text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def get_activation(name, inplace=True):
if name == 'lrelu': return nn.LeakyReLU(0.2, inplace=inplace)
if name == 'relu': return nn.ReLU(inplace)
if name == 'tanh': return nn.Tanh()
if name == 'sigmoid': return nn.Sig... | {"hexsha": "9d11afbc7c213973b5f6fcd8e6512bfda869ca3b", "size": 7210, "ext": "py", "lang": "Python", "max_stars_repo_path": "implementations/EigenGAN/model.py", "max_stars_repo_name": "STomoya/animeface", "max_stars_repo_head_hexsha": "37b3cd26097d7874559d4c152e41e5712b7a1a42", "max_stars_repo_licenses": ["MIT"], "max_s... |
import sys
import numpy as np
from itertools import islice
import matplotlib.pyplot as plt
from itertools import izip
plt.rcParams.update({'font.size': 60})
plt.rcParams.update({'axes.linewidth' : 3})
plt.rcParams.update({'lines.linewidth' : 3})
plt.rcParams.update({'lines.markersize' : 10})
def get_K(n):
return ... | {"hexsha": "9d6fabbbe00075c34dddb86081e72ba66da11478", "size": 9274, "ext": "py", "lang": "Python", "max_stars_repo_path": "ExperimentsOld/Results/BEARBh/process_ingestion.py", "max_stars_repo_name": "rdfostrich/cobra", "max_stars_repo_head_hexsha": "b65ec1aa7b10e990a3b40d86636050377ff2d2d6", "max_stars_repo_licenses":... |
[STATEMENT]
lemma conf_heap_ops_mono:
assumes "P,h \<turnstile> v :\<le> T"
shows conf_allocate_mono: "(h', a) \<in> allocate h hT \<Longrightarrow> P,h' \<turnstile> v :\<le> T"
and conf_heap_write_mono: "heap_write h a al v' h' \<Longrightarrow> P,h' \<turnstile> v :\<le> T"
[PROOF STATE]
proof (prove)
goal (1 ... | {"llama_tokens": 357, "file": "JinjaThreads_Common_Conform", "length": 2} |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Conversion functions for test statistic <-> significance <-> probability.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
# TODO: make all the other methods private?
# need to transfer the info ... | {"hexsha": "a4eb7319891f59d782a0105e3167428bc0ceeee3", "size": 10902, "ext": "py", "lang": "Python", "max_stars_repo_path": "gammapy/stats/significance.py", "max_stars_repo_name": "gabemery/gammapy", "max_stars_repo_head_hexsha": "99e5c5d38e4920dddd7bca41fb1539ccda8bea2d", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
import nibabel
import numpy
import spire
from .. import entrypoint
from ..cli import *
class ASLBOLDToASL(spire.TaskFactory):
""" Separate ASL signal from ASL-BOLD based on Fourier analysis.
References:
- Mapping resting-state functional connectivity using perfusion MRI.
... | {"hexsha": "6a759d3b6add40cac508f383ada49754c22e6c4f", "size": 2403, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/erwin/cbf/asl_bold_to_asl.py", "max_stars_repo_name": "lamyj/erwin", "max_stars_repo_head_hexsha": "a2a7c945827a54c1e89dbedb31c82e34363bf7d1", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
import pytest
from docarray import DocumentArray, Document
from docarray.math import ndarray
@pytest.mark.parametrize(
'storage, config',
[
('memory', None),
('weaviate', {'n_dim': 32}),
('annlite', {'n_dim': 32}),
('qdrant', {'n_dim': 32}),
('elasti... | {"hexsha": "f68dafb63b753d05a4b2eef86e049da78e4d727f", "size": 6466, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/array/mixins/test_find.py", "max_stars_repo_name": "jina-ai/docarray", "max_stars_repo_head_hexsha": "24301254d95fd755fb6e821df6bc2c6b977b138b", "max_stars_repo_licenses": ["Apache-2.0"... |
import tensorflow as tf
import tensorflow_probability as tfp
print(f"Tensorflow API Version: {tf.version.VERSION}")
print(f"Tensorflow API Compiler Version: {tf.version.COMPILER_VERSION}")
import numpy as np
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import os
import da... | {"hexsha": "9c823e8975089565313932b4049b4e192bf0b1de", "size": 13291, "ext": "py", "lang": "Python", "max_stars_repo_path": "NNmodels/Model.py", "max_stars_repo_name": "peiyan1234/PA_radiomics_research", "max_stars_repo_head_hexsha": "bf127ad791c10f2abd40942970f052472c931006", "max_stars_repo_licenses": ["Apache-2.0"],... |
"""
RDKit IO.
"""
import numpy as np
try:
from rdkit import Chem
import rdkit
except ModuleNotFoundError as e:
print("Module rdkit was not found, it must be installed to use rdkit IO")
raise
from .. import TinyGraph
def from_rdkit_mol(mol, use_charge=False, use_chiral=False,
u... | {"hexsha": "fdf53f05a022fc9df32c8db20ab407d73fbc0e64", "size": 4492, "ext": "py", "lang": "Python", "max_stars_repo_path": "tinygraph/io/rdkit.py", "max_stars_repo_name": "thejonaslab/tinygraph", "max_stars_repo_head_hexsha": "f1638168ed084dbb0515cafbf69282b38c4b5810", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
import numpy as np
from pytorch_grad_cam.base_cam import BaseCAM
# https://arxiv.org/abs/1710.11063
class GradCAMPlusPlus(BaseCAM):
def __init__(self, model, target_layers, use_cuda=False,
reshape_transform=None):
super(GradCAMPlusPlus, self).__init__(model, target_layers, use_c... | {"hexsha": "0afd7abbf6694dc64d4e3936081135f9e71a2411", "size": 1262, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch_grad_cam/grad_cam_plusplus.py", "max_stars_repo_name": "hddlovefxx/-pytorch-grad-cam", "max_stars_repo_head_hexsha": "6eb5bd3a41bcd661c6acc53853258282286768fe", "max_stars_repo_licenses": ... |
(* Author: Nan Jiang *)
section \<open>Soundness and completeness\<close>
theory Dom_Kildall_Correct
imports Dom_Kildall_Property
begin
context dom_sl
begin
lemma entry_dominate_dom:
assumes "i \<in> set (g_V G)"
and "dominate i 0"
shows "dom i 0"
using assms
proof-
from assms(1) entry0_dominates_... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/Dominance_CHK... |
from .handler import function_handler
import yaml
import pytest
import pandas as pd
import numpy as np
from packaging import version
def merge_setup():
# read in file infos
with open("tests/test_yamls/test_sql_merge.yml", "r") as stream:
file_infos = yaml.safe_load(stream)
merge_infos = []
fo... | {"hexsha": "8cf369d5e7473cc027b4771997310601bbc79acd", "size": 1564, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_merge.py", "max_stars_repo_name": "rainyjonne/airbnb-pipeline", "max_stars_repo_head_hexsha": "5e07e26519ac86dc5a58cc4b34710818edd241ae", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from sklearn.cluster import AgglomerativeClustering as skAgglomerative
import numpy as np
from .base import Clustering
from ..similarity.pairwise import pairwise_similarity
class AgglomerativeClustering(Clustering):
"""Hierarchical Agglomerative Clustering.
