repo_name stringlengths 9 55 | path stringlengths 7 120 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 169k | license stringclasses 12
values |
|---|---|---|---|---|---|
mikeireland/chronostar | projects/scocen/cmd_age_sequence_AG_with_lithium.py | 1 | 8855 | """
Plot CMDs for CUT components and show that components with higher kinematic
age show less overluminosity in comparison to others.
"""
import numpy as np
from astropy.table import Table
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.gridspec import GridSpec
from mpl_toolkits.axes... | mit |
meteorcloudy/tensorflow | tensorflow/examples/learn/text_classification_character_cnn.py | 33 | 5463 | # 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 |
saleiro/SemEval2017-Task5 | Code/sentiment_analysis.py | 1 | 7352 | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction import DictVectorizer
import numpy as np
from sklearn.metrics import mean_absolute_error
from sklearn.metrics.pairwise import cosine_similarity
import gensim
import argparse
from scipy.sparse import hstack
from sklearn.ensemble ... | mit |
ywcui1990/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_qt4agg.py | 70 | 4985 | """
Render to qt from agg
"""
from __future__ import division
import os, sys
import matplotlib
from matplotlib.figure import Figure
from backend_agg import FigureCanvasAgg
from backend_qt4 import QtCore, QtGui, FigureManagerQT, FigureCanvasQT,\
show, draw_if_interactive, backend_version, \
NavigationToolba... | agpl-3.0 |
dr-leo/pandaSDMX | pandasdmx/source/__init__.py | 1 | 6250 | from pydantic import HttpUrl
from enum import Enum
from importlib import import_module, resources
import json
from typing import Any, Dict, Union, Optional
from pandasdmx.model import DataStructureDefinition
from pandasdmx.util import BaseModel, Resource, validator
sources: Dict[str, "Source"] = {}
DataContentType ... | apache-2.0 |
Kate-Willett/HadISDH_Build | MakeAreaAvgTS.py | 1 | 40798 | # PYTHON 3
#
# Author: Kate Willett
# Created: 4 March 2019
# Last update: 15 April 2019
# Location: /data/local/hadkw/HADCRUH2/UPDATE2014/PROGS/PYTHON/
# GitHub: https://github.com/Kate-Willett/PYTHON
# -----------------------
# CODE PURPOSE AND OUTPUT
# -----------------------
# This code reads in monthly mean... | cc0-1.0 |
evgchz/scikit-learn | sklearn/datasets/species_distributions.py | 19 | 7870 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
pavlovml/tensorflow | tensorflow/python/client/notebook.py | 5 | 3918 | """Notebook front-end to TensorFlow.
When you run this binary, you'll see something like below, which indicates
the serving URL of the notebook:
The IPython Notebook is running at: http://127.0.0.1:8888/
Press "Shift+Enter" to execute a cell
Press "Enter" on a cell to go into edit mode.
Press "Escape" to go back ... | apache-2.0 |
nal-epfl/line-sigcomm14 | plotting-scripts-new/plot-edge-seq-cong-prob.py | 1 | 9288 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
# Install dependencies:
# sudo pip install matplotlib
import colorsys
import copy
import getopt
import json
from nicePlot import nicePlot
from line import *
import math
import numpy
import os
import pprint
import re
import subprocess
import sys
## Params
inputFile = 'e... | gpl-2.0 |
devs1991/test_edx_docmode | venv/lib/python2.7/site-packages/scipy/stats/tests/test_morestats.py | 4 | 34938 | # Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.random import RandomState
from numpy.testing import (TestCase, run_module_suite, assert_array_equal,
... | agpl-3.0 |
wjlei1990/spaceweight | setup.py | 1 | 1886 | import sys
from setuptools import setup, find_packages
from setuptools.command.test import test as TestCommand
class PyTest(TestCommand):
user_options = [('pytest-args=', 'a', "Arguments to pass to py.test")]
def initialize_options(self):
TestCommand.initialize_options(self)
self.pytest_args ... | gpl-3.0 |
shakeh/bridge | res-plotting/Residual_Plotting.py | 2 | 2481 | # Residual_Plotting.py
# A script that takes in an Output_for_plotting_[PULSAR].tim file created by Average_Epochs.py, and an output directory
# as a result, it creates a plot of pulsar residuals, stored in the output directory
# sample input:
# python Residual_Plotting.py /Users/fkeri/Desktop/Output_for_plotting_B1855... | apache-2.0 |
srjoglekar246/sympy | examples/intermediate/sample.py | 3 | 3446 | """
Utility functions for plotting sympy functions.
