text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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from glob import glob
import os
import numpy as np
import pandas as pd
import torch
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader
class AudioDataset(torch.utils.data.Dataset):
def __init__(self, data_root, types, phase):
super(AudioDataset, self).__init__()
se... | {"hexsha": "437201beb0f7580fdfe9941d94a83be11e5899db", "size": 5226, "ext": "py", "lang": "Python", "max_stars_repo_path": "filling_level/vggish/dataset.py", "max_stars_repo_name": "v-iashin/CORSMAL", "max_stars_repo_head_hexsha": "085f4c39d241bd71dcaa1ad9fef4f8728e447b98", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import time
import cmws
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import textwrap
import torch
import numpy as np
from cmws import util, losses
from cmws.examples.timeseries_real import data, run
from cmws.examples.timeseries_real import util as timeseries_util
from cmws.example... | {"hexsha": "42c03f52b1f8508645e708654db09c2969b913f2", "size": 3274, "ext": "py", "lang": "Python", "max_stars_repo_path": "cmws/examples/timeseries_real/calc_logp.py", "max_stars_repo_name": "tuananhle7/hmws", "max_stars_repo_head_hexsha": "175f77a2b386ce5a9598b61c982e053e7ecff8a2", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Simple linear damping
@author: Simon H. Thomas, COER laboratory, Maynooth University
"""
import numpy as np;
from scipy.interpolate import interp1d;
import COERbuoy.connection as connection;
conn_model=connection.connection();#Initialize connection
conn_model.openC();... | {"hexsha": "76c76b866ddadfac2452f04eb0249b676f5aa98e", "size": 1979, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/use_python/controller.py", "max_stars_repo_name": "SiHeTh/COERbuoy", "max_stars_repo_head_hexsha": "996af84e7e8605d585070517cce862f7ee32bebb", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
function s = convert(tree,uid,varargin)
% XMLTREE/CONVERT Convert an XML tree into a structure
%
% tree - XMLTree object
% uid - uid of the root of the subtree, if provided.
% Default is root
% s - converted structure
%_________________________________________________________________________... | {"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/@xmltree/convert.m"} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Autopep8: https://pypi.org/project/autopep8/
# Check with http://pep8online.com/
# Make regrid with xESMF
import numpy as np
import xesmf as xe
import scipy
def regrid(
ds_in,
ds_out,
method='bilinear',
globe=True,
periodic=Tr... | {"hexsha": "27db31cd547e9a6f6f5662cd050973ab77203933", "size": 4105, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/regrid.py", "max_stars_repo_name": "mickaellalande/CMIP6_HMA_paper", "max_stars_repo_head_hexsha": "ddd7331626f22c51979665654c157f059481a418", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
using Dice
using Dice: num_flips, num_nodes, to_dice_ir
code = @dice begin
x = flip(0.5)
y = if x x else false
if y y else false
end
# BDD analysis
bdd = compile(code)
num_flips(bdd), num_nodes(bdd)
infer(code, :bdd)
# IR analysis
println(to_dice_ir(code))
has_dice_binary() && rundice(code)
has_dice_bina... | {"hexsha": "2183b4ff03fe48b1ce97a773d81b0d290402ed16", "size": 348, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/bug.jl", "max_stars_repo_name": "rtjoa/Dice.jl", "max_stars_repo_head_hexsha": "839b906edbe6a1b51c723211533b3145700406b6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "... |
import numpy as np
from collections import deque
from functools import lru_cache
class Label:
"""A label describes a path from the depot to a customer and the resources
used in this path.
Labels are associated with a customer and are used to identify each
feasible state in which that c... | {"hexsha": "a6899e50b3a8a6df8e75af36beb7a8ce62b62160", "size": 7892, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ESPPRC.py", "max_stars_repo_name": "onboarding92/Vrptw", "max_stars_repo_head_hexsha": "a3000ce7ad083a978dee249e21dac4809df38f07", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Filename : softmax_regressor
# @Date : 2017-05-20-08-02
# @Poject: softmax
# @AUTHOR : Jayamal M.D.
'''NOTE THAT THIS MULTI-CLASS CLASSIFICATION ALGORITHM ASSUMES THAT THE DATASET [X]
CAN BE EXPRESSED AS AN EXPONENTIAL FAMILY DISTRIBUTION
AS WELL AS THE THE DATASET[X] ... | {"hexsha": "08e9336ea63b02b2d179211f5b623a31b33cdc5e", "size": 3713, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/softmax_regressor.py", "max_stars_repo_name": "mjdileep/mjLearn", "max_stars_repo_head_hexsha": "098a44e950d4f1fd95c622aa1fc93d8239da6fac", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""Collect data for comparison of MHE and EKF with different number of anchors
This script simulates the performance of MHE and EKF on the trajectories
in the data/publication_run folder. For each file, the number of anchors
is varied between 1-8 for TWR and 2-8 for TDOA. Every number of anchor
is tested in 10 runs wi... | {"hexsha": "0a5d8d8c281680490ffc473ea273b44fa8063208", "size": 6599, "ext": "py", "lang": "Python", "max_stars_repo_path": "publication/Calibrater.py", "max_stars_repo_name": "kianheus/uwb-simulator", "max_stars_repo_head_hexsha": "888cdcae0d4ca101970971afbdf0113ba3bb1480", "max_stars_repo_licenses": ["MIT"], "max_star... |
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from PIL import Image
from libs.SequentialModel import SequentialMnist
ckpt_path = './checkpoint/mnist_aug.ckpt'
aug_path = './checkpoint/mnist_chpt.ckpt'
ori_path = './checkpoint/mnist_ori.ckpt'
model1 = SequentialMnist()
model2 = Sequential... | {"hexsha": "26633d4650c1e460e9b3c5e8d1d6687d7a6e9238", "size": 1478, "ext": "py", "lang": "Python", "max_stars_repo_path": "class4/mnist_app.py", "max_stars_repo_name": "dapianzi/tf_start", "max_stars_repo_head_hexsha": "b6dc85c4c06c65ff892f6eb19aceb09fffc676a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# mnist_utils
#
# A set of utility functions for grabbing the MNIST dataset.
using MNIST
# This function does two things:
# 1) Filters the X and y data to only be those in labels
# 2) Does PCA to reduce the dimensionality of X to numdim.
#
# @returns - (X_train_reduced, y_train, X_test_reduced, y_test)
function mnist... | {"hexsha": "7f66ae87cbe5fdcd848647d96d9e5940eff872a4", "size": 1448, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/experiments/mnist/mnist_utils.jl", "max_stars_repo_name": "jgorham/stein_discrepancy", "max_stars_repo_head_hexsha": "addfe17ce04e6fec4be0c441c996e732b1f7abb0", "max_stars_repo_licenses": ["MIT... |
import numpy as np
from Input_data import *
from matplotlib import pyplot as plt
def euclidianDistance(x1, x2):
"""define the euclidian metric
Args:
x1, x2: (array) the position of two point
Returns:
distance: distance of two point
"""
return np.sqrt(np.sum(np.power(x1-x2, 2), ax... | {"hexsha": "542cc7c5fd8afcecb01357451c63732fd2de4606", "size": 5723, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tensorflow/kmeans.py", "max_stars_repo_name": "Jiachengciel/The-conversion-of-radiotherapy-image-by-machine-learning", "max_stars_repo_head_hexsha": "18570209f8ba02d28cb38b114ad49cad715f9dad", "ma... |
from typing import Iterator, Tuple
import numpy as np
class SinusoidRegression:
def __init__(self, meta_batch_size: int, num_shots: int, seed: int = 666):
self.meta_batch_size = meta_batch_size
self.num_shots = num_shots
self.rs = np.random.RandomState(seed)
@property
def train_set(
self
... | {"hexsha": "ac9d0b418a175418923b8b02bab390dce39eb17a", "size": 1697, "ext": "py", "lang": "Python", "max_stars_repo_path": "sinusoid_regression_dataset.py", "max_stars_repo_name": "yardenas/meta-learning-tutorial", "max_stars_repo_head_hexsha": "c5154eae85f6255f58fe6028ab630e3499238b3a", "max_stars_repo_licenses": ["MI... |
// (C) Copyright Edward Diener 2011-2015
// Use, modification and distribution are subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt).