Parameters
----------
n_clusters : int
... | {"hexsha": "624a799d851e06d9361233446dd4eb1d0c43c6ea", "size": 1496, "ext": "py", "lang": "Python", "max_stars_repo_path": "trajminer/clustering/agglomerative.py", "max_stars_repo_name": "ybj94/trajminer", "max_stars_repo_head_hexsha": "7355344be8fe763ba2583b6f508fefc3290c9849", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import torch
import drr_projector_function
from torch import nn
from torch.autograd import Function
class DRRProject(Function):
@staticmethod
def forward(ctx, volume, detector_shape, ray_mat, source, step_size, voxel_size, interp):
ctx.save_for_backward(ray_mat, source, step_size, v... | {"hexsha": "30c93b2cb703728ad59de56634910165ebf129fc", "size": 21043, "ext": "py", "lang": "Python", "max_stars_repo_path": "xraysyn/networks/drr_projector_new.py", "max_stars_repo_name": "cpeng93/XraySyn", "max_stars_repo_head_hexsha": "7309b2fbc28bceddbc80a03c2279540da391782a", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma none_MT_rulessubset[rule_format]:
"none_MT_rules C a \<longrightarrow> set b \<subseteq> set a \<longrightarrow> none_MT_rules C b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. none_MT_rules C a \<longrightarrow> set b \<subseteq> set a \<longrightarrow> none_MT_rules C b
[PROOF STEP]
by (indu... | {"llama_tokens": 130, "file": "UPF_Firewall_FWNormalisation_NormalisationIntegerPortProof", "length": 1} |
# approval ratings helper scripts
from nltk.tokenize import word_tokenize, sent_tokenize
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as datetime
xls_file = pd.ExcelFile('data/approval_ratings.xls')
sheet_list = []
for sheet in xls_file.sheet_names:
... | {"hexsha": "e7ccba5c1d1b08eb75d63fbb56b82df9d3e44a6c", "size": 1222, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/approval.py", "max_stars_repo_name": "ratulesrar3/sotu-approval-analysis", "max_stars_repo_head_hexsha": "4e4575e81795d09ce951ae289eb30158392ef37d", "max_stars_repo_licenses": ["MIT"], "ma... |
Our work broadly falls under the category of semi-supervised sequence learning for natural language.
There are four recent trends to pay attention to: deep contextual embeddings, pre-trained language models, a reduction in task-specific architectures, and task- and domain-specific fine-tuning.
Because of their ability... | {"hexsha": "6e7b89e2799f6fec8af59caf77524ef8b959ae4a", "size": 4883, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/literature_review.tex", "max_stars_repo_name": "elinsky/fiqa", "max_stars_repo_head_hexsha": "0caa0fdb3a684c06bea2ba97d96b928d737c8146", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from math import cos, sin, sqrt, atan2
from triangle import triangulate
from Meshes.quaternion import quat_from_axis, quat_mult... | {"hexsha": "e3ed0e883152035ee8f2d334a171698e32c1f69f", "size": 11232, "ext": "py", "lang": "Python", "max_stars_repo_path": "Meshes/sphere.py", "max_stars_repo_name": "patriciogonzalezvivo/Meshes", "max_stars_repo_head_hexsha": "8efdaf23d03e4608d662d234d7ae84a6f3cf69e3", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
# -*- coding:utf-8 -*-
# author:平手友梨奈ii
# e-mail:1353593259@qq.com
# datetime:1993/12/01
# filename:configs.py
# software: PyCharm
import numpy as np
import tensorflow as tf
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.call... | {"hexsha": "463e69bb8fdf246adb737b3ab008b90089c59786", "size": 19743, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "HirataYurina/yoloV4-keras-techi", "max_stars_repo_head_hexsha": "825be030b1bf13c6d16e54f2f7741ffc736302da", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# multivariate.py
import numpy as np
from numba import njit, jit, prange
from scipy import stats
from tqdm import tqdm
import warnings
import matplotlib
import matplotlib.pyplot as plt
from ts_analysis.dataframes import rdm
from ts_analysis.utilities import aux
from ts_analysis.utilities import matop
from ts_analysis... | {"hexsha": "53babcbb8017fd5b00a9c7086eafe5559e67df41", "size": 17163, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ts_analysis/imaging/multivariate.py", "max_stars_repo_name": "tedchengf/ts_analysis", "max_stars_repo_head_hexsha": "b1ed127b5392d177c51bd136107aa0fec4a1759c", "max_stars_repo_licenses": ["MI... |
using DataStructures: OrderedDict
abstract type Tabulation{V} end
value(t::Tabulation) = getfield(t, :value)
Base.adjoint(t::Tabulation) = value(t)
Base.getproperty(t::Tabulation, k::Symbol) = value(t)[k]
Base.getindex(t::Tabulation, k::Symbol) = getproperty(t, k)
Base.iterate(t::Tabulation) = iterate(value(t))
Base.... | {"hexsha": "ddb1beb89449dcc3692a13d5be3c441cdbbd3076", "size": 2233, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/state/tabulate.jl", "max_stars_repo_name": "cropbox/Cropbox.jl", "max_stars_repo_head_hexsha": "072c3c2664688fbe9a9dae41833ba2ce6a559f67", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
function d = degree(varargin)
F = varargin{1};
F = flatten(F);
d = subdegree(F.clauses);
function d = subdegree(clauses)
d = -inf;
for i = 1:length(clauses)
d = max(d,degree(sdpvar(clauses{i}.data)));
end | {"author": "yalmip", "repo": "YALMIP", "sha": "f6d5a6d4222a4d722de30bffb43cae4b3e13b860", "save_path": "github-repos/MATLAB/yalmip-YALMIP", "path": "github-repos/MATLAB/yalmip-YALMIP/YALMIP-f6d5a6d4222a4d722de30bffb43cae4b3e13b860/extras/@lmi/degree.m"} |
import numba
import numpy as np
# error: Untyped decorator makes function "is_monotonic_increasing" untyped
@numba.jit( # type: ignore[misc]
numba.boolean(numba.int64[:]), nopython=True, nogil=True, parallel=False
)
def is_monotonic_increasing(bounds: np.ndarray) -> bool:
"""Check if int64 values are monoton... | {"hexsha": "7c2e7636c7d81c052ebcbcb557f8e7e0789fb584", "size": 550, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/core/_numba/kernels/shared.py", "max_stars_repo_name": "IamJasonBian/pandas", "max_stars_repo_head_hexsha": "21024d5a8e05f611d0fef5ddf884ffa237643772", "max_stars_repo_licenses": ["BSD-3-Cla... |
ALL_TASK_OFFSETS[("linux-x86_64", v"1.6.1")] = Dict(
:END => 312,
:_isexception => 58,
:_state => 56,
:bufsz => 288,
:copy_stack => 296,
:ctx => 80,
:donenotify => 24,
:eh => 72,
:excstack => 64,
:gcstack => 304,
:logstate => 40,
:next => 0,
:prio => 62,
:queue =>... | {"hexsha": "adbe2774fca45cfaaef3f11cc09816cd6e776b23", "size": 489, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/memlayout/linux-x86_64-v1_6_1.jl", "max_stars_repo_name": "TuringLang/Libtask.jl", "max_stars_repo_head_hexsha": "7c404c4823eab2088df8aa05862b72b0f2638358", "max_stars_repo_licenses": ["MIT"], "... |
"""Unit tests for madis_io.py."""
import copy
import unittest
import numpy
import pandas
from gewittergefahr.gg_io import raw_wind_io
from gewittergefahr.gg_io import madis_io
from gewittergefahr.gg_utils import longitude_conversion as lng_conversion
COLUMN_NAME = raw_wind_io.TIME_COLUMN
COLUMN_NAME_ORIG = madis_io.T... | {"hexsha": "a76ff61fe8fbb285814374de6cdcca1709240d47", "size": 12946, "ext": "py", "lang": "Python", "max_stars_repo_path": "gewittergefahr/gg_io/madis_io_test.py", "max_stars_repo_name": "dopplerchase/GewitterGefahr", "max_stars_repo_head_hexsha": "4415b08dd64f37eba5b1b9e8cc5aa9af24f96593", "max_stars_repo_licenses": ... |
#!/usr/bin/env python2
###############################################################################
# ------------------------- Description ---------------------------------------
###############################################################################
# This script is designed to create a mask of stagnatio... | {"hexsha": "e0bfe32bd0dc2f5af846e397ed2b416a10b57143", "size": 8540, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/find_era_interim_stagnation_days.py", "max_stars_repo_name": "stevenjoelbrey/PMFutures", "max_stars_repo_head_hexsha": "8c6ba7576d6c3a1f0279ec8aff446478c495d184", "max_stars_repo_licenses":... |
"""
spaceprint() functions can draw a tree of AbstractDelayed objects in the terminal.
