081 | """
====================================================================
Comparison of the K-Means and MiniBatchKMeans clustering algorithms
====================================================================
We want to compare the performance of the MiniBatchKMeans and KMeans:
the MiniBatchKMeans is faster, but give... | bsd-3-clause |
kaichogami/scikit-learn | sklearn/decomposition/__init__.py | 76 | 1490 | """
The :mod:`sklearn.decomposition` module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.
"""
from .nmf import NMF, ProjectedGradientNMF, non_negative_factorization
from .pca import PCA, Ra... | bsd-3-clause |
sunzhxjs/JobGIS | lib/python2.7/site-packages/pandas/tseries/tests/test_period.py | 9 | 153010 | """Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- Pierre Gerard-Marchant & Matt Knox
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from datetime import datetime, date, timedelta
from numpy.ma.testutils import assert_equal
from pandas import Timesta... | mit |
zihua/scikit-learn | sklearn/base.py | 5 | 19817 | """Base classes for all estimators."""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import copy
import warnings
import numpy as np
from scipy import sparse
from .externals import six
from .utils.fixes import signature
from .utils.deprecation import deprecated
from .exceptions impo... | bsd-3-clause |
cybernet14/scikit-learn | examples/cluster/plot_dbscan.py | 346 | 2479 | # -*- 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 |
guziy/basemap | examples/plotmap_oo.py | 2 | 2718 | from __future__ import (absolute_import, division, print_function)
# make plot of etopo bathymetry/topography data on
# lambert conformal conic map projection, drawing coastlines, state and
# country boundaries, and parallels/meridians.
# the data is interpolated to the native projection grid.
######################... | gpl-2.0 |
mrocklin/blaze | blaze/server/tests/test_server.py | 1 | 8213 | from __future__ import absolute_import, division, print_function
import pytest
pytest.importorskip('flask')
import datashape
import numpy as np
from flask import json
from datetime import datetime
from pandas import DataFrame
from toolz import pipe
from odo import odo
from blaze.utils import example
from blaze impor... | bsd-3-clause |
zimenglan-sysu-512/pose_action_caffe | lib/fast_rcnn/test.py | 43 | 11975 | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Test a Fast R-CNN network on an imdb (image database)."""
from fast... | mit |
zuphilip/ocropy | OLD/lineproc.py | 15 | 6891 | ################################################################
### functions specific to text line processing
### (text line segmentation is in lineseg)
################################################################
from scipy import stats
from scipy.ndimage import interpolation,morphology,filters
from pylab impor... | apache-2.0 |
LiaoPan/scikit-learn | examples/linear_model/plot_sgd_loss_functions.py | 249 | 1095 | """
==========================
SGD: convex loss functions
==========================
A plot that compares the various convex loss functions supported by
:class:`sklearn.linear_model.SGDClassifier` .
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def modified_huber_loss(y_true, y_pred):
z ... | bsd-3-clause |
poryfly/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/sklearn/ensemble/gradient_boosting.py | 8 | 73285 | """Gradient Boosted Regression Trees
This module contains methods for fitting gradient boosted regression trees for
both classification and regression.
The module structure is the following:
- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regressio... | mit |
krischer/python-future | src/future/utils/__init__.py | 10 | 20353 | """
A selection of cross-compatible functions for Python 2 and 3.
This exports useful functions for 2/3 compatible code that are not
builtins on Python 3:
* bind_method: binds functions to classes
* ``native_str_to_bytes`` and ``bytes_to_native_str``
* ``native_str``: always equal to the native platform s... | mit |
mmoiozo/IROS | sw/tools/tcp_aircraft_server/phoenix/__init__.py | 86 | 4470 | #Copyright 2014, Antoine Drouin
"""
Phoenix is a Python library for interacting with Paparazzi
"""
import math
"""
Unit convertions
"""
def rad_of_deg(d): return d/180.*math.pi
def deg_of_rad(r): return r*180./math.pi
def rps_of_rpm(r): return r*2.*math.pi/60.
def rpm_of_rps(r): return r/2./math.pi*60.
def m_of_i... | gpl-2.0 |
dchabot/bluesky | bluesky/testing/decorators.py | 4 | 4434 | ########################################################################
# Copyright (c) 2015, Brookhaven Science Associates, Brookhaven #
# National Laboratory. All rights reserved. #
# #
# Redistribution and use in ... | bsd-3-clause |
roxyboy/scikit-learn | sklearn/datasets/tests/test_20news.py | 280 | 3045 | """Test the 20news downloader, if the data is available."""
