text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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from __future__ import division
import numpy as np
from operator import add
from functools import reduce
from scipy.stats import gamma
import revrand.basis_functions as bs
from revrand.btypes import Parameter, Positive, Bound
from revrand.utils import issequence
def test_simple_concat(make_gaus_data):
X, _, _,... | {"hexsha": "0eca6bdbbb0d7528dd37da320092203c5aa70aa1", "size": 7043, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_bases.py", "max_stars_repo_name": "smokarizadeh/revrand", "max_stars_repo_head_hexsha": "52288c48eb3c3d5d887292ff669b33babdefcda7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
// Copyright 2014-2015 SDL plc
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing,... | {"hexsha": "83a2427b895dcafdedf1339b18eff2b91f77ada0", "size": 11335, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "sdl/Util/Index.hpp", "max_stars_repo_name": "sdl-research/hyp", "max_stars_repo_head_hexsha": "d39f388f9cd283bcfa2f035f399b466407c30173", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
from sklearn import metrics
from sklearn.manifold import TSNE
@torch.no_grad()
def predict(model, dataloader):
"""Returns: numpy arrays of true labels and predicted probabilities."""
device = torch.device("cuda" if torch.cud... | {"hexsha": "c8f774e3c6fbf368570bb2b243d5682813640ff6", "size": 1785, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "yuanx749/deep-vaccine", "max_stars_repo_head_hexsha": "11c2e7fbb1c89a08f96bada1153bd56577e592ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# Adapted from https://github.com/huggingface/transformers/blob/21da895013a95e60df645b7d6b95f4a38f604759/examples/run_glue.py
# for training GPT-2 medium for sequence classification with GeDi objective
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2... | {"hexsha": "4c2e3da4306c95f681d2f8cbd5f6906090cf868f", "size": 47681, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_GeDi.py", "max_stars_repo_name": "hkchae96/control_generation_DA", "max_stars_repo_head_hexsha": "c5c1f9c0b49a0f6f6a18210621ee7a96c3023a81", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
#ifndef LIBKRIGING_BINDINGS_OCTAVE_TOOLS_MX_ACCESSOR_HPP
#define LIBKRIGING_BINDINGS_OCTAVE_TOOLS_MX_ACCESSOR_HPP
#include <armadillo>
#include <cstring>
#include "ObjectAccessor.hpp"
#include "mex.h"
template <typename T>
struct converter_trait {
using type = T;
};
template <>
struct converter_trait<ObjectRef> {... | {"hexsha": "64d756c43c72ff868c6d1cd6fc4bdbaf372dc365", "size": 3065, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "bindings/Octave/tools/mx_accessor.hpp", "max_stars_repo_name": "yannrichet/libKriging", "max_stars_repo_head_hexsha": "25475d1de02d518401183e93f6a0fa5c12b3c96b", "max_stars_repo_licenses": ["Apache-... |
//////////////////////////////////////////////////////////////////////////////////////////////
/// \file BlockVector.hpp
///
/// \author Sean Anderson
//////////////////////////////////////////////////////////////////////////////////////////////
#ifndef STEAM_BLOCK_VECTOR_HPP
#define STEAM_BLOCK_VECTOR_HPP
#include <... | {"hexsha": "db9e54f5bc7ed01d6b6519b6522433c149fcbaf3", "size": 4011, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/steam/blockmat/BlockVector.hpp", "max_stars_repo_name": "neophack/steam", "max_stars_repo_head_hexsha": "28f0637e3ae4ff2c21ad12b2331c535e9873c997", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import tensorflow as tf
import os
import time
import numpy as np
class bcolors:
WARNING = '\033[93m'
ENDC = '\033[0m'
#Check if MKL is enabled
import tensorflow.python.framework as tff
print(bcolors.WARNING + "MKL Enabled : ", tff.test_util.IsMklEnabled(), bcolors.ENDC)
#Set threads
tf.config.threading.set... | {"hexsha": "6a91e91ba2583f5a488c7953a1c612fcdb23d449", "size": 1221, "ext": "py", "lang": "Python", "max_stars_repo_path": "EagerVsGraphVsC/cse/cse_tf_graphMode.py", "max_stars_repo_name": "as641651/LinearAlgera-Awareness-Benchmark", "max_stars_repo_head_hexsha": "1fbbca4229d5869f35a20193c6f23414fbc81ab7", "max_stars_r... |
import numpy as np
from ._CFunctions import _CTraceField
import PyFileIO as pf
from .ct import ctBool,ctInt,ctIntPtr,ctDouble,ctDoublePtr,ctDoublePtrPtr
import matplotlib.pyplot as plt
from .PlotPlanet import PlotPlanetXY, PlotPlanetXZ, PlotPlanetYZ
class TraceField(object):
def __init__(self,*args,**kwargs):
'''
... | {"hexsha": "c4890a23527edb8ab2ef7ef46b21d99a599ca7a8", "size": 18673, "ext": "py", "lang": "Python", "max_stars_repo_path": "KT17/TraceField.py", "max_stars_repo_name": "mattkjames7/KT17", "max_stars_repo_head_hexsha": "5550afeb9fea4255b0f0105021df23743c14744f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
"""
test data for pose functions
"""
# global
import ivy.numpy
import numpy as np
# local
import ivy_mech
from ivy_mech_tests.test_orientation.orientation_data import OrientationTestData
class PoseTestData(OrientationTestData):
def __init__(self):
super(PoseTestData, self).__init__()
# cartesi... | {"hexsha": "5023b2ae7b9728333f3b5c7f105b11ce1aa2cc94", "size": 2341, "ext": "py", "lang": "Python", "max_stars_repo_path": "ivy_mech_tests/test_pose/pose_data.py", "max_stars_repo_name": "unifyai/mech", "max_stars_repo_head_hexsha": "d678c8732ee5aba4a92fb37b96519cd06553c0c6", "max_stars_repo_licenses": ["Apache-2.0"], ... |
module Hello
using Bukdu
struct WelcomeController <: ApplicationController
conn::Conn
end
function index(::WelcomeController)
render(Text, "hello")
end
function __init__()
routes() do
get("/", WelcomeController, index)
end
end
function julia_main()::Cint
try
port = isempty(ARGS)... | {"hexsha": "1726ec872ef3d863301b45f4dcbdf76eaa34d358", "size": 501, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/app/Hello/src/Hello.jl", "max_stars_repo_name": "wookay/Bukdu.jl", "max_stars_repo_head_hexsha": "a2b3e9f5ce46bb50af3749bd9846adc03ecc2cb1", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\section{Margin Classification}
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import numpy as np
import typing as typ
from . import core
from . import data
def meanap(predicts: typ.List[data.DetectResult], grandtruths: typ.List[data.DetectResult], num_classes: int, iou_threshold: float = 0.5):
nm_existed_classes = 0
ap_existed_classes = 0
for presitions, recalls in roccurve(predict... | {"hexsha": "3bb0a170cb569824a219fc91767b62205719dfd2", "size": 4871, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/metrics.py", "max_stars_repo_name": "namoshika/ssd_keras", "max_stars_repo_head_hexsha": "8ac4db114d310ad6f904731218fa4986a3178fd8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import numpy as np
from time import time
import os
import multiprocessing as mp
import pickle
from flare.mgp.mgp import MappedGaussianProcess
from flare.env import AtomicEnvironment
from flare.gp import GaussianProcess
from flare.struc import Structure
from flare.output import Output
from flare.otf_parser import OtfAn... | {"hexsha": "6431431a19d58f5d3972beb98b648527c76c0055", "size": 2416, "ext": "py", "lang": "Python", "max_stars_repo_path": "Hierarchical/mgp-2/train_hyps/opt_hyps.py", "max_stars_repo_name": "YuuuuXie/Stanene_FLARE", "max_stars_repo_head_hexsha": "b6678927dd7fe3b6e6dc405a5f27d1a3339782eb", "max_stars_repo_licenses": ["... |
module constants
implicit none
integer, parameter :: quad = selected_real_kind(33, 4931)
real(quad), parameter :: q_pi = 3.1415926535897932384626433832795
end module constants
| {"hexsha": "415e02a4069811865d185b45b1134d7a2a7346cc", "size": 183, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "constants.f90", "max_stars_repo_name": "pgniewek/hhg-1d", "max_stars_repo_head_hexsha": "7265e814c00bdccdaba54be40f231511e18a90b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 5 08:05:02 2016
plot potentials on MEA with a 11x11 figure.
