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 |
|---|---|---|---|---|---|
qeedquan/misc_utilities | math/controls/simple-gain-1.py | 1 | 1866 | """
https://lpsa.swarthmore.edu/Root_Locus/RootLocusWhy.html
"""
import numpy as np
import matplotlib.pyplot as plt
from sympy.abc import *
from sympy.integrals import inverse_laplace_transform
from sympy.solvers import solve
from sympy import *
def eval_transfer_function(G, R, K0, t0, t1):
G = G.subs(K, K0)
... | mit |
scubamut/SimpleBacktester | backtest_helpers/backtest.py | 1 | 1659 | # THIS ONE MATCHES PV
# SEE PV backtest :https://goo.gl/lBR4K9
# AND spreadsheet : https://goo.gl/8KGp58
# and Quantopian backtest : https://goo.gl/xytT5L
def backtest(prices, weights, capital, offset=1, commission=0.):
import pandas as pd
rebalance_dates = weights.index
buy_dates = [prices.index[d + o... | gpl-3.0 |
abhishekkrthakur/scikit-learn | benchmarks/bench_covertype.py | 154 | 7296 | """
===========================
Covertype dataset benchmark
===========================
Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART
(decision tree), RandomForest and Extra-Trees on the forest covertype dataset
of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It ... | bsd-3-clause |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/seaborn/categorical.py | 1 | 133468 | from __future__ import division
from textwrap import dedent
import colorsys
import numpy as np
from scipy import stats
import pandas as pd
from pandas.core.series import remove_na
import matplotlib as mpl
from matplotlib.collections import PatchCollection
import matplotlib.patches as Patches
import matplotlib.pyplot as... | mit |
sourabhdalvi/sourabhdalvi.github.io | markdown_generator/publications.py | 197 | 3887 |
# coding: utf-8
# # Publications markdown generator for academicpages
#
# Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook, with the core python code in publications.py. Run either from the `markdown_g... | mit |
franzpl/sweep | peak_to_noise_ratio/pnr_depends_on_excitation_length.py | 2 | 1779 | #!/usr/bin/env python3
"""The influence of excitation length.
"""
import sys
sys.path.append('..')
import measurement_chain
import plotting
import calculation
import ir_imitation
import generation
import matplotlib.pyplot as plt
import windows
from scipy.signal import lfilter, fftconvolve
import numpy as np
# Pa... | mit |
wavelets/BayesDataAnalysisWithPyMC | BayesDataAnalysisWithPymc/plot_post.py | 2 | 2926 | # -*- coding: utf-8 -*-
'''Plot the histogram of the posterior distribution sample,
with the mean and the 95% HDI.
Adaptation of the R code from "Doing Bayesian Data Analysis",
by John K. Krushcke.
More info: http://doingbayesiandataanalysis.blogspot.com.br/
Histogram code based on (copied from!) 'Probabilistic... | mit |
sergeykolychev/mxnet | example/reinforcement-learning/ddpg/strategies.py | 42 | 2473 | # 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 u... | apache-2.0 |
abinashpanda/pgmpy | pgmpy/models/NaiveBayes.py | 3 | 7053 | from pgmpy.independencies import Independencies
from pgmpy.models import BayesianModel
class NaiveBayes(BayesianModel):
"""
Class to represent Naive Bayes.
Subclass of Bayesian Model.
Model holds directed edges from one parent node to multiple
children nodes only.