#if !defined(BOOST_VMD_DETAIL_NOT_EMPTY_HPP)
#define BOOST_VMD_DETAIL_NOT_EMPTY_HPP
... | {"hexsha": "f6dfb66d75037bf2e053e871b4025b7122d4d5ca", "size": 575, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactNativeFrontend/ios/Pods/boost/boost/vmd/detail/not_empty.hpp", "max_stars_repo_name": "Harshitha91/Tmdb-react-native-node", "max_stars_repo_head_hexsha": "e06e3f25a7ee6946ef07a1f524fdf62e4842429... |
! path: $Source$
! author: $Author$
! revision: $Revision$
! created: $Date$
!
program rrtmg_sw
!----------------------------------------------------------------------------
! Copyright (c) 2002-2020, Atmospheric & Environmental Research, Inc. (AER)
! All rights reserved.
!
! Redistri... | {"hexsha": "abf09d9fa0bcc3fe00aab702b511b6c78adf841e", "size": 105145, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/rrtmg_sw.1col.f90", "max_stars_repo_name": "thabbott/RRTMG_SW", "max_stars_repo_head_hexsha": "1a3fb6a9aac3d602280e6979a1791a7e54cf3916", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
# -*- coding: utf-8 -*-
from numpy import pi, arcsin, sin
def comp_surface_wind(self):
"""Compute the Slot inner surface for winding (by analytical computation)
Parameters
----------
self : SlotW25
A SlotW25 object
Returns
-------
Swind: float
Slot inner surface for wind... | {"hexsha": "7ca5381faaded62ff5aa6513cf944425e74f1cbb", "size": 968, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyleecan/Methods/Slot/SlotW25/comp_surface_wind.py", "max_stars_repo_name": "harshasunder-1/pyleecan", "max_stars_repo_head_hexsha": "32ae60f98b314848eb9b385e3652d7fc50a77420", "max_stars_repo_lice... |
# From Julia 1.0's online docs. File countheads.jl available to all machines:
function count_heads(n)
c::Int = 0
for i = 1:n
c += rand(Bool)
end
c
end
| {"hexsha": "3fc7e5ad93834a41769c6cde3e1ddb3f6f538340", "size": 176, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/distributed-programming-1.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": [... |
import sys, os, argparse
sys.path.insert(0, os.path.abspath('..'))
import warnings
warnings.filterwarnings("ignore")
import foolbox
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import json
import matplotlib.pyplot as plt
import umap
import seaborn as ... | {"hexsha": "9c5a1d79516cc964a51ce642015f581861370ef6", "size": 4143, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotting/mnist_plot.py", "max_stars_repo_name": "Derek-Wds/MAD-VAE", "max_stars_repo_head_hexsha": "267ce6ca98f1b1ecc8ebec22ddeca32e2c502d5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/**
* OpenAPI Petstore
* This is a sample server Petstore server. For this sample, you can use the api key `special-key` to test the authorization filters.
*
* OpenAPI spec version: 1.0.0
*
* NOTE: This class is auto generated by OpenAPI-Generator 3.3.1-SNAPSHOT.
* https://openapi-generator.tech
* Do not edit t... | {"hexsha": "4ff69df7857b75eab53e74ce1c3f3fb7cbf8568c", "size": 3015, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "samples/client/petstore/cpp-restsdk/MultipartFormData.cpp", "max_stars_repo_name": "netfarma/openapi-generator", "max_stars_repo_head_hexsha": "8ac80203ec557a7198e48adc66e9c1961c4cd6ce", "max_stars_... |
"""
Pretrain a network on regular classification.
Author: Mengye Ren (mren@cs.toronto.edu)
Usage:
pretrain.py --config [CONFIG] --tag [TAG} --dataset [DATASET] \
--data_folder [DATA FOLDER] --results [SAVE FOLDER]
"""
from __future__ import (absolute_import, division, print_function,
... | {"hexsha": "bcb021e1f986be96ff1e81007c97adb6f05bd7c0", "size": 12625, "ext": "py", "lang": "Python", "max_stars_repo_path": "fewshot/experiments/pretrain.py", "max_stars_repo_name": "sebamenabar/oc-fewshot-public", "max_stars_repo_head_hexsha": "2dad8c9f24cb1bfe72d8b13b33d28f6788d86ca8", "max_stars_repo_licenses": ["MI... |
import os
import numpy as np
import tables as tb
from nose.tools import assert_equal, assert_not_equal, assert_almost_equal, \
assert_true, assert_false
from numpy.testing import assert_array_equal, assert_array_almost_equal
from pyne.xs import data_source
from pyne.xs.cache import xs_cache
fr... | {"hexsha": "91640fb703dbc43fd4b1aeaf4fb6d11e8819f63c", "size": 2537, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/xs/test_cache.py", "max_stars_repo_name": "ypark234/pyne", "max_stars_repo_head_hexsha": "b7c4932c0399e6a0881aea943b392fb97cd0b6bd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18... |
!
! CalculiX - A 3-dimensional finite element program
! Copyright (C) 1998-2015 Guido Dhondt
!
! This program is free software; you can redistribute it and/or
! modify it under the terms of the GNU General Public License as
! published by the Free Software Foundation(version 2);
!
! ... | {"hexsha": "a2c2ab6aeb6e5f68e0091a7341de0b8db67e2a6a", "size": 22400, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ccx_prool/CalculiX/ccx_2.10/src/mafillnet.f", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars_repo_lice... |
import numpy as np
import random
class Agent(object):
def __init__(self, state_size, action_size, prod=False):
self.state_size = state_size
self.action_size = action_size
self.prod = prod
self.alpha = .5
self.alpha_min = 0.001
# self.alpha_decay = 0.999999
... | {"hexsha": "c3c980d6dea8ddcfba1ac650750b599b6cfe815e", "size": 1032, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/agent.py", "max_stars_repo_name": "Neoares/atari-gamer", "max_stars_repo_head_hexsha": "322b539581f19956e418393cde3549c08c737b5c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# Portfolio Selection, part 1
After the discussion about the agents' preferences, we now turn to work on their consumption/investment portfolio. Let the security market has a **payoff matrix** $X\DeclareMathOperator*{\argmin}{argmin}
\DeclareMathOperator*{\argmax}{argmax}
\DeclareMathOperator*{\plim}{plim}
\newcommand{... | {"hexsha": "71c600ae996c021e97e7f195f05d78e8c6fdbcdd", "size": 18449, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "FinMath/Financial Economics/Chap_08.ipynb", "max_stars_repo_name": "XavierOwen/Notes", "max_stars_repo_head_hexsha": "d262a9103b29ee043aa198b475654aabd7a2818d", "max_stars_repo_licen... |
# Third-party libraries
import numpy as np
import random
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
def train(train, test, seed=42, feature_select=True):
... | {"hexsha": "62607a65bbf1fefe565d5cf20712b7508544bead", "size": 2025, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/rf.py", "max_stars_repo_name": "YDaiLab/MiMeNet", "max_stars_repo_head_hexsha": "f9064b54d61c40d12207db896bc137341969aef4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "ma... |
import unittest
import pandas as pd
import numpy as np
from neatdata.neatdata import *
class TestYBalancer(unittest.TestCase):
def testYBalancer(self):
# Assemble
now = pd.datetime.now()
trainX_8rows = pd.DataFrame({'col1': [1,1,1,1,1,1,1,1],