@author: young
"""
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
def find_extremum(listName):
minValue = np.min(listName)
maxVal... | {"hexsha": "818df4e2ec87c161bab71ac240ad5d4a4d328cec", "size": 1472, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulation-code/plot_LFPonMEA.py", "max_stars_repo_name": "young24/LFP-simulation-in-turtle-brain", "max_stars_repo_head_hexsha": "cd801dc02804d027b7c245b0f0ca9c8b00f8d450", "max_stars_repo_licens... |
#!/usr/bin/env python3
import os
import time
import cv2
import numpy as np
import pickle as pkl
import png
import nori2
from ip_basic import depth_map_utils, depth_map_utils_ycb
from ip_basic import vis_utils
import sys
sys.path.append('..')
from lib.utils.my_utils import my_utils
from neupeak.utils.webcv2 import ims... | {"hexsha": "74b6ec93eb37c07ca5ab692521e7ca22a9a331f8", "size": 4844, "ext": "py", "lang": "Python", "max_stars_repo_path": "pvn3d/lib/utils/ip_basic/ycb_fill_depth.py", "max_stars_repo_name": "JiazeWang/PVN3D", "max_stars_repo_head_hexsha": "07241f5e0de488c123cd78f516a707bff207c2e0", "max_stars_repo_licenses": ["MIT"],... |
\section{Syntactic Guardedness}
\label{sec:synt-guard-1}
% Sune
%#######
% Hvad er syntactic guardedness?
% Hvordan virker det?
% Hvorfor syntactic guardedness?
% Hvor kommer syntactic guardedness fra? Hvilken problemstilling løser det?
% Hvor kommer syntactic guardedness til kort?
% Ny viden: Hvordan syntactic guarde... | {"hexsha": "91be11a92c3d99f0ef5390426320743d90007d43", "size": 4497, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/background/syntactic_guardedness.tex", "max_stars_repo_name": "sualitu/thesis", "max_stars_repo_head_hexsha": "22d2cb4f21dc7c2dab011da5bb560c003650a2bc", "max_stars_repo_licenses": ["MIT"],... |
# -*- coding: utf-8 -*-
"""
For equal incident photon flux, compare the SNR.
"""
import numpy as np
import glob
import tomopy
import dxchange
import matplotlib.pyplot as plt
from project import *
from simulator import *
from sinogram import *
from instrument import *
from sample import *
if __name__ == '__main__':
... | {"hexsha": "a3a6edb0939312ed9c5b5155151297f69b1962d3", "size": 1526, "ext": "py", "lang": "Python", "max_stars_repo_path": "sim_shepp.py", "max_stars_repo_name": "mdw771/tomosim", "max_stars_repo_head_hexsha": "7736031aee861cd0ac995d83c2231a7df4fc3365", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "... |
[STATEMENT]
lemma Suc_m_minus_n[simp]:
shows "m \<ge> n \<longrightarrow> Suc m - n = Suc (m - n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. n \<le> m \<longrightarrow> Suc m - n = Suc (m - n)
[PROOF STEP]
by auto | {"llama_tokens": 98, "file": "CISC-Kernel_trace_Rushby-with-Control_List_Theorems", "length": 1} |
#-*- coding: utf8
from __future__ import division, print_function
from pyksc.dist import dist_all
import numpy as np
def cost(tseries, assign, centroids, dist_centroids=None):
num_series = tseries.shape[0]
if dist_centroids is None:
dist_centroids = dist_all(centroids, tseries)
cost_f ... | {"hexsha": "325e59488bec1ab5d7d879cc95ec077c6813bc42", "size": 2358, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pyksc/metrics.py", "max_stars_repo_name": "flaviovdf/pyksc", "max_stars_repo_head_hexsha": "6ba8988c7fad63366dc2b8d005d0779971e129c5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
#!/usr/bin/env python3
import torch
def _cross2d(a, b):
"""Cross product in 2D."""
return a[:, 0] * b[:, 1] - a[:, 1] * b[:, 0]
def _remove(T, idx_remove):
"""Remove an element from the list of points for each batch element."""
num_boxes = T.shape[0]
num_points_left = T.shape[1]
# Define wh... | {"hexsha": "1851922e80a5f9a775c9df785867bb98386a7b6d", "size": 5020, "ext": "py", "lang": "Python", "max_stars_repo_path": "dafne/utils/sort_corners.py", "max_stars_repo_name": "qilei123/DAFNe", "max_stars_repo_head_hexsha": "6ae6c17ecef6b88e21843969e456fc83b34da0fe", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
def identity_scov(s, y, k=1, e=1):
""" Useful when you don't want to waste compute """
# See equal_managers for usage example
n_dim = len(y)
return np.eye(n_dim)
| {"hexsha": "0b53f6dc318fc713007db628f7e8e443d6d9f6bb", "size": 200, "ext": "py", "lang": "Python", "max_stars_repo_path": "precise/skaters/covariance/identity.py", "max_stars_repo_name": "microprediction/precise", "max_stars_repo_head_hexsha": "0aa7c69c3c280926cec03fb6fc0934a6193da440", "max_stars_repo_licenses": ["MIT... |
from typing import Iterable, List, Tuple
from common.constants import DEFAULT_SCALE_RADIUS, DEFAULT_SCALE_WL
from spectrum import Spectrum
def align_wavelengths( s0: Iterable, s1: Iterable, wl_low: float = None, wl_high: float = None ) -> set:
"""
Takes in an iterable (such as a Spectrum or list of wavelengt... | {"hexsha": "f073274be6757e09e8098433e2820401c42eba1b", "size": 5950, "ext": "py", "lang": "Python", "max_stars_repo_path": "spectrum/utils.py", "max_stars_repo_name": "chris-wahl/SDSS_QSO", "max_stars_repo_head_hexsha": "35807ecbc819d89fd4141748b31ce2a51a1c2d34", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import logging
from Simulation import Position
from Utilities import Consts, TimeHelper
from scipy.stats import norm
from Utilities import DataHelper
from Simulation.Statistics import Stats
logger = logging.getLogger("Portfolio")
# Redis with all coins market data
coins_market_data_getter = DataHelper.DataHelper()
... | {"hexsha": "7116c62af757562a9c8311a9af23ff036a2cde98", "size": 22601, "ext": "py", "lang": "Python", "max_stars_repo_path": "Simulation/Portfolio.py", "max_stars_repo_name": "ACCrypto-io/TradingSimulator", "max_stars_repo_head_hexsha": "8b3ebecde2496d0e00dd4442ab57e92f4cda5de3", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import onnx
import onnxruntime
from dnnv.nn.converters.onnx import *
from dnnv.nn.operations import *
def test_Cast_consts():
x = np.arange(12).reshape((1, 3, 2, 2))
op = Cast(x, onnx.TensorProto.FLOAT)
onnx_model = convert(OperationGraph([op]))
results = onnxruntime.backend.run(... | {"hexsha": "f2e9e4084bf8d5ad96098b56e02c6ad022c23874", "size": 796, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit_tests/test_nn/test_converters/test_onnx/test_Cast.py", "max_stars_repo_name": "samysweb/dnnv", "max_stars_repo_head_hexsha": "58fb95b7300914d9da28eed86c39eca473b1aaef", "max_stars_repo_l... |
from flee import flee
from datamanager import handle_refugee_data
from datamanager import DataTable
import numpy as np
import outputanalysis.analysis as a
import sys
"""
Generation 1 code. Incorporates only distance, travel always takes one day.