"""
module SpacePrint
using Dates
using DocStringExtensions
export spaceprint
function cornerchar(final::Bool)::String
if final
return "└"
end
return "├"
end
function fillerchar(final::Bool)::String
if fin... | {"hexsha": "92181a2af91426d81873e140ef716e717e96085d", "size": 2688, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SpacePrint.jl", "max_stars_repo_name": "lhnguyen-vn/TreeParzen.jl", "max_stars_repo_head_hexsha": "d6b4181a45167663e8844330220f0c62c715c75f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
"""
Valence, Coordination_number, Pi electron_num and c/r^2
representation (VCP) of atom in molecule, where c, r are
the column and row number of the corresponding element
in periodic table
VCP is designed for prediction of melting & boiling
points of molecule
"""
from openeye.oechem import *
import aqml.cheminfo.O... | {"hexsha": "d26db9d212f2f31eef12892d0f9d3bb8912af61e", "size": 7556, "ext": "py", "lang": "Python", "max_stars_repo_path": "coreml/cml/representation/vcp.py", "max_stars_repo_name": "binghuang2018/aqml", "max_stars_repo_head_hexsha": "4901f3bd85db968fb3fc7ab97fd443421909d89d", "max_stars_repo_licenses": ["MIT"], "max_s... |
using Statistics
@testset "penalties" begin
A = [-1 2; -3 4]
lambda = 1
# 100% L2:
alpha = 0
penalty = MLJFlux.Penalizer(lambda, alpha)
@test penalty(A) ≈ 1 + 4 + 9 + 16
# 100% L1:
alpha = 1
penalty = MLJFlux.Penalizer(lambda, alpha)
@test penalty(A) ≈ 1 + 2 + 3 + 4
# no... | {"hexsha": "bbc677205d69e26c4d56e5694712fb196d7d6d93", "size": 967, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/penalized_losses.jl", "max_stars_repo_name": "john-waczak/MLJFlux.jl", "max_stars_repo_head_hexsha": "56a4d03fe87b15fb7672131aa0612510a9573559", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import numpy.testing as npt
from .. import bs2051
from ..point_source import Triplet, VirtualNgon, StereoPanDownmix, PointSourcePanner, configure, AllocentricPanner
from ..geom import cart, azimuth, PolarPosition
from ..layout import Speaker
import pytest
def test_virtual():
positions = np.arra... | {"hexsha": "5393a7567ec7ee7dabba862ffde8347d57ee5930", "size": 17909, "ext": "py", "lang": "Python", "max_stars_repo_path": "ear/core/test/test_point_source.py", "max_stars_repo_name": "tomjnixon/ebu_adm_renderer", "max_stars_repo_head_hexsha": "625254a6430a4f6fc093bfd51802cfe8152dcb42", "max_stars_repo_licenses": ["BS... |
/*
* ResourceFactory.h
*
* Created on: 2012-06-10
* Author: leo
*/
#ifndef RESOURCEFACTORY_H_
#define RESOURCEFACTORY_H_
#include <fruitpunch/Resources/Resource.h>
#include <boost/shared_ptr.hpp>
#include <string>
#include <map>
namespace fp_core {
/**
* Allows clients to associate file extensions with ... | {"hexsha": "3b9f16e2be9b5cf8e6de95654d2c60ec3ad79d0e", "size": 2172, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "fp_core/src/main/include/fruitpunch/Resources/ResourceFactory.hpp", "max_stars_repo_name": "submain/fruitpunch", "max_stars_repo_head_hexsha": "31773128238830d3d335c1915877dc0db56836cd", "max_stars_... |
program main
!*****************************************************************************80
!
!! MAIN is the main program for FEM1D_HEAT_STEADY_PRB.
!
! Discussion:
!
! FEM1D_HEAT_STEADY_PRB tests the FEM1D_HEAT_STEADY library.
!
! Licensing:
!
! This code is distributed under the GNU LGPL license.
!
! Modi... | {"hexsha": "dcbae5fffe97c05028599271ec396c31023112fe", "size": 4153, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "fem1d_heat_steady/fem1d_heat_steady_prb.f90", "max_stars_repo_name": "mjasher/computation", "max_stars_repo_head_hexsha": "63d83e476af5c6da5361a6bc8a7692372931a220", "max_stars_repo_licenses": [... |
! { dg-do run }
! { dg-options "-std=gnu" }
! PR38439 I/O PD edit descriptor inconsistency
! Test case prepared by Jerry DeLisle <jvdelisle@gcc.gnu.org>
character(len=25) :: str
character(len=132) :: msg, line
str = '(1pd24.15e6)'
line = "initial string"
x = 555.25
write (line... | {"hexsha": "1d677509e37e5a722e518c3691a5bf7e89d0a8fa", "size": 1058, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/fmt_error_9.f", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_license... |
# -*- coding: utf-8 -*-
"""
computeKey
computes a simple beat histogram
Args:
afAudioData: array with floating point audio data.
f_s: sample rate
afWindow: FFT window of length iBlockLength (default: hann)
iBlockLength: internal block length (default: 4096 samples)
iHopLength: internal ... | {"hexsha": "187eaad437f8e6adc98002a1bf4411f8e6da5232", "size": 2143, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyACA/computeBeatHisto.py", "max_stars_repo_name": "ruohoruotsi/pyACA", "max_stars_repo_head_hexsha": "339e9395b65a217aa5965638af941b32d5c95454", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
function add!(n::NetworkP,extra::Extracellular)
extra.v=zeros(Float64,length(n.t))
coeffs=Array(Extra_coeffs,0)
for j=1:length(fieldnames(n.neur))-4
mycoeffs=add_extra(getfield(n.neur,j),extra)
append!(coeffs,mycoeffs)
end
add_extra_(n,extra,coeffs)
end
function add!(n::Netw... | {"hexsha": "5e45ef5e2bae66241bd896362f9903af5861f38a", "size": 3989, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/parallel.jl", "max_stars_repo_name": "paulmthompson/JNeuron", "max_stars_repo_head_hexsha": "d6a389506e27df1955ac59eb08376795d20bb1b6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
#!/usr/bin/env python
# coding: utf-8
#copyRight by heibanke
import numpy as np
from matplotlib import pyplot as plt
from scipy import interpolate
x = np.linspace(0, 2*np.pi+np.pi/4, 10)
y = np.sin(x)
x_new = np.linspace(0, 2*np.pi+np.pi/4, 100)
f_linear = interpolate.interp1d(x, y)
para_bspline = int... | {"hexsha": "9cec45f86e05ea6c07e09582086976584f837e10", "size": 591, "ext": "py", "lang": "Python", "max_stars_repo_path": "misc/numpy_ex2.py", "max_stars_repo_name": "dineshkumar2509/learning-python", "max_stars_repo_head_hexsha": "e8af11ff0b396da4c3f2cfe21d14131bae4b2adb", "max_stars_repo_licenses": ["MIT"], "max_star... |
Require Import
Coq.Strings.String
Coq.Vectors.Vector.