'col2': ['a','a'... | {"hexsha": "40fe92aa46244c6d515fc5e469e33dd8c2dc8fe5", "size": 1605, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_YBalancer.py", "max_stars_repo_name": "Peter-32/neatDS", "max_stars_repo_head_hexsha": "8796ca9f027ad727440b2f11479ad5ab22aa8e09", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from vispy import app, scene, visuals
from vispy.util import keys
# from vispy.color import Color
# from collections import OrderedDict
from sklearn.neighbors import KDTree
from itertools import combinations as comb
from ..utils import key_buffer
from ..view import Picker
# from matplotlib import pat... | {"hexsha": "7a21e5e2d1febcffe9cc758ac74c89a2bf513af1", "size": 9953, "ext": "py", "lang": "Python", "max_stars_repo_path": "spiketag/view/probe_view.py", "max_stars_repo_name": "aliddell/spiketag", "max_stars_repo_head_hexsha": "f5600126c2c6c9be319e8b808d51ea33be843909", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
from __future__ import absolute_import
from __future__ import division
import os.path
import numpy as np
from nipype.interfaces.base import (
TraitedSpec, BaseInterface, File, traits, isdefined)
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
import nibabel as nib
from nianalysis.excepti... | {"hexsha": "a05a9ac0b5d9fc226a1384d18237cc52f9625b09", "size": 8178, "ext": "py", "lang": "Python", "max_stars_repo_path": "xnat_nif_qc_analysis/interface/qc.py", "max_stars_repo_name": "mbi-image/xnat-qa-analysis-pipeline", "max_stars_repo_head_hexsha": "6a24f08dfd05c1f08fa55f34372d32018818645b", "max_stars_repo_licen... |
function get_name(obj::TDBScale)
return jcall(obj, "getName", JString, ())
end
function offset_from_tai(obj::TDBScale, arg0::AbsoluteDate)
return jcall(obj, "offsetFromTAI", jdouble, (AbsoluteDate,), arg0)
end
function offset_from_tai(obj::TDBScale, arg0::FieldAbsoluteDate)
return jcall(obj, "offsetFromTA... | {"hexsha": "fadd1c18217a1344d49ee89ecf33bb1f65586e69", "size": 461, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "gen/OrekitWrapper/TimeWrapper/tdb_scale.jl", "max_stars_repo_name": "JuliaAstrodynamics/Orekit.jl", "max_stars_repo_head_hexsha": "e2dd3d8b2085dcbb1d2c75471dab42d6ddf52c99", "max_stars_repo_licenses... |
from __future__ import print_function
import torch
from model import roundNet
from utils import roundDataset, maskedNLL,maskedMSE, anchor_inverse
from torch.utils.data import DataLoader
import time
import math
import numpy as np
import scipy.io as scp
# Import the model argumnets
from model_args import args
# Anchors ... | {"hexsha": "745ea882dde50faf904388c652772cdf054e8f54", "size": 9573, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "m-hasan-n/roundabout", "max_stars_repo_head_hexsha": "0275f6b6bfd0fde67f0074ab038f141aa6e7d22f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_s... |
from collections import namedtuple
from tempfile import NamedTemporaryFile
import numpy as np
import pytest
import pandas as pd
from ananse.network import Network
from ananse.commands import network
@pytest.fixture
def binding_fname():
return "tests/example_data/binding2.tsv"
@pytest.fixture
def network_obj()... | {"hexsha": "2b2719343bec00f2b1773d67344179d69415434a", "size": 2407, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/continuous_integration/test_05_network.py", "max_stars_repo_name": "Maarten-vd-Sande/ANANSE", "max_stars_repo_head_hexsha": "18995f01657db5e92d4558eff4c1e81d30ff088e", "max_stars_repo_licens... |
/*
* Copyright Andrey Semashev 2007 - 2015.
* Distributed under the Boost Software License, Version 1.0.
* (See accompanying file LICENSE_1_0.txt or copy at
* http://www.boost.org/LICENSE_1_0.txt)
*/
/*!
* \file attr_attribute_set_ticket11106.cpp
* \author Andrey Semashev
* \date 15.03.... | {"hexsha": "142307d9036ce807dd184cb4cfd51e57cd02483a", "size": 1697, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/log/test/run/attr_attribute_set_ticket11106.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2... |
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 4.*np.pi, 33)
y = np.sin(x)
plt.plot(x, y)
plt.show()
plt.savefig("sinePlot.eps") | {"hexsha": "cf374079c2db5e64d1bf1a70ca1e1caa04de3c62", "size": 155, "ext": "py", "lang": "Python", "max_stars_repo_path": "Book/chap5/Supporting Materials/sinePlot.py", "max_stars_repo_name": "lorenghoh/pyman", "max_stars_repo_head_hexsha": "9b4ddd52c5577fc85e2601ae3128f398f0eb673c", "max_stars_repo_licenses": ["CC0-1.... |
print '***** Guided Proofreading *****'
from theano.sandbox.cuda import dnn
print 'CuDNN support:', dnn.dnn_available()
| {"hexsha": "806128d4385a8a8b5545d6177c5bda0da3530898", "size": 122, "ext": "py", "lang": "Python", "max_stars_repo_path": "gp/test.py", "max_stars_repo_name": "VCG/gp", "max_stars_repo_head_hexsha": "cd106b604f8670a70add469d41180e34df3b1068", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_repo_sta... |
#define BOOST_TEST_MODULE Delegate Libraries Unit Test
#include <boost/test/unit_test.hpp>
| {"hexsha": "05dfc4e606e70e858679b44b4a6a1c26fb793b36", "size": 91, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/main.cpp", "max_stars_repo_name": "yxbh/yxbh", "max_stars_repo_head_hexsha": "f2952625b296fbfa1fbc0cdedc949ae2229b627a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max... |
import numpy as np
from .coordinate_handler import \
CoordinateHandler, \
_get_coord_fields, \
_get_vert_fields, \
cartesian_to_cylindrical, \
cylindrical_to_cartesian
from yt.funcs import mylog
from yt.units.yt_array import uvstack, YTArray
from yt.utilities.lib.pixelization_routines import \
p... | {"hexsha": "40e3364e780ed39f24a37244a2eab64d3db6c717", "size": 23601, "ext": "py", "lang": "Python", "max_stars_repo_path": "yt/geometry/coordinates/cartesian_coordinates.py", "max_stars_repo_name": "aemerick/yt", "max_stars_repo_head_hexsha": "984484616d75c6d7603e71b9d45c5d617705a0e5", "max_stars_repo_licenses": ["BSD... |
#Jul, 2020: Expanded xGrid for positive selection
#Feb 20, 2020: Fixed the bug in B2maf.
#Jul 22, 2019: Modified the published BallerMix script for beta-binomial distribution, updates include:
##- replace optparse with argparse
##- use more numpy/scipy
import sys,argparse
from math import log,exp,floor # natura... | {"hexsha": "f25df26b49593e81487a817ac5303468fce2c8b4", "size": 31679, "ext": "py", "lang": "Python", "max_stars_repo_path": "archive/BalLeRMix+_beta.py", "max_stars_repo_name": "bioXiaoheng/BallerMixPlus", "max_stars_repo_head_hexsha": "9d927ff1198087e89ecd5f68c4237a0e8abe36c4", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
#
# Copyright 2019 DFKI GmbH.
#
# 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
# without limitation the rights to use, copy, modify, merg... | {"hexsha": "54cd08d3f8718be42349564ae97051a4ffc92d98", "size": 2923, "ext": "py", "lang": "Python", "max_stars_repo_path": "morphablegraphs/motion_model/static_motion_primitive.py", "max_stars_repo_name": "dfki-asr/morphablegraphs", "max_stars_repo_head_hexsha": "02c77aab72aa4b58f4067c720f5d124f0be3ea80", "max_stars_re... |
"""
pca_limitations
~~~~~~~~~~~~~~~
Plot graphs to illustrate the limitations of PCA.