"""
#Burundi Simulation
def date_to_sim_days(date):
return DataTable... | {"hexsha": "f7c8d94754bfa695ade7d0fec22501f95614a64d", "size": 11843, "ext": "py", "lang": "Python", "max_stars_repo_path": "burundi.py", "max_stars_repo_name": "alirezajahani60/flee-release", "max_stars_repo_head_hexsha": "33646c2224a7ef3677f82c11bf7525083e9e649c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import numpy as np
import cv2
import math
def find_distance_dot2dot(point1, point2):
return math.sqrt((point1[0] - point2[0]) * (point1[0] - point2[0]) + (point1[1] - point2[1]) * (point1[1] - point2[1]))
def center(points):
center_x = (points[0][0][0] + points[1][0][0] + points[2][0][0] + points[3][0][0])/4.... | {"hexsha": "9b35b719d50f5919145ae6984365ef2e4e432afd", "size": 4261, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/signal_sign_detection/creating_training_dataset.py", "max_stars_repo_name": "Kihoon0716/self_driving-loading-", "max_stars_repo_head_hexsha": "084874ca1558ee92883bb32a74aa72726ac31744", "max_s... |
{-# OPTIONS --rewriting --allow-unsolved-metas #-}
open import Agda.Builtin.Equality
open import Agda.Builtin.Equality.Rewrite
postulate
I : Set
A : I → Set
HEq : (i0 i1 : I) → A i0 → A i1 → Set
HEq-on-refl : (i : I) (a0 a1 : A i) → HEq i i a0 a1 ≡ I
{-# REWRITE HEq-on-refl #-}
record Con : Set where
field... | {"hexsha": "e38e6e2c4843dffbb98ad120ada1b472199f5eb3", "size": 562, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Succeed/Issue4721.agda", "max_stars_repo_name": "cagix/agda", "max_stars_repo_head_hexsha": "cc026a6a97a3e517bb94bafa9d49233b067c7559", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_... |
from strategy.astrategy import AStrategy
from datetime import datetime, timedelta, timezone
from sklearn.impute import SimpleImputer
import numpy as np
import math
import pandas as pd
import pickle
class QuarterlyFinancial(AStrategy):
def __init__(self,year,quarter,ticker,yearly_gap,training_years):
"""
... | {"hexsha": "0d627c678fe842c3981bf3f214b5dc58ef96cdc8", "size": 5328, "ext": "py", "lang": "Python", "max_stars_repo_path": "strategy/quarterly_financial.py", "max_stars_repo_name": "ajmal017/longshot", "max_stars_repo_head_hexsha": "0978fb107ab83034372e0e633483d381ac06f25f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
section \<open> Lifting Expressions \<close>
theory utp_lift
imports
utp_alphabet utp_lift_pretty
begin
subsection \<open> Lifting definitions \<close>
text \<open> We define operators for converting an expression to and from a relational state space
with the help of alphabet extrusion and restriction. In ge... | {"author": "isabelle-utp", "repo": "utp-main", "sha": "27bdf3aee6d4fc00c8fe4d53283d0101857e0d41", "save_path": "github-repos/isabelle/isabelle-utp-utp-main", "path": "github-repos/isabelle/isabelle-utp-utp-main/utp-main-27bdf3aee6d4fc00c8fe4d53283d0101857e0d41/utp/utp_lift.thy"} |
theory Sound
imports op HHL
begin
(*The definition for the soundness of sequtial process*)
definition Valid :: "fform => proc => fform => fform => bool" ("Valid _ _ _ _")
where "Valid p Q q H =
(ALL f d f' d'. (evalP (Q, f, d) = (Skip, f', d')) --> evalF (last(f(d)), p)
--> (evalF (last(f'(d')),... | {"author": "wangslyl", "repo": "hhlprover", "sha": "500e7ae1f93f0decb67b55ec2e0b4f756ae9ede0", "save_path": "github-repos/isabelle/wangslyl-hhlprover", "path": "github-repos/isabelle/wangslyl-hhlprover/hhlprover-500e7ae1f93f0decb67b55ec2e0b4f756ae9ede0/HHLProver/Sound.thy"} |
import numpy as np
import torch as th
from gym import spaces
from go_explore.cells import DownscaleObs, ImageGrayscaleDownscale
from go_explore.feature_extractor import GoExploreExtractor
def test_feature_extractor():
observation_space = spaces.Dict({"observation": spaces.Box(-1, 1, (2,)), "goal": spaces.Box(-1,... | {"hexsha": "930d3baa285a558128eb7f6e2fc4916e7f43ddbf", "size": 1590, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/feature_extractor_test.py", "max_stars_repo_name": "qgallouedec/go-explore", "max_stars_repo_head_hexsha": "87416a00ade6d35011ce1de9af883a942ec21ccc", "max_stars_repo_licenses": ["MIT"], "ma... |
from discoverlib import geom
from discoverlib import graph
import math
import numpy
import os
import scipy.ndimage
PATH = '/data/spacenet2017/favyen/segmentation_model4d3/outputs'
OUT_PATH = '/data/spacenet2017/favyen/segmentation_model4d3_newskeleton/graphs'
TOL = 10
THRESHOLD = 20
circle_mask = numpy.ones((2*TOL+1... | {"hexsha": "86332b86c356af45a397fa8afae479d5015e4422", "size": 2211, "ext": "py", "lang": "Python", "max_stars_repo_path": "fbastani-solution/skeleton.py", "max_stars_repo_name": "Hulihrach/RoadDetector", "max_stars_repo_head_hexsha": "9fedd537d7d3a5c81a60562a185fc13370af9a99", "max_stars_repo_licenses": ["Apache-2.0"]... |
#!/usr/bin/env python
"""Test a neural network."""
# Third party modules
import numpy
# First party modules
import nntoolkit.evaluate as evaluate
import nntoolkit.utils as utils
def main(model_file: str, test_data: str, verbose=True) -> float:
"""
Evaluate a model
Parameters
----------
model_f... | {"hexsha": "c5fb46bc352ea0816dafa5e13177c7e79086f325", "size": 1084, "ext": "py", "lang": "Python", "max_stars_repo_path": "nntoolkit/test.py", "max_stars_repo_name": "MartinThoma/nntoolkit", "max_stars_repo_head_hexsha": "1f9eed7b6d6fdacc706060d9cbfefaa9c2d0dbf8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""A data cleaning Python tool."""
import sys
import os
# print("Current working directory")
# print(os.getcwd()) # get current working directory
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import missingno as msno
import random
import pprint
from IPython.core.displa... | {"hexsha": "a4b6a8b23cd627158998df3a95efa38ec94e15e9", "size": 50467, "ext": "py", "lang": "Python", "max_stars_repo_path": "dcbot/dcbot.py", "max_stars_repo_name": "stger040/dcbot", "max_stars_repo_head_hexsha": "ec052da8c6f6092ec71435c03a31d62d5f5ff8ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
\documentclass[12pt]{article}
\usepackage{fullpage}
\usepackage{color}
\newcommand{\TBD}[1]{{\color{blue}{\bf TBD:} #1}}
\begin{document}
\section{C-style interface to the MDD Library}
\subsection{Forest Operations}
\subsubsection{int create\_forest()}
Initializes the mdd\_forest.
If the forest is already init... | {"hexsha": "33e6f70ab0521f63746555bba61ee0bbe7c0e912", "size": 3945, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "meddly/manual/InterfaceDoc.tex", "max_stars_repo_name": "trolando/ParallelSaturationExperiments", "max_stars_repo_head_hexsha": "ee374ed750d9d3fde1b44bf4414bef5289805090", "max_stars_repo_licenses":... |
[STATEMENT]
lemma states_of_se_assign2 :
assumes "se c (Assign v e) c'"
assumes "\<exists> \<sigma> \<in> states c. \<sigma>' = \<sigma> (v := e \<sigma>)"
shows "\<sigma>' \<in> states c'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<sigma>' \<in> states c'
[PROOF STEP]
proof -
[PROOF STATE]
proof (stat... | {"llama_tokens": 4588, "file": "InfPathElimination_SymExec", "length": 41} |
import numpy as np
from data_loader import DataLoader
import random
class ReccurentNetwork:
def __init__(self, data, size):
self.data = data
self.input_size = size
self.output_size = size
self.hidden_size = 100
# Initialize weights and biases
self.W_input = np.rand... | {"hexsha": "f4449ce7ff22580431017802b35610326724a8a1", "size": 5779, "ext": "py", "lang": "Python", "max_stars_repo_path": "rnn.py", "max_stars_repo_name": "yustoris/kaomoji_with_rnn", "max_stars_repo_head_hexsha": "a7ea3642da5ec13b337e06e71110e2da8658eea8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
from __future__ import division
import sys
import subprocess
import os
from os.path import isfile
from os.path import join as jn
from os import listdir
import time
from numpy import mean
from numpy import median
import numpy
from scipy.stats import kurtosis,skew #2018-10-24 scipy.stats throwing error with conda
def ... | {"hexsha": "f087e0fcca9a83c299b5db04f8d2698b2f0b65f7", "size": 15114, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/Coverage_Discrepancy/bin/Coverage_discrepancy_analysis.py", "max_stars_repo_name": "kbdeleon/Coverage_Discrepancy", "max_stars_repo_head_hexsha": "a5077059607ddcef32662d485a5aa6b73a9dd28e", "... |
//=============================================================================
//
// Copyright (c) Kitware, Inc.