Parameters
----------
... | mit |
nikste/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py | 75 | 29377 | # 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 |
rahuldhote/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 244 | 9986 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
aleksandr-bakanov/astropy | astropy/visualization/wcsaxes/ticklabels.py | 4 | 8121 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from matplotlib import rcParams
from matplotlib.text import Text
from .frame import RectangularFrame
def sort_using(X, Y):
return [x for (y, x) in sorted(zip(Y, X))]
class TickLabels(Text):
def __init__(self, frame, *arg... | bsd-3-clause |
offirt/SMS-Broadcast | sms_broadcast.py | 1 | 2314 | import pandas as pd
import sys, getopt
from twilio.rest import Client
def main(argv):
file, nameColumn, phoneColumn, template, sendReal, twilioSid, twilioToken, twilioFrom = parseArgs(argv)
rows = pd.read_csv(file)
rows = rows[[nameColumn, phoneColumn]]
client = Client(twilioSid, twilioToken)
f... | mit |
amolkahat/pandas | asv_bench/benchmarks/indexing.py | 5 | 10167 | import warnings
import numpy as np
import pandas.util.testing as tm
from pandas import (Series, DataFrame, Panel, MultiIndex,
Int64Index, UInt64Index, Float64Index,
IntervalIndex, CategoricalIndex,
IndexSlice, concat, date_range)
class NumericSeriesIndexing... | bsd-3-clause |
public-ink/public-ink | server/appengine/lib/matplotlib/tight_layout.py | 4 | 13206 | """
This module provides routines to adjust subplot params so that subplots are
nicely fit in the figure. In doing so, only axis labels, tick labels, axes
titles and offsetboxes that are anchored to axes are currently considered.
Internally, it assumes that the margins (left_margin, etc.) which are
differences between... | gpl-3.0 |
JunkieStyle/corgi | corgi/experiment.py | 1 | 14841 | import time
import xgboost as xgb
import lightgbm as lgb
import catboost as cat
import pandas as pd
import numpy as np
from hyperopt import fmin, tpe, Trials, STATUS_OK, STATUS_FAIL, hp
import os
from os.path import join
from os import listdir
from sklearn.model_selection import StratifiedKFold, TimeSeriesSplit, KFold... | mit |
OshynSong/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 |
sinhrks/pandas-ml | doc/source/conf.py | 3 | 8742 | # -*- coding: utf-8 -*-
#
# pandas-ml documentation build configuration file, created by
# sphinx-quickstart on Sun Feb 22 14:58:37 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
#... | bsd-3-clause |
espensirnes/paneltime | paneltime/regstats.py | 2 | 9494 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#This module calculates statistics and saves it to a file
import statproc as stat
import numpy as np
import regprocs as rp
from scipy import stats as scstats
import csv
import os
import sys
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
imp... | gpl-3.0 |
jakevdp/seaborn | seaborn/rcmod.py | 1 | 15625 | """Functions that alter the matplotlib rc dictionary on the fly."""
import numpy as np
import matplotlib as mpl
from . import palettes
_style_keys = (
"axes.facecolor",
"axes.edgecolor",
"axes.grid",
"axes.axisbelow",
"axes.linewidth",
"axes.labelcolor",
"grid.color",
"grid.linestyle... | bsd-3-clause |
botswana-harvard/bcpp-interview | bcpp_interview/management/commands/extract_from_bcpp.py | 1 | 5914 | """
A few methods to help extract data from bcpp.
either you are on the bcpp server console or have a bcpp virtualenv and tunnel to bcpp mysql
To get locator data from bcpp:
from bcpp_interview import get_locator_dataframe, identity, decrypt_locator, get_consent_dataframe, decrypt_consent
df_locator = get_lo... | gpl-3.0 |
marqh/iris | lib/iris/tests/test_plot.py | 3 | 32979 | # (C) British Crown Copyright 2010 - 2017, Met Office
#
# This file is part of Iris.
#
# Iris is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option) any l... | lgpl-3.0 |
citiususc/construe | construe/knowledge/abstraction_patterns/segmentation/pwave.py | 1 | 15026 | # -*- coding: utf-8 -*-
# pylint: disable=C0326
"""
Created on Tue Sep 30 12:31:04 2014
This module contains the definition of the P wave abstraction pattern.