"""
# Third-party libraries
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
# Plot just the data
fig = plt.figure()
ax = fig.gca(projection='3d')
z = np.linspace(-2, 2, 20)
... | {"hexsha": "4ba9a7cf6b123a8258dae97a1d11eb9c208e43e7", "size": 814, "ext": "py", "lang": "Python", "max_stars_repo_path": "fig/pca_limitations.py", "max_stars_repo_name": "Zander-Davidson/Neural-Network-Exercise", "max_stars_repo_head_hexsha": "d15df08a69ed33ae16a2fff874f83b57a956172c", "max_stars_repo_licenses": ["Unl... |
import gzip
import cPickle as pickle
from global_constants import *
import numpy as np
import logging
def unpickle_object_from_gz_file(filename):
with gzip.open(filename,"r") as fl:
logging.info("Started unpickling from %s"%(filename))
obj =pickle.load(fl)
logging.info("Unpickled from %s"%(... | {"hexsha": "2f89dcb290abcffb7833b98ff85c79518ed0fd93", "size": 1213, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/emnlp2018/io_utils.py", "max_stars_repo_name": "iKernels/RelTextRank", "max_stars_repo_head_hexsha": "0f773fca83bb23f9a159c2da1635597a089ca7e0", "max_stars_repo_licenses": ["Apache-2.0"], ... |
from MetReg.base.base_model import BaseModel
from sklearn import gaussian_process
import numpy as np
np.random.seed(1)
class GaussianProcessRegressor(BaseModel):
"""[summary]"""
def __init__(self,
alpha=1e-10,
thetaL=1e-5,
thetaU=1e5,
random_... | {"hexsha": "2e2cf0273e3c5312ab88a00b9d697a9e543db57a", "size": 1382, "ext": "py", "lang": "Python", "max_stars_repo_path": "MetReg/models/ml/gp.py", "max_stars_repo_name": "leelew/MetReg", "max_stars_repo_head_hexsha": "b38e28326374e30521eab70625b9c22105688d8d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3,... |
import argparse
import pandas as pd
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.dates as mdates
import seaborn as sns
def plot_map(dataset, title, co... | {"hexsha": "a79a6b8626f0f75cd339a8ddbf8ada57ba5bca8c", "size": 12128, "ext": "py", "lang": "Python", "max_stars_repo_path": "langmuir_plotter.py", "max_stars_repo_name": "spel-uchile/langmuir_parser", "max_stars_repo_head_hexsha": "d356e749ac09ee9af1519683f9b4a7bf68aa18eb", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 29 15:29:14 2018
@author: Pooja
"""
#testing model of doubles on singles and vice versa
#import tensorflow as tf
#import keras
import os
import numpy as np
import cv2
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
fro... | {"hexsha": "349aa4eaf54b88cb6e026ee94d88a23984dc4bc4", "size": 2325, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing/cross_Testing.py", "max_stars_repo_name": "poojacos/NAC-LSTM", "max_stars_repo_head_hexsha": "990ebca4ae34f19f75f48c12519de27488bc57b7", "max_stars_repo_licenses": ["MIT"], "max_star... |
! Compile and run: make run_gauss_jordan_test
program gauss_jordan_test
use gauss_jordan
implicit none
real, dimension(3, 3) :: A
real, dimension(3) :: y, x, x_exp
real, dimension(size(A, 1) + 1, size(A, 2)) :: Ay
A(1, :) = (/ 0, 2, 1 /)
A(2, :) = (/ 1, -2, -3 /)
A(3, :) = (/ -1, 1, 2 /)
y ... | {"hexsha": "6b1c0a14216abc0505cc829d3c549fd95300cd80", "size": 1074, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "fortran/phys395_hw1/test_gauss_jordan.f90", "max_stars_repo_name": "YodaEmbedding/experiments", "max_stars_repo_head_hexsha": "567c6a1c18fac2d951fe2af54aaa4917b7d529d2", "max_stars_repo_licenses... |
"""
Convenience script to make a video out of initial environment
configurations. This can be a useful debugging tool to understand
what different sampled environment configurations look like.
"""
import argparse
import imageio
import numpy as np
from robosuite.controllers import load_controller_config
from robosuite... | {"hexsha": "a16aa89fa63fd44532f7393b0b5e69ec79d7a1ac", "size": 2915, "ext": "py", "lang": "Python", "max_stars_repo_path": "robosuite/scripts/make_reset_video.py", "max_stars_repo_name": "spatric5/robosuite", "max_stars_repo_head_hexsha": "9e6b9691eb949fbf33a23fbe8a8c6faea61c50b6", "max_stars_repo_licenses": ["MIT"], "... |
//
// GraphTools library
// Copyright 2017-2019 Illumina, Inc.
// All rights reserved.
//
// Author: Felix Schlesinger <fschlesinger@illumina.com>
//
// 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 Lic... | {"hexsha": "e457f3243dbf6e91ac306335d2d4d7b13062ba7d", "size": 1150, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ehunter/thirdparty/graph-tools-master-f421f4c/src/graphio/AlignmentWriter.cpp", "max_stars_repo_name": "bw2/ExpansionHunter", "max_stars_repo_head_hexsha": "6a6005a4bae2c49f56ec8997a301b70a75b042b6"... |
/*******************************************************************\
Module:
Author: Daniel Kroening, kroening@kroening.com
\*******************************************************************/
#include <util/arith_tools.h>
#include <util/fixedbv.h>
#include <util/std_types.h>
fixedbv_spect::fixedbv_spect(const f... | {"hexsha": "61a3d3ef54d47443727d898b7f5aa4003d75496a", "size": 5955, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/util/fixedbv.cpp", "max_stars_repo_name": "pablodiego/esbmc", "max_stars_repo_head_hexsha": "c981c3a7e9fc25d32f39f73bc015ace4fcee2ee7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
import open3d as o3d
import numpy as np
import torch
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import h5py
import transforms3d.euler as t3d
def visualize_result(template, source):
template_ = o3d.geometry.PointCloud()
source_ = o3d.geometry.PointCloud()
template_.points = o3d.utilit... | {"hexsha": "1de308709c67cf25ac399fd36d22b2227cef7f6d", "size": 2063, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/partial_data.py", "max_stars_repo_name": "vinits5/PointNetLK", "max_stars_repo_head_hexsha": "ccae51a8462b909c6a4a9f157bdf940e5c73dd35", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
using Test
using DistributedFactorGraphs
using DistributedFactorGraphs.LightDFGs.FactorGraphs
@testset "LightDFGs.FactorGraphs BiMaps" begin
@test isa(FactorGraphs.BiDictMap(), FactorGraphs.BiDictMap{Int64})
bi = FactorGraphs.BiDictMap{Int}()
@test (bi[1] = :x1) == :x1
@test bi[:x1] == 1
@test ... | {"hexsha": "42f6b35fc32e90c376167409f6f2fb32eeb6ba68", "size": 2317, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/LightFactorGraphsTests.jl", "max_stars_repo_name": "Abhisheknishant/DistributedFactorGraphs.jl", "max_stars_repo_head_hexsha": "ac345dfc6fc5489201ada138fa4396880cce3f24", "max_stars_repo_licen... |
"""
Copyright (c) 2018, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
Compute Evaluation Metrics.