// All rights reserved.
// See LICENSE.txt for details.
//
// This software is distributed WITHOUT ANY WARRANTY; without even
// the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
// ... | {"hexsha": "962dc82e5094877e08b6ce1aeabfa23e61d138e7", "size": 6226, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "remus/testing/benchmarks/WorkerMessagePerformance.cxx", "max_stars_repo_name": "robertmaynard/Remus", "max_stars_repo_head_hexsha": "090a14c9a4b0e628a86590dcfa7e46ba728e9c04", "max_stars_repo_licens... |
import numpy as np
import os
import pickle
import torch
def load_data_3d2d_modelnet40(data_folder, dataset_split, preprocessed=True):
if preprocessed:
var_name_list = ["p2d", "p3d", "R_gt", "t_gt", "W_gt", "num_points_2d", "num_points_3d"]
subfolder = 'preprocessed'
encoding='ASCII'
els... | {"hexsha": "ce399994ab73ba7e58c9f73342dae979e31038bf", "size": 10909, "ext": "py", "lang": "Python", "max_stars_repo_path": "utilities/dataset_utilities.py", "max_stars_repo_name": "metrized-inc/bpnpnet", "max_stars_repo_head_hexsha": "5dbc9dfaddf74d5b7dcda2eb537738511d1160e5", "max_stars_repo_licenses": ["MIT"], "max_... |
# *****************************************************************************
# Copyright (c) 2021, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions ... | {"hexsha": "1b6e34a972814dd831e43039ac24ef313489c30b", "size": 2377, "ext": "py", "lang": "Python", "max_stars_repo_path": "sdc/extensions/sdc_string_view_type.py", "max_stars_repo_name": "dlee992/sdc", "max_stars_repo_head_hexsha": "1ebf55c00ef38dfbd401a70b3945e352a5a38b87", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
from __future__ import print_function
import os
import pickle
import tempfile
import unittest
import numpy as np
import sklearn.datasets as datasets
import sklearn.linear_model as glm
import sklearn.neighbors as knn
from mlflow import sklearn, pyfunc
import mlflow
from mlflow.models import Model
from mlflow.tracking... | {"hexsha": "44306deab4f94e3a67e62d2255f488af06aa3ebb", "size": 3567, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/sklearn/test_sklearn_model_export.py", "max_stars_repo_name": "yutannihilation/mlflow", "max_stars_repo_head_hexsha": "a4386c3f87923e395ba8f523e1a90749e888a541", "max_stars_repo_licenses": [... |
#!/usr/bin/env python3
###############################################################################
# #
# RMG - Reaction Mechanism Generator #
# ... | {"hexsha": "5ea4749db0b814b79f4fd56d276f5332d46e98eb", "size": 30722, "ext": "py", "lang": "Python", "max_stars_repo_path": "rmgpy/tools/fluxdiagram.py", "max_stars_repo_name": "pm15ma/RMG-Py", "max_stars_repo_head_hexsha": "ca2f663c711ec45012afc911138716aaf0049296", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# -*- coding: utf-8 -*-
# This file is part of RRMPG.
#
# RRMPG is free software with the aim to provide a playground for experiments
# with hydrological rainfall-runoff-models while achieving competitive
# performance results.
#
# You should have received a copy of the MIT License along with RRMPG. If not,
# see <http... | {"hexsha": "8a941df9d072b47eeff4cdaa158522e22762f7e9", "size": 13564, "ext": "py", "lang": "Python", "max_stars_repo_path": "rrmpg/models/hbvedu.py", "max_stars_repo_name": "tommylees112/RRMPG", "max_stars_repo_head_hexsha": "585c9ec8fe5564fe312be8137851822c2a56dafc", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Pyph Histogram
# Generate histograms of images
# Copyright 2011 Adam Greig
# Released under the simplified BSD license, see LICENSE
import Image
import numpy
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
def gen_histogram(infile, outfile):
"""Generate a colour ... | {"hexsha": "d4f196840291845d8bc0cb4eb65b641a1934130c", "size": 747, "ext": "py", "lang": "Python", "max_stars_repo_path": "histogram.py", "max_stars_repo_name": "adamgreig/Pyph", "max_stars_repo_head_hexsha": "16ab80896581f0a8e3dca1195f9bbd7866834c20", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 4, ... |
/-
Copyright (c) 2017 Scott Morrison. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Scott Morrison
-/
import Mathlib.PrePort
import Mathlib.Lean3Lib.init.default
import Mathlib.category_theory.products.bifunctor
import Mathlib.PostPort
universes u₁ u₂ u₃ v₁ v₂ v₃
n... | {"author": "AurelienSaue", "repo": "Mathlib4_auto", "sha": "590df64109b08190abe22358fabc3eae000943f2", "save_path": "github-repos/lean/AurelienSaue-Mathlib4_auto", "path": "github-repos/lean/AurelienSaue-Mathlib4_auto/Mathlib4_auto-590df64109b08190abe22358fabc3eae000943f2/Mathlib/category_theory/currying.lean"} |
from numpy import *
import numpy as np
def replace_line(file_name, line_num, text):
lines = open(file_name, 'r').readlines()
lines[line_num] = text
out = open(file_name, 'w')
out.writelines(lines)
out.close()
print ' '
print ' '
print '........................'
print 'Begin texmaker.py'
print '........ | {"hexsha": "e62e75c22e9efa40d303c21df553adaea6b605a8", "size": 1717, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python_src/texmaker.py", "max_stars_repo_name": "wallamejorge/WirelessSensorGas", "max_stars_repo_head_hexsha": "868a5c3388daabc6f8be95386f676b12b5785bf2", "max_stars_repo_licenses": ["Apache-... |
'''
Python 3.6
Pytorch 0.4
Written by Hongyu Wang in Beihang university
'''
import torch
import math
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import numpy
import torch.utils.data as data
from data_iterator import dataIterator
from Densenet_torchvision import densenet121... | {"hexsha": "1c2e0f739c71c9793927448ad4aeba67410ef221", "size": 13713, "ext": "py", "lang": "Python", "max_stars_repo_path": "Train.py", "max_stars_repo_name": "karino2/Pytorch-Handwritten-Mathematical-Expression-Recognition", "max_stars_repo_head_hexsha": "6c6139624c71fa68a0a386a94346cfab39d0f087", "max_stars_repo_lice... |
from torch_sampler import BySequenceLengthSampler
from torch.utils.data import DataLoader
from dataset import MyDataset
from torch.nn.utils.rnn import pad_sequence
import torch
import numpy as np
np.random.seed(0)
torch.manual_seed(0)
bucket_boundaries = [1, 4, 7, 10]
batch_sizes=32
my_data = MyDataset()
sampler =... | {"hexsha": "b3ad65fa6119948596666e2c0c6e69819fdafa87", "size": 844, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_torch_sampler.py", "max_stars_repo_name": "zaemyung/Pytorch-Sequence-Bucket-Iterator", "max_stars_repo_head_hexsha": "0abcfe2e623f1304a10ce007e6cf44a5564f7769", "max_stars_repo_licenses": ["Ap... |
"""
This software is governed by the CeCILL-B license under French law and
abiding by the rules of distribution of free software. You can use,
modify and/ or redistribute the software under the terms of the CeCILL-B
license as circulated by CEA, CNRS and INRIA at the following URL
"http://www.cecill.info".