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import SkipTest
from sklearn import datasets
def test_20news():
try:
data = dat... | bsd-3-clause |
jakobworldpeace/scikit-learn | examples/calibration/plot_calibration_curve.py | 113 | 5904 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
njoubert/MAVProxy | MAVProxy/modules/lib/wxhorizon_ui.py | 1 | 32207 | import time
from wxhorizon_util import Attitude, VFR_HUD, Global_Position_INT, BatteryInfo, FlightState, WaypointInfo, FPS
from wx_loader import wx
import math, time
import matplotlib
matplotlib.use('wxAgg')
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
from matplotlib.figure import F... | gpl-3.0 |
DhrubajyotiDas/PyAbel | examples/example_onion_bordas.py | 1 | 1062 | # -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import abel
import matplotlib.pyplot as plt
# Dribinski sample image
IM = abel.tools.analytical.sample_image(n=501)
# split into quadrants
origQ = abel.tools.symme... | mit |
kapteyn-astro/kapteyn | doc/source/EXAMPLES/mu_reproj_interact.py | 1 | 1085 | from kapteyn import maputils
from matplotlib import pyplot as plt
import numpy
# Read first image as base
Basefits = maputils.FITSimage(promptfie=maputils.prompt_fitsfile)
print(type(Basefits), isinstance(Basefits, maputils.FITSimage))
# Get data from a second image. This is the data that
# should be reprojected to... | bsd-3-clause |
clemkoa/scikit-learn | benchmarks/bench_rcv1_logreg_convergence.py | 58 | 7229 | # Authors: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
# Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import matplotlib.pyplot as plt
import numpy as np
import gc
import time
from sklearn.externals.joblib import Memory
from sklearn.linear_model import (LogisticRegression, SGDClassifi... | bsd-3-clause |
rajat1994/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 |
Achuth17/scikit-learn | sklearn/decomposition/pca.py | 6 | 23035 | """ 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 |
ContinuumIO/pydata-strata-2014-sj | 07-final-app/baseball_salaries.py | 1 | 2487 | import flask
import pandas as pd
import numpy as np
import blaze as bz
from into import into
from bokeh.embed import components
from bokeh.resources import INLINE
from bokeh.templates import RESOURCES
from bokeh.utils import encode_utf8
from bokeh.models import ColumnDataSource
import bokeh.plotting as plt
app = f... | bsd-2-clause |
brclark-usgs/flopy | autotest/t007_test.py | 1 | 26204 | # Test export module
import sys
sys.path.insert(0, '..')
import copy
import os
import shutil
import numpy as np
import flopy
pth = os.path.join('..', 'examples', 'data', 'mf2005_test')
namfiles = [namfile for namfile in os.listdir(pth) if namfile.endswith('.nam')]
# skip = ["MNW2-Fig28.nam", "testsfr2.nam", "testsfr2... | bsd-3-clause |
nikitasingh981/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
dhlab-epfl/cadasters | geojson_processing/evaluation.py | 1 | 9550 | #!/usr/bin/env python
__author__ = "solivr"
__license__ = "GPL"
from typing import Union
import pandas as pd
import geopandas as gpd
from sklearn.neighbors import NearestNeighbors
import numpy as np
from tqdm import tqdm
def iou_get_precision(dataframe_correspondencies: Union[pd.DataFrame, gpd.GeoDataFrame],
... | gpl-3.0 |
JosmanPS/scikit-learn | sklearn/neighbors/tests/test_approximate.py | 142 | 18692 | """
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""
# Author: Maheshakya Wijewardena, Joel Nothman
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
rabrahm/ceres | vbt/vbtpipe.py | 1 | 42920 | import sys
import matplotlib
matplotlib.use("Agg")
from pylab import *
ioff()
base = '../'
sys.path.append(base+"utils/Continuum/")
sys.path.append(base+"utils/Correlation/")
sys.path.append(base+"utils/GLOBALutils/")
sys.path.append(base+"utils/OptExtract/")
baryc_dir= base+'utils/SSEphem/'
sys.path.append(baryc_di... | mit |
Shen-Lab/cNMA | Software/helperScripts/makeBoxPlot.py | 1 | 5784 | '''
Created on Sep 26, 2014
@author: oliwa
'''
import argparse
import sys
import os
import numpy as np
from prody import *
import glob
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def percentageDecrease(x, y):
"""Return the percentage decrease from x towards y
Args:
... | mit |
alan-unravel/bokeh | bokeh/compat/mplexporter/exporter.py | 32 | 12403 | """
Matplotlib Exporter
===================
This submodule contains tools for crawling a matplotlib figure and exporting
relevant pieces to a renderer.