Code adapted from https://github.com/TimDettmers/ConvE/blob/master/eval... | {"hexsha": "4c5b4587997faf2b7c9845d575e6738ca19c9871", "size": 8785, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/eval.py", "max_stars_repo_name": "kingsaint/ExplainableEntityLinking", "max_stars_repo_head_hexsha": "2f26602a0187d8785214e639ccb8dc87f4ca2302", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import os
import matplotlib
import numpy as np
from core.argo.core.hooks.EveryNEpochsTFModelHook import EveryNEpochsTFModelHook
matplotlib.use('Agg')
from datasets.Dataset import TRAIN, VALIDATION
from core.argo.core.argoLogging import get_logger
tf_logging = get_logger()
SUMMARIES_KEY = "3by3"
class MutualInf... | {"hexsha": "483a8819e9f8dc1317b6911602ba1e5b687df1ab", "size": 2729, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/hooks/MutualInformationHook.py", "max_stars_repo_name": "szokejokepu/natural-rws", "max_stars_repo_head_hexsha": "bb1ad4ca3ec714e6bf071d2136593dc853492b68", "max_stars_repo_licenses": ["MIT"]... |
# Copyright (C) 2018-2022 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import numpy as np
from openvino.tools.mo.ops.eye import MXEye
from openvino.tools.mo.front.extractor import FrontExtractorOp
from openvino.tools.mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs
class EyeExtractor(FrontExt... | {"hexsha": "79041988748b5e99d242d1b0996a0f5f1221f105", "size": 936, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/mo/openvino/tools/mo/front/mxnet/eye_ext.py", "max_stars_repo_name": "opencv/dldt", "max_stars_repo_head_hexsha": "c0a2c98a457a08e8853abc18f5bd462169d0b354", "max_stars_repo_licenses": ["Apac... |
# -*- coding: utf-8 -*-
# @Author: Hawkin
# @License: Apache Licence
# @File: tensent_captcha_ocr.py
# @Time: 2018/8/5 9:14
import numpy as np
from keras import backend as K
from keras.layers import Input, Dense, RepeatVector, GRU, TimeDistributed, Bidirectional, Dropout
from keras.models import Model
from keras.optim... | {"hexsha": "d8f0a4cc6294457a3900f56ca02f0e48d316258e", "size": 13083, "ext": "py", "lang": "Python", "max_stars_repo_path": "ruban/applications/InkFountain.py", "max_stars_repo_name": "qaz734913414/Ruban", "max_stars_repo_head_hexsha": "9c6c8dd5b4806b104ca650a96affdfbd9d01ba6c", "max_stars_repo_licenses": ["Apache-2.0"... |
[STATEMENT]
lemma (in ab_group_add) uminus_sum_list_map:
"- sum_list (map f xs) = sum_list (map (uminus \<circ> f) xs)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. - sum_list (map f xs) = sum_list (map (uminus \<circ> f) xs)
[PROOF STEP]
by (induct xs) simp_all | {"llama_tokens": 117, "file": null, "length": 1} |
import numpy as np
from numpy import ndarray
from dataclasses import dataclass
from scipy.spatial.transform import Rotation
from config import DEBUG
from cross_matrix import get_cross_matrix
@dataclass
class RotationQuaterion:
"""Class representing a rotation quaternion (norm = 1). Has some useful
methods f... | {"hexsha": "937af3abf0fdf78beb427dae4de3592eb85fa4a3", "size": 4155, "ext": "py", "lang": "Python", "max_stars_repo_path": "Graded/G2/eskf/quaternion.py", "max_stars_repo_name": "chrstrom/TTK4250", "max_stars_repo_head_hexsha": "f453c3a59597d3fe6cff7d35b790689919798b94", "max_stars_repo_licenses": ["Unlicense"], "max_s... |
#ifndef S3_TRANSPORT_UTIL_HPP
#define S3_TRANSPORT_UTIL_HPP
#include "circular_buffer.hpp"
// iRODS includes
#include <rcMisc.h>
#include <transport/transport.hpp>
// misc includes
#include "json.hpp"
#include <libs3.h>
// stdlib and misc includes
#include <string>
#include <thread>
#include <vector>
#include <cstd... | {"hexsha": "0eb9331b3691a6b74aea86667ac43b74cd91bda7", "size": 5321, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "s3/s3_transport/include/s3_transport_util.hpp", "max_stars_repo_name": "alanking/irods_resource_plugin_s3", "max_stars_repo_head_hexsha": "492839f885f432d30fa904ac9d5f89369d248ece", "max_stars_repo_... |
@with_kw struct AbstractKktRhs{T <: AbstractFloat}
dx::Vector{T}
dy::Vector{T}
dz::Vector{T}
ds::Vector{T}
dτ::T
dκ::T
@assert length(ds) == length(dz)
end
function get_aff_dir!(iter::Iterate{T}, resid::Residual{T}, λ::Vector{T}, prob::Problem{T},
kkt::KktCache{T}, sc... | {"hexsha": "b45615f9128840083d394ff4a44e70226d6b19e7", "size": 2669, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/abs_kkt.jl", "max_stars_repo_name": "moehle/SocpSolver.jl", "max_stars_repo_head_hexsha": "4fa7f3def9669a924f7d918f5e5ebd5d6f3c049c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import datetime
import gensim
import luigi
import pandas as pd
import numpy as np
from american_gut_project_pipeline.pipeline.process import Biom
from american_gut_project_pipeline.paths import paths
class SubSentence(luigi.Task):
aws_profile = luigi.Parameter(default='default')
min_value = luigi.IntParamet... | {"hexsha": "145013cee70b04286dab187029efad4943bd5ca0", "size": 5439, "ext": "py", "lang": "Python", "max_stars_repo_path": "american_gut_project_pipeline/pipeline/embedding/w2v.py", "max_stars_repo_name": "mas-dse-ringhilt/DSE-American-Gut-Project", "max_stars_repo_head_hexsha": "dadb3be8d40d6fb325d26920b145c04c837a686... |
r"""
Finite `\ZZ`-modules with with bilinear and quadratic forms.
AUTHORS:
- Simon Brandhorst (2017-09): First created
"""
# ****************************************************************************
# Copyright (C) 2017 Simon Brandhorst <sbrandhorst@web.de>
#
# This program is free software: you can redistr... | {"hexsha": "2a2b77952a648facdea88a7799198520f400dc33", "size": 49447, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/modules/torsion_quadratic_module.py", "max_stars_repo_name": "fchapoton/sage", "max_stars_repo_head_hexsha": "765c5cb3e24dd134708eca97e4c52e0221cd94ba", "max_stars_repo_licenses": ["BSL-... |
"""
bridges(g)
Compute the [bridges](https://en.m.wikipedia.org/wiki/Bridge_(graph_theory))
of a connected graph `g` and return an array containing all bridges, i.e edges
whose deletion increases the number of connected components of the graph.
# Examples
```jldoctest
julia> using LightGraphs
julia> bridges(star_... | {"hexsha": "69d2c2b47f3b2dbd8ebfacf5ea26c51f06e04261", "size": 3410, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/biconnectivity/bridge.jl", "max_stars_repo_name": "blepabyte/LightGraphs.jl", "max_stars_repo_head_hexsha": "1fa2898a92bc551282f619d1818dd1dab4f85358", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 8 11:49:03 2021
I use the example given in
https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/
To do a forecast of the daily-averaged sea level as a function of wind speed and direction, and tidal information.
For... | {"hexsha": "8c41cee269a04bf1f389652d30ab628e0e0c2143", "size": 6906, "ext": "py", "lang": "Python", "max_stars_repo_path": "LSTM_running_daily.py", "max_stars_repo_name": "LOCO-EX/sea_level_ML", "max_stars_repo_head_hexsha": "6d4bc28f2de256c668c465ff612cfa7e9ee0b036", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "82563141cc94663ae7893de00f2da58106e49c69", "size": 4836, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/contrib/learn/python/learn/estimators/rnn_common_test.py", "max_stars_repo_name": "tianyapiaozi/tensorflow", "max_stars_repo_head_hexsha": "fb3ce0467766a8e91f1da0ad7ada7c24fde7a73a", "m... |
import inspect
import numpy as np
import chainer
import chainer.links as L
import chainer.functions as F
from chainer.dataset import convert
from third_party_library import projection_simplex_sort
class LRE(chainer.training.StandardUpdater):
"""
"""
def __init__(self, iterator, optimizer, converter=conv... | {"hexsha": "2f5936e85d14e3ea10ca0a64f5bedba5af6e6ae9", "size": 7719, "ext": "py", "lang": "Python", "max_stars_repo_path": "mnist/updaters.py", "max_stars_repo_name": "pfnet-research/robust_estimation", "max_stars_repo_head_hexsha": "9cb404f5ae80275e927ee5ccec3e3ea6099ff392", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
"""Test for RNN encoder."""