... | {"hexsha": "db2cacc357a08d595fbf338bbff3ce8b9149ed89", "size": 4221, "ext": "py", "lang": "Python", "max_stars_repo_path": "UnSynGAN/utils/patches.py", "max_stars_repo_name": "rousseau/deepBrain", "max_stars_repo_head_hexsha": "c404872bec975d52a50bda49b326068b7834dcda", "max_stars_repo_licenses": ["CECILL-B"], "max_sta... |
#!/usr/bin/env python
#=============================================================================================
# MODULE DOCSTRING
#=============================================================================================
"""
atomtyper.py
Atom type assignment engine using SMARTS strings.
Authors
-------
Jo... | {"hexsha": "45c025b5bbbfe18cbcb58123e808c4adad6dad6a", "size": 6118, "ext": "py", "lang": "Python", "max_stars_repo_path": "smarty/atomtyper.py", "max_stars_repo_name": "openforcefield/smarty", "max_stars_repo_head_hexsha": "882d54b6d6d0fada748c71789964b07be2210a6a", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""Implements hashes that are (hopefully) stable across sessions.
Note: I have found a hash instability in my implementation,
which produces different hashes for dicts that were deepcopied
from each other... which is VERY problematic. The hashes are stable
across restarts, which is doubly strange.
Therefore, we now u... | {"hexsha": "238190859b733d435311e80dbbb4bb7db0bb8b89", "size": 1893, "ext": "py", "lang": "Python", "max_stars_repo_path": "cmlkit/engine/hashing.py", "max_stars_repo_name": "sirmarcel/cmlk", "max_stars_repo_head_hexsha": "e099bf3e255b60675e8e1b3ad29db750dbd6faf3", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
The water of life, commonly known as whiskey, comes in many delicious and quickly inebriating forms. The most well known whiskeys are Bourbon, Irish whiskey and Scotch, there are many other varieties that are just as great giving the whiskey lover a reason to keep on drinking to try them all. Whiskey is an all natura... | {"hexsha": "dde4354e27954de08d03483bd1142c214c82bc3e", "size": 1418, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Whiskey.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import Data.So
fromString : String -> Either String Nat
fromString = Left
fromInteger : (x : Integer) -> (0 _ : So (x >= 0)) => Either String Nat
fromInteger x = Right $ integerToNat x
x : List $ Either String Nat
x = ["x", 1, 2, "y"]
| {"hexsha": "6b4bc8fe21027ffd31f660ec140bad206712194a", "size": 238, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "StringOrNat.idr", "max_stars_repo_name": "buzden/idris-playground", "max_stars_repo_head_hexsha": "414729c5dbd665fdb05cdfe5318f972fb277a726", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_coun... |
subroutine foo()
EXTERNAL SIN, COS
end subroutine
| {"hexsha": "96e7e85fef4363f47bee92eadfc9e172be4e297d", "size": 53, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/CompileTests/Fortran_tests/test2007_21.f90", "max_stars_repo_name": "maurizioabba/rose", "max_stars_repo_head_hexsha": "7597292cf14da292bdb9a4ef573001b6c5b9b6c0", "max_stars_repo_licenses": ... |
@testset "AlgAssOrd" begin
include("AlgAssOrd/CSAMaxOrd.jl")
include("AlgAssOrd/PicardGroup.jl")
include("AlgAssOrd/LocallyFreeClassGroup.jl")
end
| {"hexsha": "929e9f8f0679951fff1b9bc963ad2e1144fdf283", "size": 153, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/AlgAssOrd.jl", "max_stars_repo_name": "edgarcosta/Hecke.jl", "max_stars_repo_head_hexsha": "3ba4c63908eaa256150a055491a6387a45b081ec", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_co... |
from scipy import signal
import numpy as np
import matplotlib.pyplot as plt
class wtDataset:
"""
The element of the class constructor are
-data: time series to analyse
-t: time array associated to the time series
-s (optional): : scales to use for the wavelet transform and the scalogram; if None (by default... | {"hexsha": "0e7e6e9d74aeef86426bb26d7ec02b972794679c", "size": 11398, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyLib/waveletTools.py", "max_stars_repo_name": "mjsauvinen/P4US", "max_stars_repo_head_hexsha": "ba7bbc77a6e482f612ba5aa5f021a41fcbb23345", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
export simdir, simid, @run, @runsync, @rerun, @rerunsync, in_simulation_mode, @SimulationEnvironment
const ENV_SIM_FOLDER = "SIMULATION_FOLDER"
const ENV_SIM_ID = "SIMULATION_ID"
abstract type AbstractSimulationEnvironment end
struct DefaultSimulation <: AbstractSimulationEnvironment end
macro SimulationEnvironmen... | {"hexsha": "eb57b1c5673a3fe4d9397c6f3b4b639c334f0560", "size": 6304, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Simulation.jl", "max_stars_repo_name": "sebastianpech/DrWatsonSim.jl", "max_stars_repo_head_hexsha": "f81517c90a44a22b21d1d8cf65aa6da371093165", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from sklearn.preprocessing import MinMaxScaler
import numpy as np
def normalize(df, usecols, rol, window):
'''Normalize the values ina given dataframe removes nan window from the df'''
scalar = MinMaxScaler()
for col in df.columns.values:
if col not in usecols:
df = df.drop([col], 1)
... | {"hexsha": "e41330a894ae3012358a155ad4150b0d33635a60", "size": 1489, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project/deepstocks/preprocess.py", "max_stars_repo_name": "rtbins/code_alchemist", "max_stars_repo_head_hexsha": "c1475976df1b234116a77afb6dbc6cf82d0789c1", "max_stars_repo_licenses": ["MIT"], "ma... |
"""Conversion of units
"""
import numpy as np
def convert_kwh_gwh(kwh):
""""Conversion of MW to GWh
Input
-----
kwh : float
Kilowatthours
Return
------
gwh : float
Gigawatthours
"""
gwh = kwh * 0.000001
return gwh
def convert_mw_gwh(megawatt, number_of_hours)... | {"hexsha": "48df61676ba097dfe7143bf1374bbff9d9f8f80d", "size": 2698, "ext": "py", "lang": "Python", "max_stars_repo_path": "energy_demand/basic/unit_conversions.py", "max_stars_repo_name": "willu47/energy_demand", "max_stars_repo_head_hexsha": "59a2712f353f47e3dc237479cc6cc46666b7d0f1", "max_stars_repo_licenses": ["MIT... |
from PyQt5 import QtWidgets, QtGui
from PyQt5.QtWidgets import QTableWidgetItem
import UI
import itertools
from sympy import *
setA = set()
setB = set()
setC = set()
setU = set()
setcompA = set()
setcompB = set()
setcompC = set()
quitar = ['A', 'B', 'C', '=', '{', '}', 'U']
dic = {"A": setA, "B": setB, "C": setC, "U... | {"hexsha": "6c1693fca4dc7e4162be763fade1981d61f05f22", "size": 17702, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "nanyssalazar/Calculadora-Binaria", "max_stars_repo_head_hexsha": "f84e801723e894436f8e71d19b40d582c789dabf", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma eval_quot_fm_ignore: fixes A:: fm shows "\<lbrakk>\<guillemotleft>A\<guillemotright>\<rbrakk>e = \<lbrakk>\<guillemotleft>A\<guillemotright>\<rbrakk>e'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<guillemotleft>A\<guillemotright>\<rbrakk>e = \<lbrakk>\<guillemotleft>A\<guillemotright>... | {"llama_tokens": 164, "file": "Incompleteness_Coding", "length": 1} |
/*=============================================================================
Copyright (c) 2001-2011 Joel de Guzman
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)
=========================================... | {"hexsha": "c8087fac60159cd6d50b6b5395a328a6472484d6", "size": 18911, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/external/boost/boost_1_68_0/libs/spirit/example/qi/compiler_tutorial/conjure1/compiler.cpp", "max_stars_repo_name": "Bpowers4/turicreate", "max_stars_repo_head_hexsha": "73dad213cc1c4f74337b905... |
"""Test methods for testing the schemagen package (specifically,
the SchemaGenerator class).