"""
import warnings
import io
from . import utils
import matplotlib
from matplotlib import transforms
from matplotlib.backends.backend_agg import FigureCanvasAgg
clas... | bsd-3-clause |
moserand/crosswater | crosswater/routing_model/aqu_sys_hydro.py | 1 | 23407 | """
Aquasim variable definition (only hydraulics without substances).
@author: moserand
"""
import fnmatch
import math
import numpy as np
from collections import defaultdict
import sys
import itertools
import tables
import pandas
from crosswater.read_config import read_config
from crosswater.preprocess... | gpl-3.0 |
abhishekkrthakur/scikit-learn | examples/linear_model/plot_ransac.py | 250 | 1673 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import ... | bsd-3-clause |
macks22/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 |
datitran/Krimskrams | Kaggle/Sberbank Russian Housing Market/nn_model_macro.py | 1 | 5012 | import numpy as np
np.random.seed(42)
import tensorflow as tf
tf.set_random_seed(42)
import argparse
import pandas as pd
from sklearn import preprocessing
from keras.models import Model
from keras.layers import Dense, Input, Dropout, average
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping... | mit |
hrjn/scikit-learn | examples/svm/plot_custom_kernel.py | 93 | 1562 | """
======================
SVM with custom kernel
======================
Simple usage of Support Vector Machines to classify a sample. It will
plot the decision surface and the support vectors.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data... | bsd-3-clause |
dcprojects/CoolProp | dev/TTSE/check_TTSE_old.py | 3 | 3304 | from CoolProp.Plots import Ph
import CoolProp
import CoolProp.CoolProp as CP
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
import matplotlib.ticker
import numpy as np
import random
fig = plt.figure(figsize=(10,5))
ax1 = fig.add_axes((0.08,0.1,0.32,0.83))
ax2 = fig.add_a... | mit |
ahnitz/mpld3 | mpld3/test_plots/test_text.py | 21 | 1305 | """Plot to test text"""
import matplotlib.pyplot as plt
import mpld3
def create_plot():
fig, ax = plt.subplots()
ax.grid(color='gray')
# test font sizes
x = 0.1
for y, size in zip([0.1, 0.3, 0.5, 0.7, 0.9],
[8, 12, 16, 20, 24]):
ax.text(x, y, "size={0}".format(size)... | bsd-3-clause |
AvinashSingh786/RegSmart | Reports.py | 1 | 23898 | import os
import time
import datetime
import reportlab.lib.enums as e
from reportlab.lib import colors
from reportlab.lib.pagesizes import A4
from reportlab.lib.units import cm
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.u... | mit |
harisbal/pandas | pandas/tests/indexes/datetimes/test_construction.py | 1 | 29387 | from datetime import timedelta
from functools import partial
from operator import attrgetter
import numpy as np
import pytest
import pytz
from pandas._libs.tslib import OutOfBoundsDatetime
from pandas._libs.tslibs import conversion
import pandas as pd
from pandas import (
DatetimeIndex, Index, Timestamp, date_ra... | bsd-3-clause |
saketkc/statsmodels | statsmodels/examples/ex_multivar_kde.py | 34 | 1504 |
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d
import statsmodels.api as sm
"""
This example illustrates the nonparametric estimation of a
bivariate bi-modal distribution that is a mixture of two normal
distri... | bsd-3-clause |
lin-credible/scikit-learn | examples/linear_model/plot_ols.py | 220 | 1940 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
gfyoung/pandas | pandas/core/strings/__init__.py | 2 | 1187 | """
Implementation of pandas.Series.str and its interface.