import importlib
import math
import numpy as np
import pytest
import torch
from neural_sp.models.torch_utils import (
np2tensor,
pad_list
)
def make_args(**kwargs):
args = dict(
input_dim=80,
enc_type='blstm',
... | {"hexsha": "fe0136ed0eb1407e6db75acf370dae1f663a4d91", "size": 10683, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/encoders/test_rnn_encoder.py", "max_stars_repo_name": "ishine/neural_sp", "max_stars_repo_head_hexsha": "7995613541d994976b00d80dcc12e2835163acfb", "max_stars_repo_licenses": ["Apache-2.0"],... |
# -*- coding: utf-8 -*-
# """Game of Thrones + Google Sheet
# Automatically generated by Colaboratory.
# Original file is located at
# https://colab.research.google.com/drive/1bsV6QKBzG__usEDGobsujGS3-aqIMy5w
# """
import numpy as np
import pandas as pd
import streamlit as st
from imblearn.over_sampling import S... | {"hexsha": "379e66b2007b8f2cfaf38ccf9a7ca3065a974bf2", "size": 6648, "ext": "py", "lang": "Python", "max_stars_repo_path": "game_of_thrones_ver_3.py", "max_stars_repo_name": "pereira-rafael/got_predict", "max_stars_repo_head_hexsha": "3cae1dbc5aab0e2dd3db7bbf416f7a7b907b0340", "max_stars_repo_licenses": ["MIT"], "max_s... |
#***************************
#...LaGrange 3- and 4-point interpolation
#...arrays A and B are the npt data points, given aa, a value of the
#...A variable, the routine will find the corresponding bb value
#
#...input: aa
#...output: bb
using Random
function AtoB(aa,A,B,npt)
for I in 2:(npt+1)
if A[I-1] >= aa
... | {"hexsha": "b041ccc6541fcf52d3456668e46177b0459db4ab", "size": 3410, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Absorption/TableInterpolation/partition_sums.jl", "max_stars_repo_name": "Datseris/RadiativeTransfer.jl", "max_stars_repo_head_hexsha": "0fdd094f2842d574c09dfeb7cd02e40c25edaeb2", "max_stars_re... |
# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
from __future__ import print_function
from pyiron_contrib.protocol.generic import CompoundVertex
from pyiron_contrib.p... | {"hexsha": "e16ad321c2296d9b01fdc10f937584b80d19543c", "size": 52438, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyiron_contrib/protocol/compound/finite_temperature_string.py", "max_stars_repo_name": "niklassiemer/pyiron_contrib", "max_stars_repo_head_hexsha": "b108fe8b4760ac958792fe2add430244ade2e202", "ma... |
import numpy as np
import random
from collections import namedtuple, deque
from model import QNetwork
import torch
import torch.nn.functional as F
import torch.optim as optim
BUFFER_SIZE = int(1e5) # replay buffer size
BATCH_SIZE = 128 # minibatch size
GAMMA = 0.99 # discount factor
TAU = 1e-3 ... | {"hexsha": "8400d04e65f3dfa7707863e29d4afc0039d3b930", "size": 12655, "ext": "py", "lang": "Python", "max_stars_repo_path": "Prioritized Experience Replay ddqn/PER_ddqn_agent.py", "max_stars_repo_name": "quboanthony/deep-reinforcement-learning", "max_stars_repo_head_hexsha": "20573d3dd06c2b352f11ab1d891b18fbe82437a1", ... |
##
# \file plottran.py
# \brief plot transfer function.
#
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
from matplotlib.ticker import FuncFormatter
#redshift = 0.0165
#ccf = np.loadtxt("../data/1044_vr_lags.txt", skiprows=1)
#print(ccf)
#ccf[:, ... | {"hexsha": "efc9d87fde2469b766dc3b4f2faa7644e49ce9c5", "size": 1619, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/plottran.py", "max_stars_repo_name": "yzxamos/BRAINS", "max_stars_repo_head_hexsha": "b81cec02a1902df1e544542a970b66d9916a7496", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, ... |
"""
Functions for utilization.
# Requirements
tensorflow==2.0.0a0
tensorlayer==2.0.1
"""
import operator
import os
import random
import copy
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
import tensorlayer as tl
from tensorlayer.layers import Dense, Input
from tensorlayer.models im... | {"hexsha": "2953138a505a7f22141ca640203e28045070b3a9", "size": 14321, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlzoo/common/policy_networks.py", "max_stars_repo_name": "Tokarev-TT-33/RLzoo", "max_stars_repo_head_hexsha": "e91ba1543e9f972bc0c3bd71221de803b1b1f735", "max_stars_repo_licenses": ["Apache-2.0"]... |
from pyrfuniverse.envs import Ur5DrawerEnv
import numpy as np
if __name__ == '__main__':
env = Ur5DrawerEnv(
max_steps=50,
reward_type='sparse'
)
while True:
env.reset()
for i in range(10):
env._step()
for i in range(10):
env.step(np.array... | {"hexsha": "83cabfeb908d1dab3d94916febb34b3b6a279b56", "size": 499, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_ur5_drawer_env.py", "max_stars_repo_name": "happyCoderJDFJJ/pyrfuniverse", "max_stars_repo_head_hexsha": "8ddb6e0d8f113015ba820a327388a528a8b215c7", "max_stars_repo_licenses": ["Apache-2... |
\chapter{Primitive syntax}
After the {\cf import} form within a {\cf library} form or a top-level
program, the forms
that constitute the body of the library or the top-level program
depend on the libraries that are
imported. In particular, imported syntactic keywords determine
the available syntactic abstractions and... | {"hexsha": "b6f41854380ec4580f0918bb43a6a62e40218e3e", "size": 7135, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "r6rs/syntax.tex", "max_stars_repo_name": "schemedoc/rnrs-metadata", "max_stars_repo_head_hexsha": "2f998d354177dc41a8d3147fd15c056a14ffabda", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
include("./LatticeQCD.jl")
using .LatticeQCD
using Random
using Dates
using JLD
function test()
Random.seed!(111)
A = rand(ComplexF64,4,4)*4
A = A'*A
n = 4
ϕ = ComplexF64[1,2,3,4]
ϕr = LatticeQCD.calc_exactvalue(n,A,ϕ)
println(ϕ'*ϕr)
ϕr2 = LatticeQCD.calc_Anϕ(n,A,ϕ)
println(ϕ'*ϕr... | {"hexsha": "327c24885c13b8083fc68d201a8423cf7d5b927d", "size": 367, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/debug.jl", "max_stars_repo_name": "akio-tomiya/LatticeQCD.jl", "max_stars_repo_head_hexsha": "a4ba4d5bee3ecce0545a438aaa3226d7f65737f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 74... |
[STATEMENT]
lemma filter_filter_mset_ss_member: "filter_mset (\<lambda> a . {x, y} \<subseteq> a) A =
filter_mset (\<lambda> a . {x, y} \<subseteq> a) (filter_mset (\<lambda> a . x \<in> a) A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. filter_mset ((\<subseteq>) {x, y}) A = filter_mset ((\<subseteq>) {x, y})... | {"llama_tokens": 1080, "file": "Design_Theory_Multisets_Extras", "length": 9} |
#!/usr/bin/env python3
#author markpurcell@ie.ibm.com
"""
IBM-Review-Requirement: Art30.3 - DO NOT TRANSFER OR EXCLUSIVELY LICENSE THE FOLLOWING CODE UNTIL 30/11/2025!
Please note that the following code was developed for the project MUSKETEER in DRL funded by the European Union
under the Horizon 2020 Program.