See examples\mplot2d.py and examples\mplot3d.py for usable 2d and 3d
graphing functions using matplotlib.
"""
from numpy import repeat, arange, empty, ndarray, array
from sympy import Symbol, Basic, Rational, I, sympify
def sample2d(f, x_args):
"""
Samples ... | bsd-3-clause |
tomasreimers/tensorflow-emscripten | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 18 | 13185 | # 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 |
markovg/nest-simulator | pynest/nest/voltage_trace.py | 18 | 7823 | # -*- coding: utf-8 -*-
#
# voltage_trace.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, ... | gpl-2.0 |
dalejung/trtools | trtools/core/select.py | 1 | 2914 | """
Collections of tools to quickly select rows/items
"""
import collections
import warnings
import numpy as np
from pandas import Panel, DataFrame, MultiIndex, Series, Timestamp
from pandas.core.indexing import _IXIndexer
from trtools.monkey import patch, patch_prop
@patch(DataFrame, 'cols')
def _cols(self, *a... | mit |
magne-max/zipline-ja | zipline/data/bundles/yahoo.py | 1 | 6260 | import os
import numpy as np
import pandas as pd
from pandas_datareader.data import DataReader
import requests
from zipline.utils.cli import maybe_show_progress
from .core import register
def _cachpath(symbol, type_):
return '-'.join((symbol.replace(os.path.sep, '_'), type_))
def yahoo_equities(symbols, start... | apache-2.0 |
bhargav/scikit-learn | benchmarks/bench_isolation_forest.py | 40 | 3136 | """
==========================================
IsolationForest benchmark
==========================================
A test of IsolationForest on classical anomaly detection datasets.
"""
print(__doc__)
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationFore... | bsd-3-clause |
ngoix/OCRF | examples/linear_model/plot_bayesian_ridge.py | 50 | 2733 | """
=========================
Bayesian Ridge Regression
=========================
Computes a Bayesian Ridge Regression on a synthetic dataset.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the coefficient
weights are slightly shift... | bsd-3-clause |
nlpub/russe-evaluation | russe/evaluation/evaluate.py | 1 | 4899 | #!/usr/bin/env python
import argparse
from pandas import read_csv
from scipy.stats import pearsonr, spearmanr
from os.path import splitext
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import average_precision_score, precision_recall_curve, \
accura... | mit |
mlassnig/pilot | Experiment.py | 3 | 37135 | # Class definition:
# Experiment
# This class is the main experiment class; ATLAS etc will inherit from this class
# Instances are generated with ExperimentFactory
# Subclasses should implement all needed methods prototyped in this class
# Note: not compatible with Singleton Design Pattern due to the subclass... | apache-2.0 |
MRod5/pyturb | src/pyturb/gas_models/gas_mixture.py | 1 | 9198 | """
gas_mixture:
------------
Gas mixture of ideal gases. The approach for the ideal gases may be Perfect or Semiperfect.