@author: T. Teijeiro
"""
import pickle
import numpy as np
from pathlib import Path
import sklearn.preprocessing as preproc
import construe.knowledge.observab... | agpl-3.0 |
iABC2XYZ/abc | Temp/untitled14.py | 1 | 1845 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 10 17:39:17 2017
@author: A
"""
import numpy as np
import matplotlib.pyplot as plt
def ReconstructionFFT(s):
from scipy.fftpack import fft
fs=1
N=len(s)
#fs=N
n=np.linspace(0,N-1,N)
f=n*fs/N
t=n/fs
sFFT=fft(s,... | gpl-3.0 |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/scipy/stats/_multivariate.py | 13 | 99071 | #
# Author: Joris Vankerschaver 2013
#
from __future__ import division, print_function, absolute_import
import math
import numpy as np
import scipy.linalg
from scipy.misc import doccer
from scipy.special import gammaln, psi, multigammaln
from scipy._lib._util import check_random_state
from scipy.linalg.blas import dro... | mit |
felipemontefuscolo/bitme | research/ar.py | 1 | 2635 | from statsmodels.tsa.ar_model import AR
from statsmodels.graphics.tsaplots import *
from matplotlib.pyplot import *
import scipy.signal as sg # for scipy.signal.welch
from scipy.ndimage.interpolation import shift
# this script is just to understand how AR model works
def diff(x, n, remove_nan=True):
r = x - shi... | mpl-2.0 |
humdings/zipline | tests/pipeline/base.py | 2 | 5349 | """
Base class for Pipeline API unit tests.
"""
import numpy as np
from numpy import arange, prod
from pandas import DataFrame, Timestamp
from six import iteritems
from zipline.utils.compat import wraps
from zipline.pipeline.engine import SimplePipelineEngine
from zipline.pipeline import ExecutionPlan
from zipline.pip... | apache-2.0 |
alan-mnix/MLFinalProject | final.py | 1 | 3568 | import heapq, itertools
import nltk, re, numpy
import json
import re
import sklearn
import sklearn.metrics
import sklearn.svm
from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer
import time
from nltk import word_tokenize
from nltk.stem import WordNetLemmatizer
import sys
#import ps... | gpl-2.0 |
mogeiwang/nest | pynest/examples/plot_weight_matrices.py | 4 | 3194 | # -*- coding: utf-8 -*-
#
# plot_weight_matrices.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 Li... | gpl-2.0 |
ilo10/scikit-learn | examples/svm/plot_rbf_parameters.py | 57 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radius Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
Scan-o-Matic/scanomatic | scanomatic/qc/compilation_results.py | 1 | 9311 | from __future__ import absolute_import
import glob
from itertools import izip
import os
import re
import time
from types import StringTypes
from matplotlib import pyplot as plt
import numpy as np
from scanomatic.io.movie_writer import MovieWriter
from scanomatic.models.factories.compile_project_factory import (
... | gpl-3.0 |
aetilley/scikit-learn | examples/linear_model/plot_logistic_path.py | 349 | 1195 | #!/usr/bin/env python
"""
=================================
Path with L1- Logistic Regression
=================================
Computes path on IRIS dataset.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from datetime import datetime
import numpy as np
import... | bsd-3-clause |
CUFCTFACE/face-recognition | scripts/plot.py | 4 | 2754 | #!/usr/bin/python
# Create plots for log files from experiments.
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import os
def load_logfile(fname):
fp = open(fname, "rt")
X = [[], [], [], []]
for line in fp:
data = line.split()
for i in xrange(4):
X[i].append(float(data[i]))
fp.c... | mit |
OshynSong/scikit-learn | sklearn/utils/tests/test_seq_dataset.py | 93 | 2471 | # Author: Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
#
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
from sklearn.utils.seq_dataset import ArrayDataset, CSRDataset
from sklearn.datasets import load_iris
from numpy.testing import assert_array_equal
from nose.tools import assert_equal
iris =... | bsd-3-clause |
sowe9385/qiime | scripts/plot_taxa_summary.py | 15 | 12355 | #!/usr/bin/env python
# File created on 19 Jan 2011
from __future__ import division
__author__ = "Jesse Stombaugh"
__copyright__ = "Copyright 2011, The QIIME project"
__credits__ = ["Jesse Stombaugh", "Julia Goodrich", "Justin Kuczynski",
"John Chase", "Jose Antonio Navas Molina"]
__license__ = "GPL"
__... | gpl-2.0 |
vivekpatani/recommendation-system | UI.py | 3 | 3385 | """
Simple User Interface
"""
from movielens import *
from sklearn.cluster import KMeans
import numpy as np
import pickle
import random
import sys
import time
user = []
item = []
d = Dataset()
d.load_users("data/u.user", user)
d.load_items("data/u.item", item)
n_users = len(user)
n_items = len(item)
utility_matrix... | mit |
subutai/htmresearch | projects/sdr_paper/scalar_sdrs.py | 2 | 20118 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2019, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This progra... | agpl-3.0 |
cwu2011/scikit-learn | sklearn/semi_supervised/label_propagation.py | 128 | 15312 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
thunlp/OpenNE | src/openne/tadw.py | 1 | 4372 | from __future__ import print_function
import math
import numpy as np
from numpy import linalg as la
from sklearn.preprocessing import normalize
from .gcn.utils import *
class TADW(object):
def __init__(self, graph, dim, lamb=0.2):
self.g = graph
self.lamb = lamb
self.dim = int(dim/2)
... | mit |
ahoyosid/scikit-learn | sklearn/ensemble/partial_dependence.py | 6 | 14973 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals im... | bsd-3-clause |
taylort7147/udacity-projects | titanic_survival_exploration/titanic_visualizations.py | 24 | 5425 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def filter_data(data, condition):
"""
Remove elements that do not match the condition provided.