The pro... | {"hexsha": "fa3ae08070a299db9fce6e9d70740d840c5c2565", "size": 3347, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/basic/test_basic.py", "max_stars_repo_name": "cclauss/pycloudmessenger", "max_stars_repo_head_hexsha": "86155e5e150081fe7170d2de505bc9efcc4bbd81", "max_stars_repo_licenses": ["Apache-2.0"], ... |
[STATEMENT]
lemma inverse_float_interval_eq_Some_conv:
defines "one \<equiv> (1::float)"
shows
"inverse_float_interval p X = Some R \<longleftrightarrow>
(lower X > 0 \<or> upper X < 0) \<and>
lower R = float_divl p one (upper X) \<and>
upper R = float_divr p one (lower X)"
[PROOF STATE]
proof (pro... | {"llama_tokens": 224, "file": null, "length": 1} |
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import numpy as np
from sympy import *
dtype = np.float32
def bvp(kind: int, x_0: float, y_0: float, x_n: float, y_n: float, F: function, G: function):
x = Symbol('x')
y = Function('y')(x)
F, G = F(x), G(x)
ode = Eq(
y.diff... | {"hexsha": "a41787f5a2c050dc0e4214d279965e2dcc486ec0", "size": 2871, "ext": "py", "lang": "Python", "max_stars_repo_path": "bvp.py", "max_stars_repo_name": "pandov/mycourse", "max_stars_repo_head_hexsha": "0d55c4b714ae8293c0d9a60f98266829a0f42fb9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "ma... |
from supervised.perceptron import Perceptron
### Load data
## data shared
from load_data.data_inside.shared.andtable import LoadAndTable
from load_data.data_inside.shared.xortable import LoadXorTable
from load_data.data_inside.shared.titanic import LoadTitanic
from load_data.data_inside.not_shared.mnist_file import L... | {"hexsha": "5ef5f5d114c01b58289021740e99db57dd829c2b", "size": 1545, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "erickfmm/ML-experiments", "max_stars_repo_head_hexsha": "b1e81b8eea976efeda6e4dc70af747628a6eb43a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# this is a script that will test the solvers on some basic graphs.
# It requires MATLAB, CMG and LAMG.
using Laplacians
using MATLAB
include("/Users/spielman/Laplacians/compare/matlabSafe.jl")
include("/Users/spielman/Laplacians/compare/compare_solvers_TL.jl")
ac_deg = function(a; verbose=false, args...)
approx... | {"hexsha": "8c50cdcf5bd4887172528d950eb04185f2df36f2", "size": 2272, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "compare/baseTest.jl", "max_stars_repo_name": "HighDimensionalEconLab/Laplacians.jl", "max_stars_repo_head_hexsha": "25c75811f697ff1030ded0155d0d35c1fa3223c3", "max_stars_repo_licenses": ["MIT"], "m... |
"""
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
Relation-aware Graph Attention Network for Visual Question Answering
Linjie Li, Zhe Gan, Yu Cheng, Jingjing Liu
https://arxiv.org/abs/1903.12314
This code is written by Linjie Li.
"""
import numpy as np
import math
import torch
from torch.autogr... | {"hexsha": "727d0b47dd014a4e4850d619d59d9f924322c983", "size": 8624, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/position_emb.py", "max_stars_repo_name": "Lee-Ft/RHA", "max_stars_repo_head_hexsha": "8a832a9afebc9204148bbd340c31e26c83138024", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "ma... |
import os
from data import common
from data import srdata
import numpy as np
import scipy.misc as misc
from IPython import embed
import torch
import torch.utils.data as data
import glob
class DIV2KSUB(srdata.SRData):
def __init__(self, args, train=True):
super(DIV2KSUB, self).__init__(args, train)
... | {"hexsha": "1bdee31594b18f9c2460c911651ec4de627d5897", "size": 1548, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/div2ksub.py", "max_stars_repo_name": "authierj/EDSR-PyTorch", "max_stars_repo_head_hexsha": "cf67f0059276d72de7904c9605ca83f45a7a7002", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include <mitsuba/render/fresnel.h>
#include <mitsuba/layer/microfacet.h>
#include <mitsuba/layer/fourier.h>
#include <mitsuba/core/frame.h>
#include <mitsuba/core/math.h>
#include <enoki/special.h>
#include <Eigen/SVD>
#include <Eigen/LU>
#include <cmath>
#include <chrono>
#include <atomic>
using namespace std::chron... | {"hexsha": "2e7a86350fcad550e54c83639c6cf4d4be998f32", "size": 37711, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/liblayer/microfacet.cpp", "max_stars_repo_name": "tizian/layer-laboratory", "max_stars_repo_head_hexsha": "008cc94b76127e9eb74227fcd3d0145da8ddec30", "max_stars_repo_licenses": ["CNRI-Python"],... |
# -*- coding: utf-8 -*-
import numpy as np
from .utils import _get_broadcast_shape
def _check_dimension_type(csdm):
check = [item.type == "linear" for item in csdm.dimensions]
if not np.all(check):
raise NotImplementedError(
"Statistics is currently only available for linear dimensions."
... | {"hexsha": "f8e5df9c8c79adf2fc9974537d8a3bfa7cbcaafd", "size": 4193, "ext": "py", "lang": "Python", "max_stars_repo_path": "csdmpy/statistics.py", "max_stars_repo_name": "deepanshs/csdmpy", "max_stars_repo_head_hexsha": "bd4e138b10694491113b10177a89305697f1752c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
"""Testing for 'segmentation' function."""
import numpy as np
import pytest
import re
from pyts.utils import segmentation
@pytest.mark.parametrize(
'params, error, err_msg',
[({'ts_size': None, 'window_size': 3}, TypeError,
"'ts_size' must be an integer."),
({'ts_size': 4, 'window_size': None}, Ty... | {"hexsha": "be94847fdba872557244cf24ebb25679bb4d5d49", "size": 2083, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyts/utils/tests/test_segmentation.py", "max_stars_repo_name": "martanto/pyts", "max_stars_repo_head_hexsha": "1c0b0c9628068afaa57e036bd157fcb4ecdddee6", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import itertools
import numpy as np
from scipy.spatial.distance import cdist
from PIL import Image
import streamlit as st
from utils import FlowerArc, load_prec_embs
def main(top_k):
flower_arc = FlowerArc()
st.title("Flower retrieval")
train_img_fps, train_embs, train_labels = load_prec_embs()
up... | {"hexsha": "1ced95ab479e0dad8148f565bbfd9ad4145bdc8a", "size": 1217, "ext": "py", "lang": "Python", "max_stars_repo_path": "flower_st.py", "max_stars_repo_name": "Danglich/flowers102_retrieval_streamlit", "max_stars_repo_head_hexsha": "a16bf87be1e3c2da04f067d53a2fcf8172c6dd90", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma (in linorder_topology) compact_attains_sup:
assumes "compact S" "S \<noteq> {}"
shows "\<exists>s\<in>S. \<forall>t\<in>S. t \<le> s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>s\<in>S. \<forall>t\<in>S. t \<le> s
[PROOF STEP]
proof (rule classical)
[PROOF STATE]
proof (state)
goal... | {"llama_tokens": 2357, "file": null, "length": 21} |
import json
import codecs
import numpy as np
from keras import backend as K
from keras.models import Model
from keras.layers import Dense, Activation, LSTM, TimeDistributed, \
Convolution1D, MaxPooling1D, Highway, merge, Input, Masking, Bidirectional, \
Flatten, GlobalMaxPooling1D, AtrousConvolution1D
from kera... | {"hexsha": "b0e7b06327b9f41df595febf59d2b7d2b0fd6530", "size": 3941, "ext": "py", "lang": "Python", "max_stars_repo_path": "gru/model.py", "max_stars_repo_name": "tindzk/bsnlp", "max_stars_repo_head_hexsha": "5b3e7ca746506e426b399490f9da6a31aee83f9e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars... |
"""camwatcher: A component of the SentinelCam data layer.
Proivides subscriber services for log and video publishing from
outpost nodes. Drives a dispatcher to trigger other functionality.
Copyright (c) 2021 by Mark K Shumway, mark.shumway@swanriver.dev
License: MIT, see the sentinelcam LICENSE for more details.