MRodriguez 2020
"""
import pyturb.utils.constants as cts
from pyturb.gas_models.gas import Gas
from pyturb.gas_models.perfect_ideal_gas import PerfectIdealGas
from pyturb.gas_models.semiperfect_i... | mit |
marianotepper/dask | dask/array/core.py | 2 | 59930 | from __future__ import absolute_import, division, print_function
import operator
from operator import add, getitem
import inspect
from numbers import Number
from collections import Iterable
from bisect import bisect
from itertools import product, count
from collections import Iterator
from functools import partial, wr... | bsd-3-clause |
pprett/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | 25 | 16022 | from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
import scipy.sparse as sp
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing imp... | bsd-3-clause |
mxjl620/scikit-learn | sklearn/discriminant_analysis.py | 32 | 27308 | """
Linear Discriminant Analysis and Quadratic Discriminant Analysis
"""
# Authors: Clemens Brunner
# Martin Billinger
# Matthieu Perrot
# Mathieu Blondel
# License: BSD 3-Clause
from __future__ import print_function
import warnings
import numpy as np
from scipy import linalg
from .extern... | bsd-3-clause |
ilyes14/scikit-learn | examples/svm/plot_separating_hyperplane_unbalanced.py | 329 | 1850 | """
=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================
Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.
We first find the separating plane with a plain SVC and then plot
(dashed) the ... | bsd-3-clause |
mhue/scikit-learn | sklearn/ensemble/__init__.py | 217 | 1307 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification and regression.
"""
from .base import BaseEnsemble
from .forest import RandomForestClassifier
from .forest import RandomForestRegressor
from .forest import RandomTreesEmbedding
from .forest import ExtraTreesClassifier
from .fores... | bsd-3-clause |
cyber-meow/Robotic_state_representation_learning | inter/interaction.py | 1 | 2565 |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from inter.interfaces import Environment, Bot
class Interaction(object):
"""
This is rather for test purposes once the robot has learned something
from its experience.
For the main interaction class please lo... | mit |
f3r/scikit-learn | sklearn/metrics/cluster/tests/test_supervised.py | 41 | 8901 | import numpy as np
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import homogeneity_score
from sklearn.metrics.cluster import completeness_score
from sklearn.metrics.cluster import v_measure_score
from sklearn.metrics.cluster import homogeneity_completeness_v_measure
from sklearn... | bsd-3-clause |
TiedNets/TiedNets | plot_result_bars.py | 1 | 4139 | __author__ = 'Agostino Sturaro'
import os
import csv
import numpy as np
import matplotlib.pyplot as plt
# BEGIN user defined variables
input_fpath = os.path.normpath('../Simulations/MN_nets/MN_rnd_atk_reasons.tsv')
output_fpath = os.path.normpath('../Simulations/MN_nets/MN_rnd_atk_reasons.pdf')
# we need a single ta... | gpl-3.0 |
UNR-AERIAL/scikit-learn | sklearn/datasets/svmlight_format.py | 114 | 15826 | """This module implements a loader and dumper for the svmlight format
This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.
The first element of each line can be used to store a target variable to
predict.
This format is used as the... | bsd-3-clause |
wanglei828/apollo | modules/tools/plot_planning/imu_speed_jerk.py | 1 | 3753 | #!/usr/bin/env python
###############################################################################
# Copyright 2019 The Apollo 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 ... | apache-2.0 |
harisbal/pandas | pandas/tests/io/sas/test_sas7bdat.py | 4 | 8326 | import pandas as pd
from pandas.compat import PY2
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.errors import EmptyDataError
import os
import io
import numpy as np
import pytest
# https://github.com/cython/cython/issues/1720
@pytest.mark.filterwarnings("ignore:can't resolve pa... | bsd-3-clause |
q1ang/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 |
endolith/scikit-image | doc/examples/edges/plot_canny.py | 4 | 1602 | """
===================
Canny edge detector
===================
The Canny filter is a multi-stage edge detector. It uses a filter based on the
derivative of a Gaussian in order to compute the intensity of the gradients.The
Gaussian reduces the effect of noise present in the image. Then, potential
edges are thinned dow... | bsd-3-clause |
dyoung418/tensorflow | tensorflow/examples/learn/text_classification_character_rnn.py | 8 | 4104 | # 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 |
belltailjp/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
yuanagain/seniorthesis | venv/lib/python2.7/site-packages/scipy/stats/_stats_mstats_common.py | 12 | 8157 | from collections import namedtuple
import numpy as np
from . import distributions
__all__ = ['_find_repeats', 'linregress', 'theilslopes']
def linregress(x, y=None):
"""
Calculate a linear least-squares regression for two sets of measurements.