Takes a data list as input and returns a filtered list.
Conditions should be a list of strings of the following format:
'<field> <... | mit |
cbmoore/statsmodels | statsmodels/graphics/functional.py | 31 | 14477 | """Module for functional boxplots."""
from statsmodels.compat.python import combinations, range
import numpy as np
from scipy import stats
from scipy.misc import factorial
from . import utils
__all__ = ['fboxplot', 'rainbowplot', 'banddepth']
def fboxplot(data, xdata=None, labels=None, depth=None, method='MBD',
... | bsd-3-clause |
karstenw/nodebox-pyobjc | examples/Extended Application/matplotlib/examples/event_handling/figure_axes_enter_leave.py | 1 | 1669 | """
=======================
Figure Axes Enter Leave
=======================
Illustrate the figure and axes enter and leave events by changing the
frame colors on enter and leave
"""
from __future__ import print_function
import matplotlib.pyplot as plt
def enter_axes(event):
print('enter_axes', event.inaxes)
... | mit |
Achuth17/scikit-learn | examples/cluster/plot_kmeans_digits.py | 53 | 4524 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
adybbroe/atrain_match | atrain_match/reshaped_files_scr/pps_vrreport_ctth_stats.py | 2 | 34243 | """Read all matched data and make some plotting
"""
import os
import re
from glob import glob
import numpy as np
from utils.get_flag_info import get_calipso_clouds_of_type_i_feature_classification_flags_one_layer
import matplotlib.pyplot as plt
import matplotlib
from utils.get_flag_info import (get_calipso_low_medium_... | gpl-3.0 |
pgoeser/gnuradio | gr-utils/src/python/plot_data.py | 10 | 5841 | #
# Copyright 2007,2008,2011 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)
# any later ... | gpl-3.0 |
areeda/gwpy | gwpy/segments/flag.py | 2 | 61756 | # -*- coding: utf-8 -*-
# Copyright (C) Duncan Macleod (2014-2020)
#
# This file is part of GWpy.
#
# GWpy 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)... | gpl-3.0 |
kashif/scikit-learn | examples/classification/plot_lda_qda.py | 29 | 4952 | """
====================================================================
Linear and Quadratic Discriminant Analysis with confidence ellipsoid
====================================================================
Plot the confidence ellipsoids of each class and decision boundary
"""
print(__doc__)
from scipy import lin... | bsd-3-clause |
evanbiederstedt/RRBSfun | epiphen/cll_tests/annotation_scripts/total_cells_annotate_columns.py | 1 | 34503 | import glob
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', 50) # print all rows
import os
os.chdir("/gpfs/commons/home/biederstedte-934/evan_projects/correct_phylo_files")
normalB = glob.glob("binary_position_RRBS_normal_B_cell*")
mcell = glob.glob("binary_position_RRBS_NormalBCD19pCD27... | mit |
thilbern/scikit-learn | sklearn/datasets/base.py | 12 | 17971 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import shutil
from os import environ
from os.pa... | bsd-3-clause |
frank-tancf/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 |
pandas-ml/pandas-ml | pandas_ml/confusion_matrix/cm.py | 3 | 1079 | #!/usr/bin/python
# -*- coding: utf8 -*-
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
from pandas_ml.confusion_matrix.abstract import ConfusionMatrixAbstract
"""
A Python Pandas Confusion matrix implementation
"""
class ConfusionMa... | bsd-3-clause |
zihua/scikit-learn | examples/gaussian_process/plot_gpr_prior_posterior.py | 104 | 2878 | """
==========================================================================
Illustration of prior and posterior Gaussian process for different kernels
==========================================================================
This example illustrates the prior and posterior of a GPR with different
kernels. Mean, st... | bsd-3-clause |
jpautom/scikit-learn | sklearn/feature_selection/tests/test_chi2.py | 56 | 2400 | """
Tests for chi2, currently the only feature selection function designed
specifically to work with sparse matrices.