"""
... | {"hexsha": "328bb855c2abdf91ee29469af7996467d023a6e6", "size": 12712, "ext": "py", "lang": "Python", "max_stars_repo_path": "camwatcher/camwatcher.py", "max_stars_repo_name": "shumwaymark/sentinelcam", "max_stars_repo_head_hexsha": "a6381915b6b315b354856e6895a1ceacb798d755", "max_stars_repo_licenses": ["MIT"], "max_sta... |
function options = scipset(varargin)
%SCIPSET Create or alter the options for Optimization with SCIP
%
% options = scipset('param1',value1,'param2',value2,...) creates an SCIP
% options structure with the parameters 'param' set to their corresponding
% values in 'value'. Parameters not specified will be set to the SCI... | {"author": "vigente", "repo": "gerardus", "sha": "4d7c5195b826967781f1bb967872410e66b7cd3d", "save_path": "github-repos/MATLAB/vigente-gerardus", "path": "github-repos/MATLAB/vigente-gerardus/gerardus-4d7c5195b826967781f1bb967872410e66b7cd3d/matlab/ThirdPartyToolbox/OptiToolbox/Utilities/Configuration/scipset.m"} |
import numpy as np
import torch
def rand_bbox_vector(size, lam):
# lam is a vector
B = size[0]
assert B == lam.shape[0]
W = size[2]
H = size[3]
cut_rat = np.sqrt(1. - lam)
cut_w = (W * cut_rat).astype(np.int)
cut_h = (H * cut_rat).astype(np.int)
# uniform
cx = np.random.randint... | {"hexsha": "86498b174a362f113ad9834111c17f3e70dae75b", "size": 1258, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/factory/train/cutmixup.py", "max_stars_repo_name": "i-pan/kaggle-melanoma", "max_stars_repo_head_hexsha": "caaec0d7e9cafc7b405eb86e7fdf00107d89e1d9", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Implementing Different Layers
#---------------------------------------
#
# We will illustrate how to use different types
# of layers in Tensorflow
#
# The layers of interest are:
# (1) Convolutional Layer
# (2) Activation Layer
# (3) Max-Pool Layer
# (4) Fully Connected Layer
#
# We will generate two different da... | {"hexsha": "c04e0ab3b212ec27d4c049771e36d938f97c671c", "size": 8939, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter 06/implementing_different_layers.py", "max_stars_repo_name": "makwakwa/Tensor_flow", "max_stars_repo_head_hexsha": "47c240d7fb9842f36ef2a51514dfce62bb973b6a", "max_stars_repo_licenses": ["... |
#include "Rules/UninitializedFieldRule.h"
#include "Common/Context.h"
#include "Common/OutputPrinter.h"
#include "Common/PodHelper.h"
#include "Common/SourceLocationHelper.h"
#include "Common/TagTypeNameHelper.h"
#include <clang/AST/Decl.h>
#include <clang/AST/Stmt.h>
#include <boost/format.hpp>
using namespace cla... | {"hexsha": "5d5aca2e018a51a2637b020a80e1f82aa1122518", "size": 8223, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Rules/UninitializedFieldRule.cpp", "max_stars_repo_name": "colobot/colobot-lint", "max_stars_repo_head_hexsha": "30921edb55b49dbda27d645357e24d6d22f5c48f", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import tensorflow as tf
import numpy as np
from mnist_cca import get_cca_data_as_matrices
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
import os
import pickle
from tensorflow.examples.tutorials.mnist import input_data
from progress.... | {"hexsha": "99ffaa791c0b5bb88fd63a7abd878339307516d3", "size": 13544, "ext": "py", "lang": "Python", "max_stars_repo_path": "cca_nn_classifier.py", "max_stars_repo_name": "Optimist-Prime/QML-for-MNIST-classification", "max_stars_repo_head_hexsha": "7513b3faa548166dba3df927a248e8c7f1ab2a15", "max_stars_repo_licenses": [... |
import mxnet as mx
import numpy as np
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def upsample_filt(size):
factor = (size + 1) // 2
if size % 2 == 1:
center = factor - 1
else:
center = factor - 0.5
og = np.ogrid[:size, :size]
return (1 - abs(og[0] - c... | {"hexsha": "e304b1c5b57d7357a5617cf91124a1fd032eab35", "size": 2395, "ext": "py", "lang": "Python", "max_stars_repo_path": "AFLW/init_vgg16.py", "max_stars_repo_name": "kli-nlpr/FaceDetection-ConvNet-3D", "max_stars_repo_head_hexsha": "f9251c48eb40c5aec8fba7455115c355466555be", "max_stars_repo_licenses": ["Apache-2.0"]... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name：PolySimple
Description : 多项式回归
数据使用随机生成的数据
http://sklearn.apachecn.org/cn/0.19.0/modules/linear_model.html#polynomial-regression
Email : autuanliu@163.com
Date：2017/12/17
"""
import numpy a... | {"hexsha": "00edec357212b6e8e1238739bfaf9f915456d56c", "size": 769, "ext": "py", "lang": "Python", "max_stars_repo_path": "ScikitLearn/PolySimple.py", "max_stars_repo_name": "AutuanLiu/Machine-Learning-on-docker", "max_stars_repo_head_hexsha": "00eb7211a3a40a9da02114923647dfd6ac24f138", "max_stars_repo_licenses": ["Apa... |
import random
import numpy as np
from numba import jit, njit
import matplotlib.pyplot as plt
import scipy
import scipy.spatial
import time
from typing import Tuple
from prm_NR import adjacency_mat
from matrix_utils import is_primitive
@jit(forceobj=True)
def sample_points(sx, sy, gx, gy, rr, ox, oy, N, bot_size):
... | {"hexsha": "bbd17a2c23572ca2a1333376ae7f48e0ef63b12b", "size": 6045, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mapping.py", "max_stars_repo_name": "smallpondtom/DO-PRM", "max_stars_repo_head_hexsha": "b44cf80123c1ff80b55b6e6ed9b21cce04bebf30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import sys
sys.path.append(r"/home/andrea/casadi-py27-np1.9.1-v2.4.2")
from casadi import *
from numpy import *
from scipy.linalg import *
import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
N = 80 # Control discretization
T = 4.0 # End time
nx = 6
nu = 2
N_sim = 600
# Declare variab... | {"hexsha": "d3cb07804f722d247d7f46959c96bec8207323b5", "size": 9869, "ext": "py", "lang": "Python", "max_stars_repo_path": "robot/ipopt/robot_bfgs.py", "max_stars_repo_name": "embotech/forcesnlp-examples", "max_stars_repo_head_hexsha": "d601a98ca8bb3f3fbe1b96fbe683d4de6f250948", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
#
# Copyright (C) 2019 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG),
# acting on behalf of its Max Planck Institute for Intelligent Systems and the
# Max Planck Institute for Biological Cybernetics. All rights reserved.
#
# Max-Planck-Gesellschaft zur Förderung der Wissens... | {"hexsha": "f7e0f303bd6441a34f0a075fa9219cccd2a9cf2f", "size": 12919, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/human_body_prior/models/ik_engine.py", "max_stars_repo_name": "zephyr-fun/human_body_prior", "max_stars_repo_head_hexsha": "35571fe16fddca39553398f6b3eb6d18a23c985b", "max_stars_repo_licenses... |
#include <stdlib.h>
#include <chrono>
#include <ctime>
#include <cstring>
#include <iostream>
#include <iomanip>
#include <vector>
#include <set>
#include "boost/lexical_cast.hpp"
#include "boost/uuid/uuid.hpp"
#include "boost/uuid/uuid_generators.hpp"
#include "boost/uuid/uuid_io.hpp"
#include "boost/filesystem.hpp... | {"hexsha": "5e14b80913b085c29294638075696153e6c15f7c", "size": 25450, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/cpp_client/srcmain.cc", "max_stars_repo_name": "marinadudarenko/bigartm", "max_stars_repo_head_hexsha": "c7072663581c59e970ef165a577dc4969810a19d", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import numpy as np
import sys
from numpy.core.numeric import Infinity
from enum import Enum
class BlendingType(Enum):
noblend = 1
addingblend = 2
nonzerovelblend = 3
class TrapezoidalVelocityProfilePlanner:
def __init__(self, dof, ts, vmax, amax) -> None:
self.dof = dof
self.ts = ts
... | {"hexsha": "773c7b085b53b6e5dde3da54d70bcf28d5291152", "size": 35943, "ext": "py", "lang": "Python", "max_stars_repo_path": "tvpp/tvpp.py", "max_stars_repo_name": "szmlb/tvpp", "max_stars_repo_head_hexsha": "b103b1ce17c2d9bdd3316579529f4d9b1fa52ca7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
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