Require Import
Fiat.Common.SumType
Fiat.Common.BoundedLookup
Fiat.Common.ilist
Fiat.Computation
Fiat.QueryStructure.Specification.Representation.Notations
Fiat.QueryStructure.Specification.Representati... | {"author": "proofskiddie", "repo": "CoqStuff", "sha": "fc8ecdf8045bc835bb10b2e4791f041d82451b5d", "save_path": "github-repos/coq/proofskiddie-CoqStuff", "path": "github-repos/coq/proofskiddie-CoqStuff/CoqStuff-fc8ecdf8045bc835bb10b2e4791f041d82451b5d/idontevnkno/src/BinEncoders/Env/Examples/UDP_Packet.v"} |
program BasicImageTests
use RCImageBasic
use RCImageIO
use RCImagePrimitive
implicit none
type(rgbimage) :: animage
integer :: x, y
call alloc_img(animage, 200, 200)
call fill_img(animage, rgb(255,255,255))
call draw_line(animage, point(0,0), point(199,199), rgb(0,0,0))
do y=0,219,20
call ... | {"hexsha": "f9973d82d7a04f399e7863d3f579ecbbbf930f9e", "size": 562, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Task/Bitmap-Bresenhams-line-algorithm/Fortran/bitmap-bresenhams-line-algorithm-2.f", "max_stars_repo_name": "mullikine/RosettaCodeData", "max_stars_repo_head_hexsha": "4f0027c6ce83daa36118ee8b67915... |
__author__ = 'john'
from astropy import units as u
from astropy import constants as const
import math
class AstroPhysics:
sun_absolute_magnitude = 4.77
def __init__(self):
pass
def getParsecFromParalex(self, plx):
return 1 / plx
def getAbsoluteMagnitudeFromParalex(self, vMag, plx):... | {"hexsha": "da1990ed5da333a2d1bc766d70dc11d55ec71d6a", "size": 1617, "ext": "py", "lang": "Python", "max_stars_repo_path": "LightCurves/LightCurve/helpers/astrophysics.py", "max_stars_repo_name": "TohoMonster/LightCurves", "max_stars_repo_head_hexsha": "a941af9eb50ef4ebd06bbfb8028630244a85e783", "max_stars_repo_license... |
import cv2
from darkflow.net.build import TFNet
import numpy as np
import time
import tensorflow as tf
config = tf.ConfigProto(log_device_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
options = {
'model': 'cfg/yolov2-1c.cfg',
'load... | {"hexsha": "29407adb5cae3d379cb142206c99d917a86a4530", "size": 1378, "ext": "py", "lang": "Python", "max_stars_repo_path": "Computer vision/yolov2_custom_object_cam.py", "max_stars_repo_name": "zbs881314/Deep-Learning-and-Computer-Vision", "max_stars_repo_head_hexsha": "a5965ea1562d2adbba381d3e411ad2b0a7a14d30", "max_s... |
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_datasets as tfds
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from src.data import ... | {"hexsha": "336871871e8b4f3c1229f3edefdd87b3bd47e150", "size": 2802, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/embeddings.py", "max_stars_repo_name": "Aelvangunduz/phd_code", "max_stars_repo_head_hexsha": "b8dc7d8cfe647e791820519ff51f10d9b0f42845", "max_stars_repo_licenses": ["FTL"], "max_stars_... |
\subsection{The Aggregate Demand - Aggregate Supply (AD-AS) model}
| {"hexsha": "f172459cfd9ec378823564370e0cc05aa98d5e59", "size": 70, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/economics/neoKeynesian/03-03-AD-AS.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo_lice... |
import socketserver as ss
import socket
import json
import numpy as np
from display_util import print_error, print_info, print_notification, print_warning
def get_local_ip():
"""Finds the localhost ip address used for connecting to the LAN."""
hostname = socket.gethostname()
ip = socket.gethostbyname(hos... | {"hexsha": "c509e50367856acfdce7579dac12e256fb7aa163", "size": 1243, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyCharm/TCP_Socket_Chatroom/depth_video_streaming/web_socket_util.py", "max_stars_repo_name": "iggy12345/emerson_seed_object_detection", "max_stars_repo_head_hexsha": "121c6fe55fb4c903cb2c05f12077... |
[STATEMENT]
lemma foundation6:
"\<tau> \<Turnstile> P \<Longrightarrow> \<tau> \<Turnstile> \<delta> P"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<tau> \<Turnstile> P \<Longrightarrow> \<tau> \<Turnstile> \<delta> P
[PROOF STEP]
by(simp add: OclNot_def OclValid_def true_def false_def defined_def
... | {"llama_tokens": 152, "file": "Featherweight_OCL_UML_Logic", "length": 1} |
#赋值
import torch
import numpy as np
a = torch.arange(24).reshape(2,3,4)
"""1、basic， 可以赋值， 可以广播赋值 """
a[1,2] = 100 # 下一维度自动广播
a[1, 2, 3] = 1000 # 没有广播
print("basic\n", a)
print("-"*20)
"""2、选择多个， 这里的0， 1表示该维度以及下一维度是否选择"""
b = torch.from_numpy(np.array([[0,0,0],
[0,0,0]]))
b = b.type(torch... | {"hexsha": "387530e5f4a77bc69209999adc1a4906ce798624", "size": 767, "ext": "py", "lang": "Python", "max_stars_repo_path": "script_python/learn/how_to_assign_an_array.py", "max_stars_repo_name": "yunshangyue71/mycodes", "max_stars_repo_head_hexsha": "54b876004c32d38d9c0363fd292d745fee8dff3c", "max_stars_repo_licenses": ... |
# -*- coding: utf-8 -*-
"""
@author: syahr
"""
import gc
import sys
import csv
import glob
import os
import pandas as pd
import traceback
from os.path import basename, dirname
from datetime import datetime
from pkg_resources import resource_filename
import argparse
from PyQt5.QtWidgets import QApplication, QFileDialo... | {"hexsha": "59b0691659bda5f3d7ecf3e369c81110f7c8405c", "size": 62354, "ext": "py", "lang": "Python", "max_stars_repo_path": "octavvs/mcr_als.py", "max_stars_repo_name": "S73ph4n/octavvs", "max_stars_repo_head_hexsha": "adbfa3f489b1928281a55de640c64d20afb4f9e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#!/usr/bin/env python3
"""
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.
"""
from queue import Queue
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
import torch.nn... | {"hexsha": "8d4f4b20e9f52f08d1461dc6405cdfebae65b1f0", "size": 2917, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "yutingye/FRL-Distributed-ML-Scaffold", "max_stars_repo_head_hexsha": "717e46bef2be17e9e4ef9e542a8d7d10669950b4", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma (in Ring) coprime_n_idealsTr0:"\<lbrakk>ideal R A; ideal R B; ideal R C;
coprime_ideals R A C; coprime_ideals R B C \<rbrakk> \<Longrightarrow>
coprime_ideals R (A \<diamondsuit>\<^sub>r B) C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>ideal R A; ideal R B; idea... | {"llama_tokens": 4771, "file": "Group-Ring-Module_Algebra5", "length": 15} |
import numpy as np
import utils
from collections import defaultdict
# ====================================================================================================================
# Read data in format of array (n_samples, n_features)
# =========================================================================... | {"hexsha": "1f3bab8d203fda71b1af6f4ce98620a9941e0782", "size": 1743, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/causality/randomExperiments/evaluatorUsage.py", "max_stars_repo_name": "AnverK/VK_Graduation", "max_stars_repo_head_hexsha": "a8d457d1fcb677d417a5ea82011393160762c0b1", "max_stars_repo_license... |
"""
Este módulo contém as funções comuns para equações diferenciais parciais
em estados estacionários.
"""
import numpy as np
def set_u(x, y, bound_x0, bound_xf, bound_y0, bound_yf):
"""Inicializa a matriz 'u' com as condições de contorno."""
u = np.empty((x.size, y.size))
u[0, :] = bound_x0
u[-1, :... | {"hexsha": "f0b4766f5b68df479ff545f34c438800302e546c", "size": 394, "ext": "py", "lang": "Python", "max_stars_repo_path": "pdepy/steady.py", "max_stars_repo_name": "OliverTso/PDE", "max_stars_repo_head_hexsha": "b65fd92f0d62d4160ef93e2a29762025ba869012", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_s... |
[STATEMENT]
lemma partial_evaluation_is_saturated :
assumes "saturated_binary_rule resolvent S"
shows "saturated_binary_rule ordered_resolvent (partial_evaluation S C)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. saturated_binary_rule ordered_resolvent (partial_evaluation S C)
[PROOF STEP]
proof (rule ccontr... | {"llama_tokens": 7165, "file": "PropResPI_Prime_Implicates", "length": 63} |
#!/usr/bin/env python
# encoding: utf-8
#
# This file is part of pydc1394.
#
# pydc1394 is free software: you can redistribute it and/or modify it
# under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later v... | {"hexsha": "e0820c853714eb2f4ae60e35c65b3fc3aceb5fc1", "size": 38667, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydc1394/camera.py", "max_stars_repo_name": "joristork/milovision", "max_stars_repo_head_hexsha": "aeb09b9c75f7bc0900cb513079bbe08b3c439bbc", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import CommutativeRings: pseudo_ideal
@testset "basics" begin
R = ZZ{Int}
RX = R[:x]
RYZ = R[:y,:z]
@test iszero(Ideal(0))
@test isone(Ideal(-1))
@test Ideal(15, 21) == Ideal(3)
@test Ideal([3, 5]) == R
@test R == Ideal(-1)
@test Ideal(3, ZZ(15)) == Ideal(3)
x, = generators(RX... | {"hexsha": "4c3a1bf43fd95fca59d19b19a1a034cfc2f188de", "size": 1898, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/ideal.jl", "max_stars_repo_name": "KlausC/CommutativeRings.jl", "max_stars_repo_head_hexsha": "2b6027c126b90f61bbad4ea230a34367522c3e52", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""
Olivia Model for studying the vulnerability of package dependency networks.