"""
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
import scipy.stats
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.feature_selection.univariate_selection im... | bsd-3-clause |
DGrady/pandas | pandas/io/s3.py | 16 | 1222 | """ s3 support for remote file interactivity """
from pandas import compat
try:
import s3fs
from botocore.exceptions import NoCredentialsError
except:
raise ImportError("The s3fs library is required to handle s3 files")
if compat.PY3:
from urllib.parse import urlparse as parse_url
else:
from urlpar... | bsd-3-clause |
lifemapper/LmQGIS | dev/trees/newStatsCov.py | 2 | 12533 | import numpy as np
from itertools import combinations
import matplotlib.pyplot as plt
from csv import reader
import math
import os
#ll = [[1,1,1,0,0,0],[0,1,1,1,0,1],[1,1,1,0,0,0],[1,1,1,0,0,0],
# [0,1,1,1,1,0],[1,1,1,0,0,1],[1,1,1,1,0,0]]
def buildCovarianceAvgPerCell():
"""
@summary: this is the averag... | gpl-2.0 |
drandykass/fatiando | fatiando/gravmag/transform.py | 3 | 21318 | """
Potential field transformations, like upward continuation and derivatives.
.. note:: Most, if not all, functions here required gridded data.
**Transformations**
* :func:`~fatiando.gravmag.transform.upcontinue`: Upward continuation of
gridded potential field data on a level surface.
* :func:`~fatiando.gravmag.t... | bsd-3-clause |
aabadie/scikit-learn | sklearn/feature_selection/__init__.py | 140 | 1302 | """
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 |
pypot/scikit-learn | sklearn/ensemble/gradient_boosting.py | 126 | 65552 | """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... | bsd-3-clause |
cuemacro/findatapy | findatapy/market/datavendorweb.py | 1 | 108889 | __author__ = 'saeedamen' # Saeed Amen
#
# Copyright 2016 Cuemacro
#
# 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 ... | apache-2.0 |
odoousers2014/addons-yelizariev | sugarcrm_migration/import_kashflow.py | 16 | 21779 | # -*- coding: utf-8 -*-
import logging
_logger = logging.getLogger(__name__)
from openerp.exceptions import except_orm
try:
import MySQLdb
import MySQLdb.cursors
from pandas import merge, DataFrame
except ImportError:
pass
from openerp.addons.import_framework.import_base import import_base
from openerp... | lgpl-3.0 |
xzh86/scikit-learn | examples/cluster/plot_lena_ward_segmentation.py | 271 | 1998 | """
===============================================================
A demo of structured Ward hierarchical clustering on Lena image
===============================================================
Compute the segmentation of a 2D image with Ward hierarchical
clustering. The clustering is spatially constrained in order
... | bsd-3-clause |
eg-zhang/scikit-learn | examples/tree/plot_tree_regression.py | 206 | 1476 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... | bsd-3-clause |
sunpy/solarbextrapolation | solarbextrapolation/analyticalmodels/base.py | 1 | 8497 | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 28 19:30:22 2015
@author: alex_
"""
# General Imports
import matplotlib as mpl
mpl.use('TkAgg') # Force mpl backend not to use qt. Else we have a conflict.