* strings.accessor.StringMethods : Accessor for Series.str
* strings.base.BaseStringArrayMethods: Mixin ABC for EAs to implement str methods
Most methods on the StringMethods accessor follow the pattern:
1. extract the array from the series (or index)
... | bsd-3-clause |
mojoboss/scikit-learn | sklearn/metrics/tests/test_score_objects.py | 84 | 14181 | import pickle
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.testing import assert_true
from sklearn.utils.testing im... | bsd-3-clause |
deepesch/scikit-learn | examples/linear_model/plot_ols_3d.py | 350 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature... | bsd-3-clause |
michalsenkyr/spark | python/pyspark/ml/clustering.py | 5 | 50284 | #
# 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 |
saketkc/statsmodels | statsmodels/tsa/filters/cf_filter.py | 28 | 3435 | from statsmodels.compat.python import range
import numpy as np
from ._utils import _maybe_get_pandas_wrapper
# the data is sampled quarterly, so cut-off frequency of 18
# Wn is normalized cut-off freq
#Cutoff frequency is that frequency where the magnitude response of the filter
# is sqrt(1/2.). For butter, the norm... | bsd-3-clause |
chris1610/pbpython | code/advanced_excel.py | 1 | 2204 | """
See http://pbpython.com/advanced-excel-workbooks.html for details on this script
"""
from __future__ import print_function
import pandas as pd
from xlsxwriter.utility import xl_rowcol_to_cell
def format_excel(writer, df_size):
""" Add Excel specific formatting to the workbook
df_size is a tuple represent... | bsd-3-clause |
alexis-roche/nipy | tools/run_log_examples.py | 4 | 6007 | #!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
from __future__ import print_function, with_statement
DESCRIP = 'Run and log examples'
EPILOG = \
""" Run examples in directory
Typical usage is:
run_log_examples.py nipy/examples -... | bsd-3-clause |
HasanIssa88/EMG_Classification | EMG_Thershold.py | 1 | 5278 | '''
This function will read the EMG data after the RMS filter and will make thresholding for the force and the EMG Channels by deleting
all values which are below a certain Thershold and will keep the values which is above and plot all channels configuration for this
and will construct a DataFrame from all channel... | gpl-3.0 |
manojgudi/sandhi | modules/gr36/gnuradio-core/src/examples/pfb/fmtest.py | 17 | 7785 | #!/usr/bin/env python
#
# Copyright 2009 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 |
mlperf/training_results_v0.6 | Intel/benchmarks/minigo/implementations/tensorflow/oneoffs/l2_cost_by_var.py | 7 | 3864 | # Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
altermarkive/Resurrecting-JimFleming-Numerai | src/ml-zygmuntz--numer.ai/march/validate_lr.py | 1 | 2996 | #!/usr/bin/env python3
"Load data, create the validation split, optionally scale data, train a linear model, evaluate"
"Code updated for march 2016 data"
import json
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Norma... | mit |
jkarnows/scikit-learn | examples/neighbors/plot_species_kde.py | 282 | 4059 | """
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... | bsd-3-clause |
amueller/scipy-2016-sklearn | notebooks/figures/plot_pca.py | 5 | 3131 | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
def plot_pca_illustration():
rnd = np.random.RandomState(5)
X_ = rnd.normal(size=(300, 2))
X_blob = np.dot(X_, rnd.normal(size=(2, 2))) + rnd.normal(size=2)
pca = PCA()
pca.fit(X_blob)
X_pca = pca.transfo... | cc0-1.0 |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/tests/io/json/test_pandas.py | 11 | 44634 | # -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import pytest
from pandas.compat import (range, lrange, StringIO,