Parameters
----------
x, y : array_like
T... | mit |
yunfeilu/scikit-learn | examples/ensemble/plot_gradient_boosting_regression.py | 227 | 2520 | """
============================
Gradient Boosting regression
============================
Demonstrate Gradient Boosting on the Boston housing dataset.
This example fits a Gradient Boosting model with least squares loss and
500 regression trees of depth 4.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prett... | bsd-3-clause |
qjcg/dotfiles | .ipython/profile_default/ipython_config.py | 1 | 18940 | # Configuration file for ipython.
c = get_config()
#------------------------------------------------------------------------------
# InteractiveShellApp configuration
#------------------------------------------------------------------------------
# A Mixin for applications that start InteractiveShell instances.
#
#... | gpl-3.0 |
schreiberx/sweet | mule_local/python/mule_local/rexi/brexi/BREXI.py | 1 | 4254 | #! /usr/bin/env python3
import sys
import numpy
import mule_local.rexi.brexi.rk_co as rk_co
import mule_local.rexi.EFloat as ef
from mule_local.rexi.REXICoefficients import *
from mule_local.rexi.Functions import *
class BREXI:
def __init__(
self,
efloat_mode = None,
):
self.eflo... | mit |
226262/Neural-Network-Digit-Recognition | plotter.py | 1 | 3993 | import os
import numpy
import matplotlib.pyplot as plt
import pygame, random
class Plotter:
width=300
height=width
array=numpy.full((width,height),0)
xMin=width
xMax=0
yMin=height
yMax=0
edge=0
isAnythingDrew = False
def write_rad(self,x,y,promien):
... | gpl-3.0 |
Tong-Chen/scikit-learn | examples/cluster/plot_dict_face_patches.py | 12 | 2723 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | bsd-3-clause |
joshloyal/scikit-learn | sklearn/utils/validation.py | 8 | 25965 | """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 |
gfrubi/GR | figuras-editables/fig-Killing-S2.py | 2 | 2263 | from mpl_toolkits.mplot3d import axes3d
from matplotlib.pyplot import * # requiere version 1.4 !!
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d
from numpy import *
class Arrow3D(FancyArrowPatch):
def __init__(self, xs, ys, zs, *args, **kwargs):
FancyArrowPatch.__ini... | gpl-3.0 |
and2egg/philharmonic | philharmonic/explorer.py | 2 | 4700 | import itertools
import numpy as np
import pandas as pd
from philharmonic import conf
from philharmonic.simulator.simulator import run
from philharmonic.logger import info
from philharmonic.utils import loc
def _generate_range(min_value, max_value, resolution):
return np.arange(min_value, max_value + resolution,... | gpl-3.0 |
hsiaoyi0504/scikit-learn | examples/cluster/plot_kmeans_assumptions.py | 270 | 2040 | """
====================================
Demonstration of k-means assumptions
====================================
This example is meant to illustrate situations where k-means will produce
unintuitive and possibly unexpected clusters. In the first three plots, the
input data does not conform to some implicit assumptio... | bsd-3-clause |
jjx02230808/project0223 | examples/applications/wikipedia_principal_eigenvector.py | 233 | 7819 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/sklearn/utils/arpack.py | 265 | 64837 | """
This contains a copy of the future version of
scipy.sparse.linalg.eigen.arpack.eigsh
It's an upgraded wrapper of the ARPACK library which
allows the use of shift-invert mode for symmetric matrices.
Find a few eigenvectors and eigenvalues of a matrix.