import numpy as np
#import pickle
import time
from datetime import datetime
#from collections import namedtuple
imp... | mit |
DanielJMaher/compliance-checker | compliance_checker/tests/test_acdd.py | 2 | 15456 | from compliance_checker.acdd import ACDD1_1Check, ACDD1_3Check
from compliance_checker.tests.resources import STATIC_FILES
from compliance_checker.tests import BaseTestCase
from netCDF4 import Dataset
import os
def to_singleton_var(l):
'''
Get the first value of a list if this implements iterator protocol and... | apache-2.0 |
apdavison/IzhikevichModel | PyNN/old/test_IF_cond_exp.py | 2 | 2394 |
from pyNN.random import RandomDistribution, NumpyRNG
from pyNN.neuron import *
from pyNN.utility import get_script_args, Timer, ProgressBar, init_logging, normalized_filename
import matplotlib.pyplot as plt
import numpy as np
timeStep = 0.01
setup(timestep=timeStep, min_delay=0.5)
tau_m = 20.... | bsd-3-clause |
stuart-knock/tvb-library | contrib/from_articles/region_deterministic_bnm_sj3d_a.py | 5 | 6407 | # -*- coding: utf-8 -*-
"""
What:
Reproduces Figures 23 and 24of Sanz-Leon P., Knock, S. A., Spiegler, A. and Jirsa V.
Mathematical framework for large-scale brain network modelling in The Virtual Brain.
Neuroimage, 2014, (in review)
Needs:
A working installation of tvb
Run:
python region_det... | gpl-2.0 |
CFIS-Octarine/octarine | src/ossos_tests/test_ossos/test_fitsviewer/test_interaction.py | 2 | 7159 | __author__ = "David Rusk <drusk@uvic.ca>"
import unittest
from hamcrest import assert_that, instance_of, equal_to, none
from matplotlib.backend_bases import MouseEvent as MPLMouseEvent
from mock import Mock, patch
from ossos.fitsviewer.displayable import ImageSinglet
from ossos.fitsviewer.interaction import Interact... | gpl-3.0 |
ericmjl/bokeh | examples/reference/models/radio_button_group_server.py | 1 | 1317 | ## Bokeh server for Radio Button Group
import pandas as pd
from bokeh.io import curdoc
from bokeh.layouts import row
from bokeh.models import ColumnDataSource, RadioButtonGroup
from bokeh.plotting import figure
x=[3,4,6,12,10,1,5,6,3,8]
y=[7,1,3,4,1,6,10,4,10,3]
label=['Red', 'Orange', 'Red', 'Orange','Red', 'Orange'... | bsd-3-clause |
sgiavasis/nipype | build_docs.py | 10 | 6995 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Code to build the documentation in the setup.py
To use this code, run::
python setup.py build_sphinx
"""
from __future__ import print_function
# Standard library imports
import sys
import os
fro... | bsd-3-clause |
ClementPhil/deep-learning | image-classification/helper.py | 155 | 5631 | import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer
def _load_label_names():
"""
Load the label names from file
"""
return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def load_cfar10_batch(ci... | mit |
FHannes/intellij-community | python/testData/debug/test_dataframe.py | 23 | 1309 | import pandas as pd
import numpy as np
df1 = pd.DataFrame({'row': [0, 1, 2],
'One_X': [1.1, 1.1, 1.1],
'One_Y': [1.2, 1.2, 1.2],
'Two_X': [1.11, 1.11, 1.11],
'Two_Y': [1.22, 1.22, 1.22]})
print(df1) ###line 8
df2 = pd.DataFrame({'row': [0... | apache-2.0 |
low-sky/GAS | ah_bootstrap.py | 31 | 36163 | """
This bootstrap module contains code for ensuring that the astropy_helpers
package will be importable by the time the setup.py script runs. It also
includes some workarounds to ensure that a recent-enough version of setuptools
is being used for the installation.
This module should be the first thing imported in th... | mit |
thorwhalen/ut | ml/prep/relabeling.py | 1 | 3943 |
from sklearn.cluster import SpectralClustering, AgglomerativeClustering, KMeans
from sklearn.neighbors import KNeighborsClassifier
from sklearn.base import ClusterMixin
from numpy import max, array
from scipy.spatial.distance import cdist
import itertools
class DataBasedLabeling(ClusterMixin):
pass
class Kn... | mit |
tombstone/models | official/vision/detection/utils/object_detection/visualization_utils.py | 1 | 29252 | # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
mmottahedi/neuralnilm_prototype | scripts/e551.py | 2 | 7824 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource)
from neuralnilm.source import (standardise, discretize, fdiff, power_and_fdiff,
... | mit |
agentfog/qiime | scripts/print_qiime_config.py | 15 | 35150 | #!/usr/bin/env python
from __future__ import division
__author__ = "Jens Reeder"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["Jens Reeder", "Dan Knights", "Antonio Gonzalez Pena",
"Justin Kuczynski", "Jai Ram Rideout", "Greg Caporaso",
"Emily TerAvest"]
__license__ ... | gpl-2.0 |
schets/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py | 221 | 5517 | """
Testing for the gradient boosting loss functions and initial estimators.