OrderedDict, is_platform_32bit)
import os
import numpy as np
from pandas import (Series, DataFrame, DatetimeIndex, Timestamp,
read_json, compat)
fro... | mit |
ahoyosid/scikit-learn | sklearn/decomposition/pca.py | 24 | 22932 | """ 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 |
akunze3/pytrajectory | examples/ex3_Aircraft.py | 1 | 3087 | # vertical take-off aircraft
# import trajectory class and necessary dependencies
from pytrajectory import ControlSystem
from sympy import sin, cos
import numpy as np
from numpy import pi
# define the function that returns the vectorfield
def f(x,u):
x1, x2, x3, x4, x5, x6 = x # system state variables
u1, u2... | bsd-3-clause |
PatrickChrist/scikit-learn | sklearn/pipeline.py | 162 | 21103 | """
The :mod:`sklearn.pipeline` module implements utilities to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Lars Buitinck
# Licence: BSD
from collections import defaultdict... | bsd-3-clause |
sowe9385/qiime | scripts/make_otu_heatmap.py | 15 | 11322 | #!/usr/bin/env python
from __future__ import division
__author__ = "Dan Knights"
__copyright__ = "Copyright 2011, The QIIME project"
__credits__ = [
"Dan Knights",
"Jose Carlos Clemente Litran",
"Yoshiki Vazquez Baeza",
"Greg Caporaso",
"Jai Ram Rideout"]
__license__ = "GPL"
__version__ = "1.9.1-de... | gpl-2.0 |
archiekey/MachineLearningResearch | Clustering.py | 2 | 1119 | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.metrics import accuracy_score
from sklearn.cluster import KMeans
from preprocesstest import testdata
from preprocesstra... | apache-2.0 |
suiyuan2009/tensorflow | tensorflow/examples/learn/iris_custom_model.py | 37 | 3651 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | apache-2.0 |
jwlockhart/concept-networks | nlp.py | 1 | 2220 | # utility functions for NLP-based similarity metrics
# version 1.0
# code modified from:
# https://stackoverflow.com/questions/8897593/similarity-between-two-text-documents
import nltk
import string
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
stemmer = nltk.stem.porter.PorterStem... | gpl-3.0 |
manashmndl/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
yarikoptic/pystatsmodels | statsmodels/examples/tut_ols_ancova.py | 4 | 2413 | '''Examples OLS
Note: uncomment plt.show() to display graphs
Summary:
========
Relevant part of construction of design matrix
xg includes group numbers/labels,
x1 is continuous explanatory variable
>>> dummy = (xg[:,None] == np.unique(xg)).astype(float)
>>> X = np.c_[x1, dummy[:,1:], np.ones(nsample)]
Estimate the... | bsd-3-clause |
caseyclements/bokeh | examples/plotting/file/unemployment.py | 46 | 1846 | from collections import OrderedDict
import numpy as np
from bokeh.plotting import ColumnDataSource, figure, show, output_file
from bokeh.models import HoverTool
from bokeh.sampledata.unemployment1948 import data
# Read in the data with pandas. Convert the year column to string
data['Year'] = [str(x) for x in data['Y... | bsd-3-clause |
crichardson17/starburst_atlas | Low_resolution_sims/Dusty_LowRes/Geneva_cont_NoRot/Geneva_cont_NoRot_2/fullgrid/Optical1.py | 30 | 9342 | 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 |
TheaGao/SklearnModel | Vote_Results.py | 1 | 1602 | import os
import numpy as np
from baseZhang import class_encoder_to_number
from sklearn.ensemble import VotingClassifier
from sklearn.externals import joblib
from preprocessData import getDataXY
trainX, trainY, testX, testY, validX, validY = getDataXY()
encoder_path = 'encoder.pkl'
if not os.path.isfile(encoder_pat... | mit |
aruneral01/autokit | autokit/hyper.py | 2 | 7674 | import numpy as np
import scipy as sp