Uses ARPACK: http://www.caam.rice.edu/software/ARPACK/
"""
#... | gpl-2.0 |
Erotemic/ibeis | ibeis/algo/graph/tests/mst_debug.py | 1 | 5614 | import networkx as nx
import utool as ut
import pandas as pd
edges = {
2234: {5383: {'decision': 'match', 'reviewed_tags': ['needswork']}},
2265: {},
2280: {},
2654: {5334: {'decision': 'match',
'reviewed_tags': ['needswork', 'viewpoint', 'correctable', 'orientation']}},
5334: {26... | apache-2.0 |
zhuango/python | pandasLearning/featureProcessing.py | 2 | 4334 | import numpy as np
import pandas as pd
from math import *
from sklearn import preprocessing
# def cut(tasks):
# count = 4
# tasks['任务gps经度'] = pd.factorize(pd.qcut(tasks['任务gps经度'], count))[0]
# tasks['任务gps纬度'] = pd.factorize(pd.qcut(tasks['任务gps纬度'], count))[0]
# attribute = 'vip_count_around_33... | gpl-2.0 |
ericdill/bokeh | bokeh/cli/core.py | 42 | 16025 | from __future__ import absolute_import, print_function
import sys, os
from six.moves.urllib import request as urllib2
from six.moves import cStringIO as StringIO
import pandas as pd
try:
import click
is_click = True
except ImportError:
is_click = False
from . import help_messages as hm
from .utils import... | bsd-3-clause |
nhoffman/bioy | bioy_pkg/subcommands/classifier.py | 2 | 37986 | # This file is part of Bioy
#
# Bioy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Bioy is distributed in the hope... | gpl-3.0 |
danmergens/mi-instrument | mi/common/zpls_plot.py | 3 | 7240 | """
@package mi.common
@file mi/common/zpls_plot.py
@author Rene Gelinas
@brief ZPLSC Echogram generation for the ooicore
Release notes:
This class supports the generation of ZPLS echograms. It needs matplotlib version 1.3.1 (or higher) for the code
to display the colorbar on the right side of the figure. If matplotl... | bsd-2-clause |
AlertaDengue/InfoDenguePredict | infodenguepredict/models/cross_prediction_RQF.py | 1 | 3663 | """
This scripts implements cross disease predicitons using RQF model trained on dengue
"""
from infodenguepredict.models.quantile_forest import build_model, build_lagged_features, calculate_metrics
from infodenguepredict.data.infodengue import get_cluster_data, get_city_names
from infodenguepredict.predict_settings i... | gpl-3.0 |
apache/spark | python/pyspark/sql/tests/test_arrow.py | 15 | 27974 | #
# 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 |
ryanjmccall/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_macosx.py | 69 | 15397 | from __future__ import division
import os
import numpy
from matplotlib._pylab_helpers import Gcf
from matplotlib.backend_bases import RendererBase, GraphicsContextBase,\
FigureManagerBase, FigureCanvasBase, NavigationToolbar2
from matplotlib.cbook import maxdict
from matplotlib.figure import Figure
from matplotl... | gpl-3.0 |
Srisai85/scikit-learn | sklearn/ensemble/weight_boosting.py | 97 | 40773 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
sangwook236/SWDT | sw_dev/python/rnd/test/machine_learning/keras/keras_class_activation_map.py | 2 | 2826 | #!/usr/bin/env python
# coding: UTF-8
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.layers import UpSampling2D, Conv2D
import tensorflow.keras.applications.resnet50 as resnet
import cv2
import matplotlib.pyplot as plt
def load_img(fname, input_size, preprocess_fn):
original_img =... | gpl-3.0 |
upibhalla/moose-core | tests/python/test_rdesigneur_random_syn_input.py | 2 | 1402 | # -*- coding: utf-8 -*-