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.utils import check_random_state
from ... | bsd-3-clause |
vivekmishra1991/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
marcocaccin/scikit-learn | benchmarks/bench_plot_svd.py | 325 | 2899 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklearn.datasets.s... | bsd-3-clause |
liuwenf/moose | modules/porous_flow/doc/tests/mandel.py | 6 | 8844 | #!/usr/bin/env python
# Solution to Mandel's problem as presented in
# AHD Cheng and E Detournay "A direct boundary element method for plane strain poroelasticity" International Journal of Numerical and Analytical Methods in Geomechanics 12 (1988) 551-572
import os
import sys
import numpy as np
import matplotlib.pypl... | lgpl-2.1 |
NeuroDataDesign/seelviz | Tony/clviz_web_tony_edits/densitygraph.py | 1 | 12529 | #!/usr/bin/env python
#-*- coding:utf-8 -*-
from __future__ import print_function
__author__ = 'seelviz'
# import matplotlib as mpl
# mpl.use('Agg')
from skimage import data, img_as_float
from skimage import exposure
import plotly
from plotly.graph_objs import *
import cv2
import collections as col
import math, o... | apache-2.0 |
bsipocz/scikit-image | doc/examples/plot_ssim.py | 15 | 2238 | """
===========================
Structural similarity index
===========================
When comparing images, the mean squared error (MSE)--while simple to
implement--is not highly indicative of perceived similarity. Structural
similarity aims to address this shortcoming by taking texture into account
[1]_, [2]_.
T... | bsd-3-clause |
xhqu1981/pymatgen | pymatgen/analysis/pourbaix/plotter.py | 1 | 34818 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
from __future__ import division, unicode_literals
import platform
from matplotlib.font_manager import FontProperties
from matplotlib.ticker import FormatStrFormatter
"""
This module provides classes for plot... | mit |
weixsong/algorithm | MLLearn/MLBox/logistic_regression/sms.py | 1 | 2883 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
''' 通过垃圾短信数据训练Logistic回归模型，并进行留存交叉验证
'''
import re
import random
import numpy as np
import matplotlib.pyplot as plt
from logreg_stoch_grad_ascent import LogisticRegressionClassifier
ENCODING = 'ISO-8859-1'
TRAIN_PERCENTAGE = 0.9
def get_doc_vector(words, vocabulary):
... | mit |
DebbyChiang/prospecting-reformat | For-LeadFuze/emails.py | 1 | 1868 | #import libraries
import pandas as pd
import numpy as np
#import file
prospects = pd.DataFrame(pd.read_csv("/Users/open/Desktop/department.csv"))
#Drop rows with no company
prospects = prospects.dropna(subset=['Company'])
#Copy orange emails into 'Test Email 1'
prospects['Test Email 1'] = ""
prospects['Test Email... | gpl-2.0 |
jakirkham/lazyflow | lazyflow/operators/opDetectMissingData.py | 1 | 40074 | ###############################################################################
# lazyflow: data flow based lazy parallel computation framework
#
# Copyright (C) 2011-2014, the ilastik developers
# <team@ilastik.org>
#
# This program is free software; you can redistribute it and/o... | lgpl-3.0 |
hunering/demo-code | python/books/DLWP/3.5-reuters.py | 1 | 1163 | import numpy as np
from keras.datasets import reuters
from keras import models
from keras import layers
from keras import optimizers
from keras import losses
from keras import metrics
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from utils import init_keras, vectorize_sequ... | gpl-3.0 |
zhengwsh/InplusTrader_Linux | InplusTrader/backtestEngine/model/account/stock_account.py | 1 | 12148 | # -*- coding: utf-8 -*-
#
# Copyright 2017 Ricequant, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | mit |
huzq/scikit-learn | sklearn/ensemble/_forest.py | 2 | 95540 | """
Forest of trees-based ensemble methods.