Typical usage example:
python -m unittest
or, to run a single test:
python -m unittest -k test__build_schema
"""
import unittest
import pathlib
import logging
import copy
import os
import pandas as pd
import numpy a... | {"hexsha": "90e34a12e8b9e4a3553e847fbec09a27e4f92d6a", "size": 12680, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_schemagen.py", "max_stars_repo_name": "hd23408/nist-schemagen", "max_stars_repo_head_hexsha": "258e453d6f3bdc763e48e2c32668c932f7ffcd40", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma [iff]:
"length (tr_ss_f (map_of (zip (map (case_vd (\<lambda>cl. x_var)) vds) (map x_var vars'))(x_this \<mapsto> x')) ss') = length ss'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length (tr_ss_f (map_of (zip (map (case_vd (\<lambda>cl. x_var)) vds) (map x_var vars'))(x_this \<mapsto> x')) s... | {"llama_tokens": 159, "file": "LightweightJava_Lightweight_Java_Proof", "length": 1} |
# -*- coding: utf8 -*-
# board
# helper class for cuatro
# Alfredo Martin 2021
version = 'wscreenpos.v.1.0.0'
import numpy as np
class ScreenPos:
"""the instance of this class generates a 2d coordinates array from a 3d coordinates array given the position
of the camera, its angle and the position of the pro... | {"hexsha": "81533c5433a8e681640ceeed6dbe33393ec692f7", "size": 3570, "ext": "py", "lang": "Python", "max_stars_repo_path": "classes/screenpos.py", "max_stars_repo_name": "jamflcjamflc/cuatro", "max_stars_repo_head_hexsha": "007fabf1f75f87b3631966a10923ddccfe9d56af", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
#include <boost/atomic.hpp>
#include <boost/thread/mutex.hpp>
#include <stdlib.h>
namespace ilrd
{
using boost::atomic;
template<typename T>
class Singleton
{
public:
static T* instance();
static void KillInstance(void);
private:
//constructor && destructor are generated
static boost::atomic<T*> m_instance;
... | {"hexsha": "63d9ce96c90ea1f537f1d6fcf7f02a5c8e677541", "size": 1204, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "DesignPatterns/Singletone/singletone.hpp", "max_stars_repo_name": "zrora/DistributedNAS", "max_stars_repo_head_hexsha": "f750e919eb9159b2406180a21aeb8018a3a432a0", "max_stars_repo_licenses": ["MIT"]... |
from __future__ import absolute_import
import argparse
import json
from . import abstract_models
from . import layers
from classification import utility
from classification.objectives import (
FishingLocalizationObjectiveFishingTime, TrainNetInfo)
import logging
import math
import numpy as np
import os
import tens... | {"hexsha": "0f43615e0f44ae688b53f4d41660b9bc72ae9962", "size": 3839, "ext": "py", "lang": "Python", "max_stars_repo_path": "classification/models/prod/fishing_detection_time.py", "max_stars_repo_name": "rechardchen123/Classification-and-regression-of-vessel-AIS-data", "max_stars_repo_head_hexsha": "e9d8c7a807cb6e495777... |
"""
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import os
import numpy as np
import io
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
from... | {"hexsha": "51154e0c2c1933ad16ab446b7c6037aa38ee6579", "size": 9490, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/vis_utils.py", "max_stars_repo_name": "SRDewan/c3dpo_nrsfm", "max_stars_repo_head_hexsha": "1677335f8119eb8ab04d5f915523719e4c05cea9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from icecube.icetray import OMKey
from icecube.simclasses import I3MapModuleKeyI3ExtraGeometryItemCylinder, I3ExtraGeometryItemCylinder
from icecube.dataclasses import I3Position, ModuleKey
from I3Tray import I3Units
import numpy as np
from os.path import expandvars
from_cable_shadow = expandvars("$I3_BUILD/ice-mod... | {"hexsha": "a6d7aa2dbeb0b62e780ca80c636e66e22d39cb3b", "size": 1654, "ext": "py", "lang": "Python", "max_stars_repo_path": "clsim/python/GetIceCubeCableShadow.py", "max_stars_repo_name": "hschwane/offline_production", "max_stars_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_stars_repo_licenses": [... |
# julia script to test PUMI interface
using PumiInterface
include("funcs2.jl")
apf.declareNames(); # declare global variable names
# apf.initilize mesh
dmg_name = "cube.dmg"
smb_name = "tet-mesh-1.smb"
#dmg_name = "apf.reorder_a.dmg"
#smb_name = "apf.reorder_a.smb"
#dmg_name = ".null"
#smb_name = ".smb"
downward_co... | {"hexsha": "47613345b0c10ed9b147605efe2dd664ff065528", "size": 6183, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Main_script2.jl", "max_stars_repo_name": "OptimalDesignLab/PumiInterface.jl", "max_stars_repo_head_hexsha": "ca2cfe4cdb35958921ffc3748387772db4406901", "max_stars_repo_licenses": ["MIT"], "max_... |
# new tracking table contains all history is needed except data type plus unique value
# also it is not clear what to do with INPUT_VALUE column - mostly it is the same as column
# however it can have grouping operations like sum/aver etc.
viz_history<-function(fileroot){
LM<-6
MH<-15
library(data.table)
... | {"hexsha": "fa1dee759341dc27d7d52bec28e10df7926de5d2", "size": 7162, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Recommendations/viz_recom.r", "max_stars_repo_name": "kunal0137/SEMOSS_Volume", "max_stars_repo_head_hexsha": "7870d743fa60dad3d6baaa1a61613ec2da3e141b", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/env python
# coding: utf-8
# **Author: Fitria Dwi Wulandari (wulan391@sci.ui.ac.id) - September 9, 2021.**
# # Data Analysis of COVID-19 in the World and ASEAN
# ### Data Loading
# In[1]:
# Import libraries
import numpy as np
import pandas as pd
pd.set_option("display.max_columns", None)
# In[2]:
#... | {"hexsha": "0a53fbbb48482385514ee8183e1a6d8a99a29f67", "size": 5145, "ext": "py", "lang": "Python", "max_stars_repo_path": "Data Analysis of COVID-19 in the World and ASEAN.py", "max_stars_repo_name": "fitria-dwi/Data-Analysis-of-COVID-19-in-the-World-and-ASEAN", "max_stars_repo_head_hexsha": "5b4ff44d48c031b0fd239eeb4... |
import numpy as np
classification_vector= np.array([-1,-1,-1,-1,-1,1,1,1,1,1])
data_vector= np.array([[0,0], [2,0],[3,0], [0,2],[2,2],[5,1],[5,2],[2,4],[4,4],[5,5]])
def quadratic_kernel(data_vector):
return np.array((1 + np.dot(data_vector, data_vector.T))**2)
def perceptron_quadratic_kernel(feature_matrix, la... | {"hexsha": "c8d35523a045f3013f4710bab1beb903b85de30e", "size": 896, "ext": "py", "lang": "Python", "max_stars_repo_path": "Machine-Learning-with-Python- From-LM-to-DL/Midterm/04_midterm_exercise.py", "max_stars_repo_name": "andresdelarosa1887/Public-Projects", "max_stars_repo_head_hexsha": "db8d8e0c0f5f0f7326346462fcdf... |