from __future__ import print_function, division
# This example demonstrates random (Poisson) synaptic input to a cell.
# Copyright (C) Upinder S. Bhalla NCBS 2018
# Released under the terms of the GNU Public License V3. No warranty.
# Changelog:
# Thursday 20 September 2018 09:53:27 AM IST
# - ... | gpl-3.0 |
camallen/aggregation | experimental/serengeti/IAAI/bySpecies.py | 2 | 1975 | #!/usr/bin/env python
__author__ = 'greg'
from nodes import setup
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats.stats import pearsonr
numUser = [5,10,15,20,25]
algPercent = []
currPercent = []
speciesList = ['elephant','zebra','warthog','impala','buffalo','wildebeest','gazelleThomsons','dikDik','... | apache-2.0 |
joeyginorio/Action-Understanding-with-Rational-Rules | model_src/grid_world.py | 1 | 9591 | # Joey Velez-Ginorio
# Gridworld Implementation
# ---------------------------------
from mdp import MDP
from grid import Grid
from scipy.stats import uniform
from scipy.stats import beta
from scipy.stats import expon
import numpy as np
import random
import pyprind
import matplotlib.pyplot as plt
class GridWorld(MDP):... | mit |
kdaily/cloudbiolinux | installed_files/ipython_config.py | 15 | 14156 | # Configuration file for ipython.
c = get_config()
c.InteractiveShell.autoindent = True
c.InteractiveShell.colors = 'Linux'
c.InteractiveShell.confirm_exit = False
c.AliasManager.user_aliases = [
('ll', 'ls -l'),
('lt', 'ls -ltr'),
]
#------------------------------------------------------------------------------
#... | mit |
IndraVikas/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 93 | 3460 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the `datasets.make_multilabel_classification` dataset
generator. Each sample consists of counts of two features (up to 50 in
total), which are differently distri... | bsd-3-clause |
thomasahle/numberlink | gen/mitm.py | 1 | 6461 | import random
from collections import Counter, defaultdict
import itertools
# starter altid (0,0) -> (0,1)
# Sti har formen [2, l, r]*, da man kan forlænge med 2, gå til venstre eller gå til højre.
T, L, R = range(3)
class Path:
def __init__(self, steps):
self.steps = steps
def xys(self, dx=0, dy=1)... | agpl-3.0 |
MikaelFuresjo/ImundboQuant | src/PreProcess.py | 1 | 169439 | """
MIT License
Copyright (c) [2016] [Mikael Furesjö]
Software = Python Scripts in the [Imundbo Quant v1.6] series
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including w... | mit |
flameOnYou/mt4plus | main.py | 1 | 6204 | #coding:utf-8
'''
读取hst文件
'''
import struct
import time
import datetime
import hstutils
import json
import threading
import logging
import initEnvironment
import tushare as ts
import pandas as pd
import traceback
import requests as rq
logging.basicConfig(level=logging.DEBUG,
format='%(as... | gpl-2.0 |
kubeflow/pipelines | components/dataset_manipulation/split_data_into_folds/in_CSV/component.py | 1 | 2735 | from kfp.components import InputPath, OutputPath, create_component_from_func
def split_table_into_folds(
table_path: InputPath('CSV'),
train_1_path: OutputPath('CSV'),
train_2_path: OutputPath('CSV'),
train_3_path: OutputPath('CSV'),
train_4_path: OutputPath('CSV'),
train_5_path: OutputPath('C... | apache-2.0 |
kuke/HAMDLE | PyCUDA/plot_first_five_Legendre.py | 1 | 1152 | #! /bin/python
import sys
import matplotlib.pyplot as plt
import numpy as np
X = np.arange(-1, 1.05, 0.05)
order = 5
def generate_Legendre_matrix(order, X):
m = X.size
Legendre_mat = [[0 for col in range(m)] for row in range(order+1)]
Legendre_mat[0] = [1 for i in range(0, m)]
Legendre_mat[1] = X
for i in rang... | gpl-3.0 |
arahuja/scikit-learn | sklearn/decomposition/tests/test_truncated_svd.py | 240 | 6055 | """Test truncated SVD transformer."""