Olivia stands for 'Open-source Library Indexes Vulnerability Identification and Analysis'.
Includes tools for the analysis of package dependency networks vulnerability to failures and attacks.
"""
import networkx as nx
import gzip
import ... | {"hexsha": "b1c60eeb789485cc5ea2a811fea0fb93d06bef71", "size": 9093, "ext": "py", "lang": "Python", "max_stars_repo_path": "olivia/model.py", "max_stars_repo_name": "dsr0018/olivia", "max_stars_repo_head_hexsha": "8b7de3a512848c5d313bbc848ac9c7b667c2f6ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import numpy as np
import logging
from mtrack.evaluation.dda3 import DDA3
from mtrack.graphs import G1
from mtrack.preprocessing import g1_to_nml
logger = logging.getLogger(__name__)
class VoxelSkeleton(object):
def __init__(self, g1_cc, voxel_size, subsample=1):
"""
Interpolate a g1 graph connec... | {"hexsha": "154a1edea112f0bffc6e617a5ebacd2ef163b478", "size": 5208, "ext": "py", "lang": "Python", "max_stars_repo_path": "mtrack/evaluation/voxel_skeleton.py", "max_stars_repo_name": "nilsec/mtrack", "max_stars_repo_head_hexsha": "76652c468417c7e3ac9903586c0127b884d6b032", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import gensim
import pickle
import os
import data
import numpy as np
import argparse
import tensorflow as tf
from transformers import BertTokenizer, TFBertModel
from tqdm import tqdm
import time
parser = argparse.ArgumentParser(description='The Embedded Topic Model')
### data and file related arguments
parser.add_arg... | {"hexsha": "6173b3fd0c37a5993f30c43ecf956540e95e61fc", "size": 2929, "ext": "py", "lang": "Python", "max_stars_repo_path": "bert.py", "max_stars_repo_name": "jdenes/ETM", "max_stars_repo_head_hexsha": "dda02ce65e1d7ef22fbc0a869e6f91833adf8244", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_rep... |
import numpy as np
from growth_procs import unit_sample_on_sphere,\
direction_to,\
normalize_length,\
get_entity,\
prepare_next_front
L_NORM=2.0
def extend_front(front,seed,constellation) :
# attract by a different neuron, get information
other_entities = get_entity("substance_x",constell... | {"hexsha": "2fe7ef59d96d9197eb69533ebb4693c34fde9802", "size": 827, "ext": "py", "lang": "Python", "max_stars_repo_path": "hackathon/HePing/neuromac-master/examples/update_environment/Attracted_by.py", "max_stars_repo_name": "zzhmark/vaa3d_tools", "max_stars_repo_head_hexsha": "3ca418add85a59ac7e805d55a600b78330d7e53d"... |
\documentclass[output=collectionpaper]{langsci/langscibook}
\ChapterDOI{10.5281/zenodo.3462772}
\title{Gender typology and gender (in)stability in Hindu Kush Indo-Aryan languages}
\shorttitlerunninghead{Gender in Hindu Kush Indo-Aryan}
\author{Henrik Liljegren\affiliation{Stockholm University}}
\abstract{This paper ... | {"hexsha": "aa0971e3242f7be883ef74be683c45a2f89c745b", "size": 111534, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/10.tex", "max_stars_repo_name": "langsci/223", "max_stars_repo_head_hexsha": "ea6237c615cb72a22455fdf221866093c7c5b5c3", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null,... |
__precompile__()
"""
$(DocStringExtensions.README)
"""
module ParameterizedFunctions
using DocStringExtensions
using DataStructures, DiffEqBase, Latexify
using Reexport
@reexport using ModelingToolkit
using ModelingToolkit: Sym, FnType, tosymbol
import LinearAlgebra
import Base: getindex
include("od... | {"hexsha": "aa2c1a6c6cc9055fb04728963a3245a01149fd2c", "size": 1968, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ParameterizedFunctions.jl", "max_stars_repo_name": "anandijain/ParameterizedFunctions.jl", "max_stars_repo_head_hexsha": "f71ef21b75a246ae3aaae0c4325d40ca0e6f899e", "max_stars_repo_licenses": [... |
"""
Adjust weekday effects via Poisson model.
"""
# third party
import cvxpy as cp
import numpy as np
from sklearn.model_selection import LeaveOneOut
class Weekday:
"""Class to handle weekday effects."""
@staticmethod
def get_params(sig, lam=10):
"""
Estimate the fitted parameters of th... | {"hexsha": "0e5481bd7d0627c32167e71d3fb9957eede07a0f", "size": 4752, "ext": "py", "lang": "Python", "max_stars_repo_path": "case_deconv/code/weekday.py", "max_stars_repo_name": "dfarrow0/covidcast-nowcast", "max_stars_repo_head_hexsha": "8d9dfc56c643c4f47b72a58dc3e8811ddeb1a6c8", "max_stars_repo_licenses": ["MIT"], "ma... |
#include <boost/filesystem.hpp>
#include "KAI/Core/File.h"
#include "KAI/Core/Object.h"
#include "KAI/Core/Exception.h"
using namespace boost::filesystem;
using namespace std;
KAI_BEGIN
namespace File
{
namespace fs = boost::filesystem;
// return a vector of all files that have the given extension, startin... | {"hexsha": "445bd249b784bce7edfd9f435aa2e5568173f493", "size": 2097, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Source/Library/Core/Source/File.cpp", "max_stars_repo_name": "cschladetsch/KAI", "max_stars_repo_head_hexsha": "b7078bc73817f0f76805c9330dbaf45584d86a22", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Copyright (c) 2021 Mira Geoscience Ltd.
#
# This file is part of geoapps.
#
# geoapps is distributed under the terms and conditions of the MIT License
# (see LICENSE file at the root of this source code package).
from __future__ import annotations
from typing import Any
from uuid import UUID
import numpy as np... | {"hexsha": "0df8e11e89a38a1c6707cfe911566892719359e1", "size": 13719, "ext": "py", "lang": "Python", "max_stars_repo_path": "geoapps/io/validators.py", "max_stars_repo_name": "MiraGeoscience/geoapps", "max_stars_repo_head_hexsha": "74568414f8eb7342ad68473e2edc3a799f2fdca4", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
from pathlib import Path
import codecs
import pysrt
from matplotlib import pyplot as plt
"""
All thresholds
"""
TRANSCRIPT_DELAY = 0 # 6 for unaligned videos
MIN_TRANSCRIPT = 0.3
MIN_BLACKFRAME = 0.99
MIN_BLACKWINDOW = 1
MIN_BLANKWINDOW = 30
MAX_BLANKWINDOW = 270
MIN_LOWERTEXT = 0.5
MIN_LOWERWI... | {"hexsha": "5b8eb0356edecf8cf501330a0b4ce9cf2d71bf97", "size": 16488, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/esper/commercial_detection.py", "max_stars_repo_name": "scanner-research/esper-tv", "max_stars_repo_head_hexsha": "179ef57d536ebd52f93697aab09bf5abec19ce93", "max_stars_repo_licenses": ["Apac... |
import numpy as np
import tensorflow as tf
from tensorflow import keras
# define iou or jaccard loss function
def iou_loss (y_true, y_pred):
y_true = tf.reshape (y_true, [-1])
y_pred = tf.reshape (y_pred, [-1])
intersection = tf.reduce_sum (y_true * y_pred)
score = (intersection + 1.) / (tf.reduce_s... | {"hexsha": "7e1bcbf16c45c4988a5a4d6e9bc68f37aa6f0a36", "size": 1088, "ext": "py", "lang": "Python", "max_stars_repo_path": "resources/Object Detection/train.py", "max_stars_repo_name": "miladlink/Streamlit_Flask", "max_stars_repo_head_hexsha": "23340eeab192f0ccae9a6cc03f7eb9b7b8985f6a", "max_stars_repo_licenses": ["MIT... |
import os
import random
from tqdm import trange
from scipy.misc import imsave
import tensorflow as tf
import numpy as np
from generator import Generator
from discriminator import Discriminator
from utils import logger
class HistoryQueue(object):
def __init__(self, shape=[128,128,3], size=50):
self._size... | {"hexsha": "f47c599e70952a607daa7f3f5349ed470926d523", "size": 10606, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "ngc92/CycleGAN-Tensorflow", "max_stars_repo_head_hexsha": "cd3cf625c1b81581bbf8a7ca821eb50b6f681311", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 24,... |