# Copyright (c) Gorilla-Lab. All rights reserved.
import math
import numpy as np
import scipy.ndimage as ndimage
import scipy.interpolate as interpolate
import transforms3d.euler as euler
def elastic(xyz, gran, mag):
"""Elastic distortion (from point group)
Args:
xyz (np.ndarray): input point cloud
... | {"hexsha": "e01c487c5baa0d85fa52d4bf0e2a6ead6ac05f56", "size": 2243, "ext": "py", "lang": "Python", "max_stars_repo_path": "sstnet/data/utils.py", "max_stars_repo_name": "xiaobaishu0097/SSTNet", "max_stars_repo_head_hexsha": "d9e94da5d359732789a1bdc06d89ff96433c86b8", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
-- by Tojans
-- to run this:
-- idris 99bottles.idr
-- > beerSong 1
-- > beerSong 99
-- > beerSong 100 -- throws
beerSong : Fin 100 -> String
beerSong x = verses x where
-- invoke this in the CLI using `bottlesOfBeer (the (Fin 10) 4)`
bottlesOfBeer : Fin n -> String
bottlesOfBeer fZ = "No more bottl... | {"hexsha": "61d45b1e4093aaf877f5a27e2649e286d0acc68e", "size": 815, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "katas/001/tojans/99bottles.idr", "max_stars_repo_name": "ToJans/idris101", "max_stars_repo_head_hexsha": "c1912a30161be6d9987c40ebe304c8ae7827367e", "max_stars_repo_licenses": ["Unlicense"], "max_s... |
"""
BiSpectral Representation Method - 1D
=================================================================
In this example, the BiSpectral Representation Method is used to generate stochastic processes from a prescribed Power
Spectrum and associated Bispectrum. This example illustrates how to use the BSRM class for ... | {"hexsha": "ad75a7d8dee2f28bde1e419ba1d5a3f652ee6146", "size": 2587, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/code/stochastic_processes/bispectral/bispectral_1d.py", "max_stars_repo_name": "SURGroup/UncertaintyQuantification", "max_stars_repo_head_hexsha": "a94c8db47d07134ea2b3b0a3ca53ca818532c3e6", ... |
## Firebreak week December 2021 - Causal Inference & Machine Learning
#
# Short tutorial for the DoWhy package (https://microsoft.github.io/dowhy/)
#
# This tutorial makes use of a pre-processed subset of the EST data (np_processed.csv),
# that contains a number of variables. The ones relevant for this example are... | {"hexsha": "0dbbbd282a5319eee77bbdcc8f95ef62664f8718", "size": 7858, "ext": "py", "lang": "Python", "max_stars_repo_path": "tutorials/nesta/est_dowhy_test.py", "max_stars_repo_name": "nestauk/causal_ml_learning", "max_stars_repo_head_hexsha": "76fb96d370ccbf4076ca3f3b4b07e2c471aa7261", "max_stars_repo_licenses": ["MIT"... |
using MinFEM
function parabolic(;T::Float64, tsteps::Int, theta=1.0)
mesh = unit_square(100)
boundary = union(mesh.Boundaries[1001].Nodes,
mesh.Boundaries[1002].Nodes,
mesh.Boundaries[1003].Nodes,
mesh.Boundaries[1004].Nodes)
L = asmLaplacian(mesh)
M =... | {"hexsha": "832a4ae464af1e667660faadd0351807afa41df2", "size": 1304, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/parabolic.jl", "max_stars_repo_name": "msiebenborn/MinFEM.jl", "max_stars_repo_head_hexsha": "f703c4c227d90ed0c327bc84ca5cd566119a3cd3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
module Constants
using OffsetArrays
export get_offset_constants, get_offset_axial_constants, init!, ConstantContext
mutable struct ConstantContext
nmax::Int64
bcof::Array{Float64,2}
fnr::Array{Float64,1}
monen::Array{Int64,1}
vwh_coef::Array{Float64,4}
vcc::Array{Float64,3}
fnm1::Array{Floa... | {"hexsha": "9d1a31ee6025712e81e928519ce0d876a80b9fec", "size": 4650, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/constants.jl", "max_stars_repo_name": "JuliaRemoteSensing/MSTM.jl", "max_stars_repo_head_hexsha": "fd030fbb20d3f9db8944b40477d4b30d6cfa6ed5", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
__author__ = 'Prateek'
import time
import math
import numpy as np
import copy
from decisiontree import DecisiontreeClassifier
from multiprocessing import Process, Queue
class BaggingClassifier():
'''
Bagging classifier is meta-algorithm that builds a number of estimators on bootstrapped(with replacement)
... | {"hexsha": "6e140767d68bf4e4d0a20ab04d4246a549adbfb8", "size": 7248, "ext": "py", "lang": "Python", "max_stars_repo_path": "ensemble.py", "max_stars_repo_name": "prateekbhat91/Decision-Tree", "max_stars_repo_head_hexsha": "c06566684de3fa20d5984de6af0e3780c11a5fda", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
From Test Require Import tactic.
Section FOFProblem.
Variable Universe : Set.
Variable UniverseElement : Universe.
Variable wd_ : Universe -> Universe -> Prop.
Variable col_ : Universe -> Universe -> Universe -> Prop.
Variable col_swap1_1 : (forall A B C : Universe, (col_ A B C -> col_ B A C)).
Variable col_swap2_... | {"author": "janicicpredrag", "repo": "Larus", "sha": "a095ca588fbb0e4a64a26d92946485bbf85e1e08", "save_path": "github-repos/coq/janicicpredrag-Larus", "path": "github-repos/coq/janicicpredrag-Larus/Larus-a095ca588fbb0e4a64a26d92946485bbf85e1e08/benchmarks/coq-problems/col-trans/col_trans_0206.v"} |
% ========================================================================
% Fast Multi-Scale Structural Patch Decomposition for Multi-Exposure Image Fusion, TIP,2020
% algorithm Version 1.0
% Copyright(c) 2020, Hui Li, Kede Ma, Yongwei Yong and Lei Zhang
% All Rights Reserved.
% -------------------------------------... | {"author": "thfylsty", "repo": "Classic-and-state-of-the-art-image-fusion-methods", "sha": "5d9457df396f1ea6921e1b9b3703995205940862", "save_path": "github-repos/MATLAB/thfylsty-Classic-and-state-of-the-art-image-fusion-methods", "path": "github-repos/MATLAB/thfylsty-Classic-and-state-of-the-art-image-fusion-methods/Cl... |
import unittest
import numpy as np
from rastervision.core.box import Box
from rastervision.data.label import SemanticSegmentationLabels
class TestSemanticSegmentationLabels(unittest.TestCase):
def setUp(self):
self.windows = [Box.make_square(0, 0, 10), Box.make_square(0, 10, 10)]
self.label_arr0... | {"hexsha": "fc40d1604a871c23eda5fa1abc4f0c6fb174bbc9", "size": 1686, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/data/label/test_semantic_segmentation_labels.py", "max_stars_repo_name": "carderne/raster-vision", "max_stars_repo_head_hexsha": "915fbcd3263d8f2193e65c2cd0eb53e050a47a01", "max_stars_repo_l... |
import h5py
import numpy as np
import random
WINDOW_SIZE = 100
def rescale_array(X):
X = X / 20
X = np.clip(X, -5, 5)
return X
def aug_X(X):
scale = 1 + np.random.uniform(-0.1, 0.1)
offset = np.random.uniform(-0.1, 0.1)
noise = np.random.normal(scale=0.05, size=X.shape)
X = scale * X + o... | {"hexsha": "150120347733047ec85c20e061c93edd032699cb", "size": 1107, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/utils.py", "max_stars_repo_name": "CVxTz/EEG_classification", "max_stars_repo_head_hexsha": "f65060d6adb8de39afeb63b41207e864e3090ced", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
#-----------------------------------------------------------------------------------------------------
# Tensor Basis
#-----------------------------------------------------------------------------------------------------
"""
TensorBasis
Basis without any symmetries.