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_raises, assert_greater,
... | bsd-3-clause |
a-doumoulakis/tensorflow | tensorflow/examples/learn/text_classification_cnn.py | 29 | 5677 | # 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 |
dennissergeev/sattools | sattools/utils.py | 2 | 6718 | # -*- coding: utf-8 -*-
"""
Auxiliary functions for sattools module
"""
from __future__ import division, print_function
import datetime
import h5py
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import pytz
default_cmap_dir = os.path.join(os.path.dirname(os.path.realpath(__file_... | mit |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/sklearn/feature_extraction/hashing.py | 24 | 5668 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: BSD 3 clause
import numbers
import numpy as np
import scipy.sparse as sp
from . import _hashing
from ..base import BaseEstimator, TransformerMixin
def _iteritems(d):
"""Like d.iteritems, but accepts any collections.Mapping."""
return d.iteritems() if... | mit |
markinho-web/markinho-web.github.io | MEFaplicado-html/estado_plano/codigos/Derivando-FuncoesFormaEstadoPlano3nos.py | 2 | 22792 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 10 14:46:37 2019
Funções de forma ok!
@author: markinho
"""
import sympy as sp
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d, Axes3D
from mpl_toolkits.mplot3d.art3d import Po... | mit |
mattjj/pyhawkes | experiments/discrete_continuous_comparison.py | 2 | 5308 | import time
import numpy as np
np.random.seed(1111)
np.seterr(over="raise")
import cPickle, os
from hips.plotting.layout import create_figure
import matplotlib.pyplot as plt
import brewer2mpl
colors = brewer2mpl.get_map("Set1", "Qualitative", 9).mpl_colors
# goodcolors = np.array([0,1,2,4,6,7,8])
# colors = np.array(... | mit |
r-mart/scikit-learn | sklearn/metrics/tests/test_classification.py | 83 | 49782 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import la... | bsd-3-clause |
costypetrisor/scikit-learn | examples/text/hashing_vs_dict_vectorizer.py | 284 | 3265 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the e... | bsd-3-clause |
jreback/pandas | pandas/core/indexes/timedeltas.py | 1 | 9039 | """ implement the TimedeltaIndex """
from pandas._libs import index as libindex, lib
from pandas._libs.tslibs import Timedelta, to_offset
from pandas._typing import DtypeObj
from pandas.errors import InvalidIndexError
from pandas.core.dtypes.common import TD64NS_DTYPE, is_scalar, is_timedelta64_dtype
from pandas.cor... | bsd-3-clause |
roytu/impede | impede-app/server/py/filter_library.py | 2 | 4231 |
""" Module that contains some example filters """
import numpy as np
import matplotlib.pyplot as plt
from graph import Node, Edge, Graph
from resistor import Resistor
from capacitor import Capacitor
from diode import Diode
from opamp import Opamp
from wire import Wire
from units import Units
from filter import Filte... | mit |
dungvtdev/upsbayescpm | bayespy/inference/vmp/nodes/GaussianProcesses.py | 5 | 25953 | ################################################################################
# Copyright (C) 2011-2012 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
import itertools
import numpy as np
#import scipy as sp
#import s... | mit |
spektom/incubator-airflow | airflow/providers/apache/hive/hooks/hive.py | 4 | 39159 | #
# 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... | apache-2.0 |
bartvm/GroundHog | experiments/nmt/tree.py | 17 | 6574 | #!/usr/bin/env python
import argparse
import cPickle
import traceback
import logging
import time
import copy
import networkx as nx
import numpy
import experiments.nmt
from experiments.nmt import RNNEncoderDecoder, parse_input
import theano
import theano.tensor as TT
import matplotlib.pyplot as plt
import matplotl... | bsd-3-clause |
googleinterns/amt-xpub | buffling/signal_to_noise_ratio.py | 1 | 4384 | # Copyright 2020 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
thientu/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
eg-zhang/scikit-learn | benchmarks/bench_glm.py | 297 | 1493 | """
A comparison of different methods in GLM
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from sklearn import linear_model
from sklearn.utils.bench import total_seconds
if __name__ == '__main__':
import pylab as pl
n_iter = 40
time_ridge = np.empty(n_it... | bsd-3-clause |
ulisespereira/PereiraBrunel2016 | S2/fig5.py | 1 | 10899 | import numpy as np