# This method give you 0.6924224, adn 75% of confidence.
import fcntl
import time
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.metrics import log_loss
# random search
# Best: 0.509408 using {'subsample': 0.7, 'reg_alpha': 0.005, 'n_estimators': 100, 'min_child_weight': 5, 'max_depth': 3, ... | {"hexsha": "7a332ab42b599ed4e11895623f883eb2f2ce3264", "size": 4164, "ext": "py", "lang": "Python", "max_stars_repo_path": "XGBoost_method.py", "max_stars_repo_name": "Nawter/numer.ai", "max_stars_repo_head_hexsha": "5a07fe8bf10eb8bdbbaea1e0ab20940d6fbc21af", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
20080808 19:14:12 nbsp How about adding a link or two to other pages on the wiki so that this page is less of a dead end? Users/JasonAller
| {"hexsha": "edef0cd6b39dcc38b49413107936396fcd6a3911", "size": 140, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/JasonHammons.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
eltype_or(::Type{G}, or) where G <: (AbstractGeometry{N, T} where N) where T = T
eltype_or(::Type{G}, or) where G <: (AbstractGeometry{N, T} where {N, T}) = or
ndims_or(::Type{G}, or) where G <: (AbstractGeometry{N, T} where T) where N = N
ndims_or(::Type{G}, or) where G <: (AbstractGeometry{N, T} where {N, T}) = or
... | {"hexsha": "33c74dd27c0a101b987646785fc9edaa61a294e8", "size": 2679, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "jw3126/GeometryBasics.jl", "max_stars_repo_head_hexsha": "3c4d3da2b93f11adab85f0d4d0c6051e9fe5625a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 81... |
import numpy as np
import pandas as pd
import os
import time
import shutil
from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
import indoor_location.getWordVector as getWordVector
import indoor_location.globalConfig as globalCo... | {"hexsha": "ab8fe26243e8e560fb1a3a00168d50894888c1f1", "size": 21121, "ext": "py", "lang": "Python", "max_stars_repo_path": "indoor_location/dataProcess.py", "max_stars_repo_name": "WQAQs/keras-bert", "max_stars_repo_head_hexsha": "17a08c2b727eda7381bc815b0983ca27021f5041", "max_stars_repo_licenses": ["MIT"], "max_star... |
module adjtest_obs
!$$$ module documentation block
! . . . .
! module: adjtest_obs
! prgmmr: Syed RH Rizvi, NCAR/NESL/MMM/DAS
!
! abstract: Performs adjoint test for linear observation operator
!
! program history log:
! 2012-09-14 Rizvi, NCAR/NESL/MMM/DAS -... | {"hexsha": "67e2ff0cdd84978c84fbce36a860a6238fbf2085", "size": 5793, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "GEOSaana_GridComp/GSI_GridComp/adjtest_obs.f90", "max_stars_repo_name": "GEOS-ESM/GEOSana_GridComp", "max_stars_repo_head_hexsha": "cf33607613754313a2383bb7e7b3d29c856b9daf", "max_stars_repo_lic... |
import threading
import time
import os
import logging
from scipy.spatial.distance import euclidean
#import numpy as np
#from peon import BlockTypes
log = logging.getLogger(__name__)
def start_afk_thread(bot):
def do_afk_thread(bot, pid):
i = 0
while True:
if time.time() - bot.last_ke... | {"hexsha": "087d22dd14f3ea8d36af9fa2aed9674c78d8e457", "size": 2354, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "Rexkh/mcpython", "max_stars_repo_head_hexsha": "31a4af448d740b4625556ceb927f514717de266a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 30, "max_stars_... |
mutable struct Lorentz
σ::Float64
ρ::Float64
β::Float64
x::Float64
y::Float64
z::Float64
end
function step!(l::Lorentz, dt::Float64)
dx = l.σ*(l.y - l.x)
dy = l.x*(l.ρ - l.z) - l.y
dz = l.x*l.y - l.β*l.z
l.x += dt*dx
l.y += dt*dy
l.z += dt*dz
end
attractor = Lorentz(10.... | {"hexsha": "4162d423a8a4a247849c870ddcfc5d850605ef10", "size": 729, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "slides2021/day3/lorentz.jl", "max_stars_repo_name": "oist/skillpill-julia", "max_stars_repo_head_hexsha": "eb719677ae84bd26c1e3fdb889cf2b54f65d47ae", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
function solve_boundary_condition(ω::T, k_eff::Complex{T}, eigenvectors::Array{Complex{T}}, psource::PlaneSource{T,2,1,Acoustic{T,2}}, material::Material{2,Halfspace{T,2}};
basis_order::Int = 2,
kws...
) where T
if size(eigenvectors)[end] > 1
@warn "The effective wavenumber: $k_eff has ... | {"hexsha": "b4429bad88717dfe2b7f55a40116b96200541a58", "size": 5525, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/acoustics/effective_wave/planewaves/boundary_condition.jl", "max_stars_repo_name": "JuliaWaveScattering/EffectiveWaves.jl", "max_stars_repo_head_hexsha": "6c11a212761934e72f691ad19b29cf2f8d426e... |
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import numpy as np
import openmdao.api as om
from mach import MachSolver
from motor_current import MotorCurrent
num_magnets_true = 40
num_magnets = 40
mag_pitch = num_magnets // num_magnets_true
num... | {"hexsha": "9e4ef900c91308a46d4fea2a68533191f357dd9c", "size": 8273, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_motor_ac_loss.py", "max_stars_repo_name": "tuckerbabcock/MotorModel", "max_stars_repo_head_hexsha": "3eef2a855594daa330b63aca651d918bd4cfbe46", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
export L2Loss, Regularization, LogLikeLoss, prior_loss
struct Regularization{L,P} <: DiffEqBase.DECostFunction
λ::L
penalty::P
end
Regularization(λ) = Regularization{typeof(λ),typeof(L2Penalty())}(λ,L2Penalty())
function (f::Regularization)(p)
f.λ*value(f.penalty, p)
end
function prior_loss(prior,p)
ll = 0.0... | {"hexsha": "619b1bf4fa50cf58fe1dde977052c44325617946", "size": 5358, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/cost_functions.jl", "max_stars_repo_name": "Vaibhavdixit02/DiffEqParamEstim.jl", "max_stars_repo_head_hexsha": "d68ac5e139eb1005064d1b5f404870c008889d03", "max_stars_repo_licenses": ["MIT"], "m... |
% !Mode:: "TeX:UTF-8"
\chapter{The Theory of FDTD}
\section{Yee Cell}
Maxwell's equations are a set of equations which can be written in differential form or integral form. They are the foundation of macroscopic electromagnetic phenomenas. There are two kinds of numerical solver to Maxwell's equation. One kind of solv... | {"hexsha": "47ba941c2a9b8dc17dc11ccdc1d14cb4674c711c", "size": 22593, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "latex-en/chapters/chapter2.tex", "max_stars_repo_name": "obserthinker/bachelorgraduatethesis", "max_stars_repo_head_hexsha": "445351447c95a48b5f8af4b1081c3dcf0018045c", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma closed_segment_eq_sing: "closed_segment a b = {c} \<longleftrightarrow> a = c \<and> b = c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (closed_segment a b = {c}) = (a = c \<and> b = c)
[PROOF STEP]
by auto | {"llama_tokens": 96, "file": null, "length": 1} |
#!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
import math
z_one = None
z_two = None
def without_resampling():