from sklearn.linear_model import Ridge, RidgeClassifier, LogisticRegression
from sklearn.naive_bayes import BernoulliNB
from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, BaggingClassifier, BaggingRegressor, RandomForestClassifier
from sklearn.pipel... | mit |
tcstewar/nstbot | nstbot/retinabot.py | 1 | 13309 | from . import nstbot
import numpy as np
import threading
class RetinaBot(nstbot.NSTBot):
def initialize(self):
super(RetinaBot, self).initialize()
self.retina(False)
self.retina_packet_size = None
self.image = None
self.record_file = None
self.count_spike_regions =... | gpl-2.0 |
BoltzmannBrain/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_gtkagg.py | 70 | 4184 | """
Render to gtk from agg
"""
from __future__ import division
import os
import matplotlib
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.backends.backend_gtk import gtk, FigureManagerGTK, FigureCanvasGTK,\
show, draw_if_interactive,\
error_ms... | agpl-3.0 |
wwf5067/statsmodels | statsmodels/sandbox/multilinear.py | 25 | 13937 | """Analyze a set of multiple variables with a linear models
multiOLS:
take a model and test it on a series of variables defined over a
pandas dataset, returning a summary for each variable
multigroup:
take a boolean vector and the definition of several groups of variables
and test if the group has a f... | bsd-3-clause |
lukas/scikit-class | examples/scikit/lstm.py | 2 | 1587 |
import json
from keras.layers import Embedding, LSTM, Dense, Conv1D, MaxPooling1D, Dropout, Activation
from keras.models import Sequential
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import np_utils
import numpy as np
import pandas as pd
im... | gpl-2.0 |
davidwaroquiers/pymatgen | pymatgen/analysis/magnetism/tests/test_heisenberg.py | 5 | 2735 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
import os
import unittest
import warnings
import pandas as pd
from pymatgen.core.structure import Structure
from pymatgen.analysis.magnetism.heisenberg import HeisenbergMapper
from pymatgen.util.testing impor... | mit |
roshantha9/AbstractManycoreSim | src/analyse_results/AnalyseResults_Exp_HRTVid_varCCR.py | 1 | 13711 | import sys, os, csv, pprint, math
from collections import OrderedDict
import numpy as np
import random
import shutil
import math
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import scipy.stats
import itertools
from matplotlib.colors import ListedColormap, NoNorm
from matplo... | gpl-3.0 |
tracierenea/gnuradio | gr-digital/examples/example_fll.py | 49 | 5715 | #!/usr/bin/env python
#
# Copyright 2011-2013 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 optio... | gpl-3.0 |
adrn/ophiuchus | ophiuchus/tests/test_orbitfit.py | 1 | 3417 | # coding: utf-8
from __future__ import division, print_function
__author__ = "adrn <adrn@astro.columbia.edu>"
# Third-party
from astropy import log as logger
import astropy.units as u
import matplotlib.pyplot as pl
import numpy as np
import gala.potential as gp
import gala.integrate as gi
import scipy.optimize as so... | mit |
hendrikwout/pynacolada | pynacolada/archive.py | 1 | 80243 | import glob
import os
import pandas as pd
import xarray as xr
import numpy as np
import numpy as np
import datetime as dt
import itertools
import yaml
import sys
from tqdm import tqdm
import tempfile
from . import apply_func
def parse_to_dataframe(list_or_dict_or_dataframe):
if type(list_or_dict_or_dataframe) == p... | gpl-3.0 |
fengzhyuan/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 |
sukritranjan/ranjansasselov2016b | radiativetransfer_albedo_subfunctions.py | 1 | 10411 | # -*- coding: iso-8859-1 -*-
"""
This script holds the subfunctions used to define the surface albedo for the radiativetransfer_vX.py code.