import matplotlib.gridspec as gridspec
from scipy import sparse
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import math as mt
from stimulus import *
from myintegrator import *
import cProfile
import json
from matplotlib.colors import LogNorm
from matplotlib.ticker import Multip... | gpl-2.0 |
toobaz/pandas | pandas/tests/util/test_assert_numpy_array_equal.py | 2 | 5404 | import numpy as np
import pytest
from pandas import Timestamp
from pandas.util.testing import assert_numpy_array_equal
def test_assert_numpy_array_equal_shape_mismatch():
msg = """numpy array are different
numpy array shapes are different
\\[left\\]: \\(2L*,\\)
\\[right\\]: \\(3L*,\\)"""
with pytest.raise... | bsd-3-clause |
colinbrislawn/scikit-bio | skbio/draw/tests/test_distributions.py | 7 | 27970 | # ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | bsd-3-clause |
karvenka/sp17-i524 | project/S17-IO-3012/code/bin/benchmark_version_mapreduce.py | 19 | 4617 | import matplotlib.pyplot as plt
import sys
import pandas as pd
def get_parm():
"""retrieves mandatory parameter to program
@param: none
@type: n/a
"""
try:
return sys.argv[1]
except:
print ('Must enter file name as parameter')
exit()
def read_file(filename):
"""... | apache-2.0 |
JackKelly/neuralnilm_prototype | neuralnilm/net.py | 2 | 25235 | from __future__ import division, print_function
from functools import partial
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import csv
import h5py
from datetime import datetime, timedelta
import logging
from numpy.random import rand
from time import time
from os.path import exists, join
import... | mit |
thesuperzapper/tensorflow | tensorflow/examples/learn/iris_custom_model.py | 50 | 2613 | # 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 |
DonBeo/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
jmschrei/scikit-learn | sklearn/cluster/tests/test_birch.py | 342 | 5603 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.l... | bsd-3-clause |
amolkahat/pandas | pandas/tests/test_base.py | 2 | 46356 | # -*- coding: utf-8 -*-
from __future__ import print_function
import re
import sys
from datetime import datetime, timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.compat as compat
from pandas.core.dtypes.common import (
is_object_dtype, is_datetimetz, is_datetime64_dtype,
needs_i8_... | bsd-3-clause |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/pandas/sparse/tests/test_pivot.py | 7 | 2417 | import numpy as np
import pandas as pd
import pandas.util.testing as tm
class TestPivotTable(tm.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
self.dense = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
'foo', 'bar', 'foo', 'foo'],
... | mit |
shyamalschandra/scikit-learn | sklearn/svm/setup.py | 321 | 3157 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('svm', parent_package, top_path)
config.add_subpackage('tests')
# Section L... | bsd-3-clause |
andaag/scikit-learn | sklearn/metrics/pairwise.py | 104 | 42995 | # -*- coding: utf-8 -*-
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Robert Layton <robertlayton@gmail.com>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Philippe Gervais <philippe.gervais@inria.fr>
# Lars Buitinck ... | bsd-3-clause |
lshain-android-source/external-blktrace | btt/btt_plot.py | 43 | 11282 | #! /usr/bin/env python
#
# btt_plot.py: Generate matplotlib plots for BTT generate data files
#
# (C) Copyright 2009 Hewlett-Packard Development Company, L.P.
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free So... | gpl-2.0 |
Vishruit/DDP_models | code/create_label_tags.py | 1 | 2811 | import random
import numpy as np
import os
import shutil
import time
import sys
np.random.seed(1)
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import scipy.misc
def getJPGFilePaths(directory,excludeFiles):
file_paths = []
file_name = []
file_loc = []
global ext... | gpl-3.0 |
JesseLivezey/pylearn2 | pylearn2/packaged_dependencies/theano_linear/unshared_conv/localdot.py | 39 | 5044 | """
WRITEME
"""
import logging
from ..linear import LinearTransform
from .unshared_conv import FilterActs, ImgActs
from theano.compat.six.moves import xrange
from theano.sandbox import cuda
if cuda.cuda_available:
import gpu_unshared_conv # register optimizations
import numpy as np
import warnings
try:
impor... | bsd-3-clause |
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