# Plot the axes in grey.
plt.axhline(linewidth=1, color='#bbbbbb')
plt.axvline(linewidth=1, color='#bbbbbb')
# The X domain here is modeled with 100 sample frames ... | {"hexsha": "0f6a3825e0c69fad18283c046a3b4a733d55e26a", "size": 4149, "ext": "py", "lang": "Python", "max_stars_repo_path": "plots/resampling.py", "max_stars_repo_name": "nick-thompson/neuro", "max_stars_repo_head_hexsha": "fa06f1de6bb38279ea360ebc19057bb2e39e5665", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
nx,ny,nr,nt=50,100,30,1
# double prec ieee-be
wt64 = np.ones((nt,nr,ny,nx),dtype='>f8')
wt64.tofile('ones_64b.bin')
| {"hexsha": "cdbde599d04994887729d210f6130e34dc60252f", "size": 137, "ext": "py", "lang": "Python", "max_stars_repo_path": "verification/isomip/input_ad/genweights.py", "max_stars_repo_name": "mitgcm/mitgcm", "max_stars_repo_head_hexsha": "20a156cdcefcb000006123cf7ddac8fc997cb603", "max_stars_repo_licenses": ["MIT"], "m... |
Require Import bigstep.
Require Import coinduction.
Require Import datatypes.
Require Import MminusNoo.
Require Import Ndiv2oo.
Require Import streams_vs_lists.
Require Import UndefNoo.
(* Function undef(n) = undefined for any n
Example machine:
1 1 -> 1 R
1 B -> 2 1
2 1 -> 2 L
2 B -> 1 1
... | {"author": "asr", "repo": "tm-coinduction", "sha": "599083b74ffdf0c1032c5c2495fef9bf23a4058c", "save_path": "github-repos/coq/asr-tm-coinduction", "path": "github-repos/coq/asr-tm-coinduction/tm-coinduction-599083b74ffdf0c1032c5c2495fef9bf23a4058c/animation/examples/UndefNoo_unguarded.v"} |
function [K, sK] = indexKernCompute(kern, x, x2)
% INDEXKERNCOMPUTE Compute the INDEX kernel given the parameters and X.
% FORMAT
% DESC computes the kernel parameters for the index based covariance function
% kernel given inputs associated with rows and columns.
% ARG kern : the kernel structure for which the matrix ... | {"author": "SheffieldML", "repo": "GPmat", "sha": "4b5914a38ecbad9fb7a13a3392970bfc28c9d911", "save_path": "github-repos/MATLAB/SheffieldML-GPmat", "path": "github-repos/MATLAB/SheffieldML-GPmat/GPmat-4b5914a38ecbad9fb7a13a3392970bfc28c9d911/kern/indexKernCompute.m"} |
#!/usr/bin/env python
# Copyright 2020 The PySCF Developers. 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... | {"hexsha": "6a6c0cea0310c3fc9d5d8eff122255f3728bc118", "size": 57033, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/solvent/_ddcosmo_tdscf_grad.py", "max_stars_repo_name": "QuESt-Calculator/pyscf", "max_stars_repo_head_hexsha": "0ed03633b699505c7278f1eb501342667d0aa910", "max_stars_repo_licenses": ["Apac... |
import numpy as np
import pytest
from pandas import (
DataFrame,
DatetimeIndex,
PeriodIndex,
Series,
date_range,
period_range,
)
import pandas._testing as tm
class TestToPeriod:
def test_to_period(self):
rng = date_range("1/1/2000", "1/1/2001", freq="D")
t... | {"hexsha": "bc4053a313da41851ff3be671f5524af25056831", "size": 1811, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/Lib/site-packages/pandas/tests/series/methods/test_to_period.py", "max_stars_repo_name": "OliviaNabbosa89/Disaster_Responses", "max_stars_repo_head_hexsha": "1e66d77c303cec685dfc2ca94f4fca4cc... |
C * * * * * * * * * * * * * * * * * * * * * * * * *
C --- DRIVER FOR RADAU5 ON E5 PROBLEM
C * * * * * * * * * * * * * * * * * * * * * * * * *
IMPLICIT REAL*8 (A-H,O-Z)
C --- PARAMETERS FOR RADAU5 (FULL JACOBIAN)
PARAMETER (ND=4,LWORK=4*ND*ND+12*ND+20,LIWORK=3*ND+20)
DIMENSION Y(ND),WORK(LWORK),I... | {"hexsha": "c738539dd310c490e3c20af963b6d0a4cbe7b114", "size": 2797, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "tests/e5_radau5.f", "max_stars_repo_name": "cpmech/hwode", "max_stars_repo_head_hexsha": "7a6c033ace96b8b8dcf564ba0f38dd501fe2f60d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count":... |
#include <boost/test/unit_test.hpp>
#include "Werk/Utility/Counter.hpp"
BOOST_AUTO_TEST_SUITE(CounterTest)
BOOST_AUTO_TEST_CASE(TestAdd)
{
Werk::Counter<> c;
BOOST_REQUIRE_EQUAL(c.value(), 0);
c.increment();
BOOST_REQUIRE_EQUAL(c.value(), 1);
c.increment();
BOOST_REQUIRE_EQUAL(c.value(), 2);
c.reset();
... | {"hexsha": "beddc81618ef8c73374041af8c1ce2cbfdbc5c99", "size": 970, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/WerkTest/Utility/Counter.cpp", "max_stars_repo_name": "mish24/werk", "max_stars_repo_head_hexsha": "2f8822842fb8f68a4402775d1d3b41021b5a9945", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from numpy import array
from pybimstab.bim import BlocksInMatrix
slopeCoords = array([[0, 1, 1, 0, 0], [0, 0, 1, 1, 0]])
bim = BlocksInMatrix(slopeCoords=slopeCoords, blockProp=0.5,
tileSize=0.1, seed=123)
fig = bim.plot()
| {"hexsha": "2f6ad990f5eab337242a7bdb70854df384b54463", "size": 244, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/figuresScripts/bim_example1.py", "max_stars_repo_name": "eamontoyaa/pybimstab", "max_stars_repo_head_hexsha": "ca13d23379c60453e1df53c6d3849902e3c600e0", "max_stars_repo_licenses": ["BSD-2... |
# Copyright 2020 Google LLC
#
# Licensed under the Apache License',' Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing... | {"hexsha": "f90bbb3dac44a11935db7b2d98ef8df6192802d2", "size": 1127, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/oecd/regional_demography/clean_geos_resolved_to_dict.py", "max_stars_repo_name": "eftekhari-mhs/data", "max_stars_repo_head_hexsha": "af6dd910be966a657878a68b5a6e7d07342f78a3", "max_stars_... |
import numpy as np
import pylab as pl
from numpy import pi, sin, cos
# ***************************************************
#
# TEST 102
#
# ***************************************************
eps = 1.e-1
m = 2 ; n = 1
f = lambda y : sin( n*pi * ( 4*y*(1-y) ) )
df = lambda y : (-4*pi*n*y + 4*pi... | {"hexsha": "d570b2ed9d88b7f8ecd775fdc0f1c4bfc6d69fe6", "size": 2514, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/manual/include/approximation/anidiff_tests_potential.py", "max_stars_repo_name": "NegriLuca/pigasus", "max_stars_repo_head_hexsha": "d5057b771f81cfa05bb08ea4b0fd99088150cd7a", "max_stars_repo_... |
from types import SimpleNamespace
from enum import Enum
import logging
import numpy as np
import cv2
from .detector import SSDDetector, YOLODetector, PublicDetector
from .feature_extractor import FeatureExtractor
from .tracker import MultiTracker
from .utils import Profiler
from .utils.visualization import Visualizer
... | {"hexsha": "165a9874d0385470e145a055d17a1065b52e639f", "size": 8810, "ext": "py", "lang": "Python", "max_stars_repo_path": "fastmot/mot.py", "max_stars_repo_name": "6DammK9/FastMOT", "max_stars_repo_head_hexsha": "ff5febf4f4bac576db6e5846479bdc0891fa740b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
tests = ["testini.jl",
"testhttp.jl",
"testsimple.jl",
"testmerge.jl"]
for test in tests
include(test)
end
outfile = joinpath(@__DIR__, "confs", "out.conf")
if isfile(outfile)