Properties:
-----------
- dgt : Vector{Int}, Di... | {"hexsha": "7d183bd6aab0ce797199acada585b244786bde92", "size": 2543, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/basis/TensorBasis.jl", "max_stars_repo_name": "jayren3996/ExactDiagonalization.jl", "max_stars_repo_head_hexsha": "5022d5868f9f5c9604577b1d217b5a51a49bafe3", "max_stars_repo_licenses": ["MIT"],... |
struct DayAheadIndices <: AbstractModelIndices
hours::Vector{Int}
plants::Vector{Plant}
segments::Vector{Int}
bids::Vector{Int}
blockbids::Vector{Int}
blocks::Vector{Int}
hours_per_block::Vector{Vector{Int}}
end
plants(indices::DayAheadIndices) = indices.plants
function HydroModels.modelind... | {"hexsha": "a8c2c2e820d9dffd3e14a76872de398f8415c4ce", "size": 1156, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/models/day-ahead/indices.jl", "max_stars_repo_name": "martinbiel/HydroModels.jl", "max_stars_repo_head_hexsha": "dee21e849922aa4938394ba464423762fd1e578f", "max_stars_repo_licenses": ["MIT"], "... |
# \MODULE\-------------------------------------------------------------------------
#
# CONTENTS : BumbleBee
#
# DESCRIPTION : Nanopore Basecalling
#
# RESTRICTIONS : none
#
# REQUIRES : none
#
# ---------------------------------------------------------------------------------
# Copyright 2021 Pay Gies... | {"hexsha": "db3a68f419afbd10a21a68cb2774cba003652a8d", "size": 10661, "ext": "py", "lang": "Python", "max_stars_repo_path": "bumblebee/cli/modcall.py", "max_stars_repo_name": "giesselmann/bumblebee", "max_stars_repo_head_hexsha": "6c4034d1576d3ce4a392bdaebe30968688892792", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
function sift!(Eav, decomp, d_osf, nsifts=5)
N = length(decomp)
e1 = zeros(N)
e2 = zeros(N)
avg = zeros(N)
w = max(div(d_osf-1, 2), 3)
if iseven(w)
w += 1
end
for j in 1:nsifts
stream_minmax(e1, e2, decomp, d_osf)
e1 .= moving_average(e1, d_osf)
... | {"hexsha": "0b16e1079fbd11f929700c217c2ae52420c312dc", "size": 1087, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/sEMD.jl", "max_stars_repo_name": "jarrison/EMD.jl", "max_stars_repo_head_hexsha": "b6c6e07602f84f010fd0745ab1a4ada62fe28fd5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_star... |
"""
Created May 13, 2016
Observation object for calculating satellite and star positions using SkyField.
@author: EP-Guy
"""
import numpy as np
import pandas as pd
from skyfield.api import load
class Observation:
"""Observation object for times and positions of an observer.
Observer is a tuple of strings... | {"hexsha": "82eea6fe5a1f34d7fd3f3ed6b282de251a21e0ed", "size": 1116, "ext": "py", "lang": "Python", "max_stars_repo_path": "vispe/Observation.py", "max_stars_repo_name": "EP-Guy/VisPe", "max_stars_repo_head_hexsha": "dd699775d032e2e266fda99ca0251504bc4aa9c0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from __future__ import print_function
import os
import math
from numpy.random import rand
from matplotlib import cm
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
from matplotlib import animation
from functions import ackley_function, griewank, schaeffer
from mp... | {"hexsha": "17f6288898ad9b57074b7e61a0a82e2cc1240fa2", "size": 4115, "ext": "py", "lang": "Python", "max_stars_repo_path": "PSO.py", "max_stars_repo_name": "EmmanuelleB985/Scientific-Computing", "max_stars_repo_head_hexsha": "796a01611869fc49bef4e5ec6c6d6e98f1f44b4c", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# installing dependencies for module
pip install pandas
pip install numpy
pip install matplotlib
pip install seaborn
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
| {"hexsha": "ac13896e51dcc4ae9c58d99eb9808cc29b53902f", "size": 212, "ext": "py", "lang": "Python", "max_stars_repo_path": "lamblibs.py", "max_stars_repo_name": "veritaem/LambdataChrisSeiler", "max_stars_repo_head_hexsha": "67a00402b743f508caf6de81152dbecd0d555b72", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
act <- c(0, 1, 1, 0, 0)
pred <- c(0.12, 0.45, 0.9, 0.3, 0.4)
## Test correctness ------------------------------------------------------------
## check eps and handling of absolute zero and one probabilities
expect_equal(mtr_mean_log_loss(act, act), 0)
## Metrics::logLoss(act, pred)
## 0.3798404
expect_equal(
m... | {"hexsha": "099bdf00f9ebf2641c104667e6cf843c44777eca", "size": 881, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/tinytest/test-logloss.r", "max_stars_repo_name": "maiing/metrics", "max_stars_repo_head_hexsha": "ae4e4cbe5b025c53f2fbf552a46f335aa34f687f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
// Copyright Gavin Band 2008 - 2012.
// 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)
#ifndef QCTOOL_SNP_SUMMARY_COMPONENT_BED4_ANNOTATION_HPP
#define QCTOOL_SNP_SUMMARY_COMPONENT_BED4_ANNOT... | {"hexsha": "43607ce406ee1251b50ceceff758973c3cac1a53", "size": 1844, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "components/SNPSummaryComponent/include/components/SNPSummaryComponent/Bed4Annotation.hpp", "max_stars_repo_name": "CreRecombinase/qctool", "max_stars_repo_head_hexsha": "6dad3a15c461177bf6940ba7b991... |
import numpy
from sympy import Rational as frac
from sympy import pi, sqrt
from ..helpers import article, fsd, pm, pm_array, pm_array0, untangle
from ._helpers import E2r2Scheme
_citation = article(
authors=["A.H. Stroud", "D. Secrest"],
title="Approximate integration formulas for certain spherically symmetri... | {"hexsha": "a89ca146d70acdd10bf85ce60965b05363018193", "size": 1263, "ext": "py", "lang": "Python", "max_stars_repo_path": "quadpy/e2r2/_stroud_secrest.py", "max_stars_repo_name": "whzup/quadpy", "max_stars_repo_head_hexsha": "ca8bd2f9c5a4ae30dc85d8fb79217602bd42525e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# code adapted from https://github.com/lingxiaoli94/SPFN/blob/master/spfn/lib/dataset.py
import sys
import os
import re
import pickle
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
import torch
import numpy as np
from torch.utils import data
import h5py
import random
import pandas
fro... | {"hexsha": "88e095d450608c7f80746969798cbc023e511f37", "size": 8385, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/ANSI_dataset.py", "max_stars_repo_name": "Meowuu7/AutoGPart", "max_stars_repo_head_hexsha": "5d9f18f7e88426b672812aa4f134799124196126", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# syntax: proto3
using ProtoBuf
import ProtoBuf.meta
mutable struct HloInstructionProto_SliceDimensions <: ProtoType
start::Int64
limit::Int64
stride::Int64
HloInstructionProto_SliceDimensions(; kwargs...) = (o=new(); fillunset(o); isempty(kwargs) || ProtoBuf._protobuild(o, kwargs); o)
end #mutable str... | {"hexsha": "13781ecd32c7cea22588fbe40631775b95a62147", "size": 13370, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/gen/hlo_pb.jl", "max_stars_repo_name": "Keno/XLA.jl", "max_stars_repo_head_hexsha": "6e0b15cd3b334ce40ffce1bae9bdc1a817f83c13", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 209, "max... |
***********************************************************************
SUBROUTINE FPOST(ITYP,IFILEN,ITFILM,UE,MSHOW,DFWX,DFWY,
* ITOPT,COFN)
************************************************************************
* Purpose: - performs the nonsteady postprocess:
* ... | {"hexsha": "9439c75265be35423d295fcfd0721c5247cc3aca", "size": 27099, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "area51/cc2d_movbc_structured_2/src/postprocessing/fpost.f", "max_stars_repo_name": "tudo-math-ls3/FeatFlow2", "max_stars_repo_head_hexsha": "56159aff28f161aca513bc7c5e2014a2d11ff1b3", "max_stars_... |
import data.list.basic
variable {α : Type*}
open list
example (xs : list ℕ) :
reverse (xs ++ [1, 2, 3]) = [3, 2, 1] ++ reverse xs :=
by simp
example (xs ys : list α) :
length (reverse (xs ++ ys)) = length xs + length ys :=
by simp [add_comm]
variables (x y z : ℕ) (p : ℕ → Prop)
example (h : p ((x + 0) * (0 + ... | {"author": "agryman", "repo": "theorem-proving-in-lean", "sha": "cf5a3a19d0d9d9c0a4f178f79e9b0fa67c5cddb9", "save_path": "github-repos/lean/agryman-theorem-proving-in-lean", "path": "github-repos/lean/agryman-theorem-proving-in-lean/theorem-proving-in-lean-cf5a3a19d0d9d9c0a4f178f79e9b0fa67c5cddb9/src/05-Tactics/example... |
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