"""
import numpy as np
import matplotlib.pyplot as plt
import pdb
import scipy.integrate
from scipy import interpolate as interp
def get_surface_albedo(wav_left, wav_right, solarz... | mit |
vene/ambra | ambra/grid_search.py | 1 | 21548 | from abc import ABCMeta, abstractmethod
from collections import namedtuple, Sized
import warnings
import numpy as np
from sklearn.base import BaseEstimator, MetaEstimatorMixin, is_classifier, clone
from sklearn.grid_search import ParameterGrid, ParameterSampler
from sklearn.metrics.scorer import check_scoring
from sk... | bsd-2-clause |
iABC2XYZ/abc | CM/cmCooorect_3.py | 1 | 12861 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 14 11:46:48 2017
@author: p
"""
from epics import caget,caput
import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
plt.close('all')
filenRec='log_'+time.asctime().replace(' __','_').replace(' ','_')[4:]
fid=ope... | gpl-3.0 |
mne-tools/mne-tools.github.io | 0.11/_downloads/plot_label_source_activations.py | 32 | 2269 | """
====================================================
Extracting the time series of activations in a label
====================================================
We first apply a dSPM inverse operator to get signed activations
in a label (with positive and negative values) and we then
compare different strategies to ... | bsd-3-clause |
vitan/blaze | blaze/compute/tests/test_chunks_compute.py | 1 | 4072 | from __future__ import absolute_import, division, print_function
import pytest
import datetime
from toolz import map
from pandas import DataFrame
from toolz import concat
from blaze import into
from blaze.expr import Symbol, join, by
from blaze.compute.core import compute
from blaze.compute.chunks import ChunkIterab... | bsd-3-clause |
buckiracer/data-science-from-scratch | RefMaterials/Library/gradient.py | 1 | 3873 | from functools import partial
def sum_of_squares(v):
return sum(v_i ** 2 for v_i in v)
def difference_quotient(f,x,h):
return (f(x+h) - f(x))/ h
def square(x):
return x * x
def derivative(x):
return 2 * x
derivative_estimate = partial(difference_quotient,square,h=0.00001)
# import matplotlib.pyplot as plt
... | unlicense |
JohnStarich/github-code-recommendations | data-scripts/word-diff.py | 1 | 4631 | #!/usr/bin/env python3
import re
import numpy as np
import pandas as pd
from pymongo import MongoClient
from collections import Counter
import math
from sklearn.metrics import accuracy_score
client = MongoClient()
db = client.github
collection = db.events
data = list(collection.find(
{"type": "PullRequestEvent", ... | apache-2.0 |
hyqneuron/pylearn2-maxsom | pylearn2/scripts/datasets/step_through_small_norb.py | 49 | 3123 | #! /usr/bin/env python
"""
A script for sequentially stepping through SmallNORB, viewing each image and
its label.
Intended as a demonstration of how to iterate through NORB images,
and as a way of testing SmallNORB's StereoViewConverter.
If you just want an image viewer, consider
pylearn2/scripts/show_binocular_gra... | bsd-3-clause |
srowen/spark | python/pyspark/sql/tests/test_pandas_udf_typehints.py | 22 | 9603 | #
# 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 |
percyfal/snakemakelib | snakemakelib/bio/ngs/qc/qualimap.py | 1 | 6130 | # Copyright (C) 2015 by Per Unneberg
import pandas as pd
import numpy as np
from math import log10
from bokeh.plotting import figure, gridplot
from bokeh.charts import Scatter
from bokehutils.geom import points, abline
from bokehutils.facet import facet_grid
from bokehutils.axes import xaxis, yaxis, main
from snakemake... | mit |
rkmaddox/mne-python | tutorials/time-freq/20_sensors_time_frequency.py | 10 | 8158 | """
.. _tut-sensors-time-freq:
============================================
Frequency and time-frequency sensor analysis
============================================
The objective is to show you how to explore the spectral content
of your data (frequency and time-frequency). Here we'll work on Epochs.
We will use th... | bsd-3-clause |
feststelltaste/software-analytics | notebooks/lib/ausi/portfolio.py | 1 | 1394 | #!/usr/bin/env python
# -*- encoding: utf-8 -*-
import matplotlib.pyplot as plt
def plot_diagram(plot_data, x, y, size='Size'):
fig, ax = plt.subplots()
ax = plot_data.plot.scatter(
x,
y,
s=plot_data[size] * 100,
alpha=0.7,
title="SWOT matrix",
figsize=[10... | gpl-3.0 |
Midafi/scikit-image | doc/examples/plot_regionprops.py | 23 | 1297 | """
=========================
Measure region properties
=========================
This example shows how to measure properties of labelled image regions.
"""
import math
import matplotlib.pyplot as plt
import numpy as np
from skimage.draw import ellipse
from skimage.measure import label, regionprops
from skimage.tra... | bsd-3-clause |
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