Those methods include random forests and extremely randomized trees.
The module structure is the following:
- The ``BaseForest`` base class implements a common ``fit`` method for all
the estimators in the module. The ``fit`` method of the base ``Forest``
class calls th... | bsd-3-clause |
printedheart/simpleai | simpleai/machine_learning/reinforcement_learning.py | 5 | 6345 | # -*- coding: utf-8 -*-
from collections import defaultdict, Counter
import math
import random
from simpleai.search.utils import argmax
import pickle
try:
import matplotlib.pyplot as plt
import numpy
except:
plt = None # lint:ok
numpy = None # lint:ok
def make_at_least_n_times(optimistic_reward, min... | mit |
zfrenchee/pandas | pandas/plotting/_core.py | 1 | 99095 | # being a bit too dynamic
# pylint: disable=E1101
from __future__ import division
import warnings
import re
from collections import namedtuple
from distutils.version import LooseVersion
import numpy as np
from pandas.util._decorators import cache_readonly
from pandas.core.base import PandasObject
from pandas.core.co... | bsd-3-clause |
jungla/ICOM-fluidity-toolbox | Detectors/offline_advection/advect_particles_C_3Db_big.py | 1 | 4082 | import os, sys
import myfun
import numpy as np
import lagrangian_stats
import scipy.interpolate as interpolate
import csv
import matplotlib.pyplot as plt
import advect_functions
import fio
from intergrid import Intergrid
## READ archive (too many points... somehow)
# args: name, dayi, dayf, days
#label = 'm_25_2_512'... | gpl-2.0 |
mkowoods/deep-learning | weight-initialization/helper.py | 153 | 3649 | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def hist_dist(title, distribution_tensor, hist_range=(-4, 4)):
"""
Display histogram of a TF distribution
"""
with tf.Session() as sess:
values = sess.run(distribution_tensor)
plt.title(title)
plt.hist(values, ... | mit |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/io/json/json.py | 6 | 24553 | # pylint: disable-msg=E1101,W0613,W0603
import os
import numpy as np
import pandas._libs.json as json
from pandas._libs.tslib import iNaT
from pandas.compat import StringIO, long, u
from pandas import compat, isnull
from pandas import Series, DataFrame, to_datetime, MultiIndex
from pandas.io.common import get_filepath... | mit |
ethantang95/DIGITS | digits/dataset/generic/views.py | 3 | 7099 | # Copyright (c) 2016-2017, NVIDIA CORPORATION. All rights reserved.
from __future__ import absolute_import
import os
# Find the best implementation available
try:
from cStringIO import StringIO
except ImportError:
from StringIO import StringIO
import caffe_pb2
import flask
import matplotlib as mpl
import mat... | bsd-3-clause |
sunyihuan326/DeltaLab | Andrew_NG_learning/class_two/week_one/syh_01.py | 1 | 8056 | # coding:utf-8
'''
Created on 2017/11/10
@author: sunyihuan
'''
import numpy as np
import matplotlib.pyplot as plt
import sklearn
import sklearn.datasets
from class_two.week_one.init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation
from class_two.week_one.init_utils import update_pa... | mit |
smartscheduling/scikit-learn-categorical-tree | examples/linear_model/plot_ard.py | 248 | 2622 | """
==================================================
Automatic Relevance Determination Regression (ARD)
==================================================
Fit regression model with Bayesian Ridge Regression.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary l... | bsd-3-clause |
loli/semisupervisedforests | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
mirestrepo/voxels-at-lems | dbrec3d/bof/general/learn_categories/plot_histograms_in_one.py | 1 | 3335 | # -*- coding: utf-8 -*-
"""
Created on Mon Mar 7 14:38:46 2011
Plot pca test error vs train error
@author: -
"""
# Computes the gaussian gradients on a boxm_alpha_scene
import os;
import optparse;
import time;
import sys;
import numpy as np
import matplotlib.pyplot as plt
import glob
if __name__=="__main__":
... | bsd-2-clause |
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