rm(outfile)
end
| {"hexsha": "377cffa6f3e783dc515a9f8e0af9d9ba1d8be270", "size": 229, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ConfParser.jl-88353bc9-fd38-507d-a820-d3b43837d6b9", "max_stars_repo_head_hexsha": "0b00cacf5e8eaca477ef85cf95c967214184f4cb... |
[STATEMENT]
lemma less_restrictive_complete:
assumes "less_restrictive R1 R2"
assumes "Complete R2"
shows "Complete R1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Complete R1
[PROOF STEP]
using assms less_restrictive_saturated Complete_def
[PROOF STATE]
proof (prove)
using this:
less_restrictive R1 R2
Comp... | {"llama_tokens": 255, "file": "PropResPI_Propositional_Resolution", "length": 2} |
# -*- coding: utf-8 -*-
"""
Created on Tue May 22 11:29:02 2018
@author: YSu
"""
from __future__ import division
from sklearn import linear_model
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.api import VAR, DynamicVAR
import pandas as pd
import matp... | {"hexsha": "3f72d49886348dc79f8cb1af9fbb39be85a62010", "size": 11514, "ext": "py", "lang": "Python", "max_stars_repo_path": "Stochastic_engine/Synthetic_weather/Validation.py", "max_stars_repo_name": "romulus97/HYDROWIRES", "max_stars_repo_head_hexsha": "115e534764d8f58d64340d99cf6cb8eb6598c4ee", "max_stars_repo_licens... |
#!/usr/bin/env python
"""
Import lybraries
"""
from __future__ import print_function
import os
import os.path
import datetime
import sys
import argparse
import time
import serial
import serial.tools.list_ports
import struct
import numpy
import matplotlib.pyplot as plt
from enum import Enum
from threading import Threa... | {"hexsha": "2c593fc3221ad5ac4fa07fcb6900bc7698b52638", "size": 6360, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python_DataLogger/src/test.py", "max_stars_repo_name": "jonra1993/BLDC_Speed_Control", "max_stars_repo_head_hexsha": "6c211660e829966f5e4943b90ad16840d91b7671", "max_stars_repo_licenses": ["MIT"],... |
import os
import sys
from compute import Config_ini
curPath = os.path.abspath(os.path.dirname(__file__))
rootPath = os.path.split(curPath)[0]
sys.path.append(os.path.split(rootPath)[0])
from algs.genetic_CNN.utils import Utils
from algs.genetic_CNN.genetic.statusupdatetool import StatusUpdateTool
from comm.log impor... | {"hexsha": "1bfa7e179d5c06bfdb181b50be85d4791c8f35b6", "size": 5507, "ext": "py", "lang": "Python", "max_stars_repo_path": "BenchENAS_python_package/algs/genetic_CNN/main.py", "max_stars_repo_name": "benchenas/BenchENAS", "max_stars_repo_head_hexsha": "776cd1dd035d73c4af369d0106d010b932f64782", "max_stars_repo_licenses... |
import os
import json
import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
year_join=2015
month_join=10
def timeAddition(df,timestamp):
year=[timeConversion(x).split("-")[0] for x in df[str(timestamp)]]
month=[timeConversion(x).split("-")[1] for x in df[str(timestamp)]]
... | {"hexsha": "834d266dd6d978a964256711178617441978e243", "size": 5237, "ext": "py", "lang": "Python", "max_stars_repo_path": "post.py", "max_stars_repo_name": "ujjaldas132/facebook-User-Information", "max_stars_repo_head_hexsha": "eb65e2b098313328bfc83c531f7619a96353e06b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
(************************************************************************)
(* v * The Coq Proof Assistant / The Coq Development Team *)
(* <O___,, * INRIA - CNRS - LIX - LRI - PPS - Copyright 1999-2010 *)
(* \VV/ **************************************************************)
(* // * Th... | {"author": "mattam82", "repo": "Coq-misc", "sha": "60bc3cbe72083f4fa1aa759914936e4fa3d6b42e", "save_path": "github-repos/coq/mattam82-Coq-misc", "path": "github-repos/coq/mattam82-Coq-misc/Coq-misc-60bc3cbe72083f4fa1aa759914936e4fa3d6b42e/theories/Reals/RiemannInt_SF.v"} |
import numpy as np
import colorsys
def get_colors(n):
hue = np.arange(0., 360., 360. / n) / 360
lightness = (50 + 10 * np.random.rand(n)) / 100
saturation = (90 + 10 * np.random.rand(n)) / 100
return list(zip(hue, lightness, saturation))
def get_colors_rgb(n):
return [colorsys.hls_to_rgb(*hls) for... | {"hexsha": "6962230ac3cee1dddf9b5abfeb46f4091691e89a", "size": 1080, "ext": "py", "lang": "Python", "max_stars_repo_path": "filigree/colors.py", "max_stars_repo_name": "Sweetblood22/filigree", "max_stars_repo_head_hexsha": "f80e7f04e0bbf3e99887c0d52bf4cccd04fc4ce4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from . import _utils as utils
from ._libs import (string_funcs as _sf,
math as _math)
import re
import numpy as np
from numpy import nan, ndarray
from typing import (Union, Dict, List, Optional, Tuple, Callable, overload,
NoReturn, Set, Iterable, Any, TypeVar, Type, Generator)
f... | {"hexsha": "0a0a657799a250da01f1d247aecc05ea7a59f546", "size": 26422, "ext": "py", "lang": "Python", "max_stars_repo_path": "dexplo/_strings.py", "max_stars_repo_name": "dexplo/dexplo", "max_stars_repo_head_hexsha": "2a522437d3bf848260f9772e7a8f705f534c2e2c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
import numpy as np
import RPi.GPIO as GPIO
import datetime,time,os
calibration = 0.2
pin = 23
sleep_time = 58.5
GPIO.setmode(GPIO.BCM)
GPIO.setup(pin,GPIO.IN,pull_up_down=GPIO.PUD_UP)
rain = 0.
def cb(channel):
global rain
rain += calibration
GPIO.add_event_detect(pin,GPIO.FALLING,callback=cb,bouncetime=300... | {"hexsha": "b0e728a7c92c2b71fec53f660138221cd5c95e47", "size": 1149, "ext": "py", "lang": "Python", "max_stars_repo_path": "rpi_scripts/rain.py", "max_stars_repo_name": "AdamTheisen/3DWxSt", "max_stars_repo_head_hexsha": "1a6378eb44477fbd1ad3f8f8a18515e9a2512ef8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
from allennlp.models import Model
import torch
import numpy as np
from allennlp.data import Vocabulary
from allennlp.models import Model
from allennlp.nn import util
from allennlp.training.metrics import CategoricalAccuracy, Metric, F1Measure
from pytorch_pretrained_bert.modeling import BertModel
from kb.metrics import... | {"hexsha": "d25183bf3f8983fe21a6fc2d9f61bb7407ac3a0f", "size": 9010, "ext": "py", "lang": "Python", "max_stars_repo_path": "kb/evaluation/classification_model.py", "max_stars_repo_name": "shmuelamar/kb", "max_stars_repo_head_hexsha": "b5c26ad11e3f6cb1569187cddef600db04bb4399", "max_stars_repo_licenses": ["Apache-2.0"],... |
module Gaussians
using LinearAlgebra
using SpecialFunctions
using Formatting
include("sto_ng.jl")
export STO_NG, sto_g,
KineticOperator, CoulombPotential,
ElectronRepulsionIntegrals
end
| {"hexsha": "dfd16d740c18c31c614124137a268a9daec14f23", "size": 199, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Gaussians.jl", "max_stars_repo_name": "JuliaAtoms/Gaussians.jl", "max_stars_repo_head_hexsha": "e1a7c943d414e289b5f260181b4bcfab3d47511d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
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