text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy
from pylab import *
from scipy.interpolate import interp1d
d1,g1,e1,ee1,f1,ef1,s=numpy.loadtxt("PEC_combined_results.txt",unpack=True,skiprows=1)
f1=-f1*31.6e-15
inds=argsort(d1)
d1=d1[inds]
f1=f1[inds]
g1=g1[inds]
s=s[inds]
inds=numpy.where(s == 0)
d1=d1[inds]
f1=f1[inds]
g1=g1[inds]
d1t,g1t,e1t,ee1t,f1... | {"hexsha": "08ef9fd7bcccb8393bb2fac967e6bd017f0d9d79", "size": 4407, "ext": "py", "lang": "Python", "max_stars_repo_path": "casimir/scuffCode/CubeSphere/compare.py", "max_stars_repo_name": "charlesblakemore/opt_lev_analysis", "max_stars_repo_head_hexsha": "704f174e9860907de349688ed82b5812bbb07c2d", "max_stars_repo_lice... |
"""Methods used for INtERAcT."""
import numpy as np
import pandas as pd
from collections import Counter
from numpy.linalg import norm
from scipy.stats import entropy
from scipy.spatial.distance import pdist, squareform
from .nn_tree import NeighborsMode
def _nn_data_to_clusters_counts(
nn_data, number_of_clusters... | {"hexsha": "3f702f514b54dee5286b862aa91ab8d95349b883", "size": 5850, "ext": "py", "lang": "Python", "max_stars_repo_path": "interact/core.py", "max_stars_repo_name": "drugilsberg/interact", "max_stars_repo_head_hexsha": "39cf22d8b8b35b9a38e111d0b016331af0c3747a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4... |
import argparse
import json
import networkx as nx
import os
parser = argparse.ArgumentParser()
parser.add_argument('-i', help='Input json', required=True)
parser.add_argument('-o', help='Output json', required=True)
args = parser.parse_args()
def main():
with open(args.i, 'r') as f:
data = json.load(f)
... | {"hexsha": "56838e6143b48544aefc1b4f334cbca74a1d03ff", "size": 2541, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignments/hw9/260831147_submission_template/src/compute_network_stats.py", "max_stars_repo_name": "amalkoodoruth/DATASCIENCE-COMP598", "max_stars_repo_head_hexsha": "e2b212fe6adfdc670ec32ab91bb3... |
#!/usr/bin/env python
"""
example of libtcod's SDL hook
draws a simple white square.
"""
import tcod
import tdl
import numpy as np
# generate a callback for libtcod
@tcod.ffi.callback('SDL_renderer_t')
def sdl_hook(surface):
tcod.lib.SDL_UpperBlit(my_surface, tcod.ffi.NULL, surface, [{'x':0, 'y':0}])
p... | {"hexsha": "454ad642696b99a4a27b136f9140da77d362dfcb", "size": 971, "ext": "py", "lang": "Python", "max_stars_repo_path": "dev/sdl_hook_test.py", "max_stars_repo_name": "psizek/python-tcod", "max_stars_repo_head_hexsha": "32fcc42d5107c5b212978ea88df1a8f6f4743061", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars... |
#define BOOST_TEST_MODULE "JosephusModule"
#include <boost/test/unit_test.hpp>
#include <boost/test/unit_test_parameters.hpp>
#include "Josephus.h"
#include <list>
#include <vector>
BOOST_AUTO_TEST_CASE(ERASE_VECTOR_AT_INDEX)
{
boost::unit_test::unit_test_log.set_threshold_level(boost::unit_test::log_all_errors);
... | {"hexsha": "f56faa3698f25d5297ba33f2c8db943a7166b682", "size": 3977, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tests/JosephusTest.cc", "max_stars_repo_name": "RobertHue/JosephusProblem", "max_stars_repo_head_hexsha": "f4dcf95f21fb11c460832b2eab94da86d6048959", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 4 11:01:16 2015
@author: hehu
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.lda import LDA
from sklearn.svm import SVC, LinearSVC
from sklearn.linear_model import LogisticRegression
from sklearn.na... | {"hexsha": "d8b4e47a454ccd17596e7d034c11a5596f9e3150", "size": 7573, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/SVMExample.py", "max_stars_repo_name": "mahehu/SGN-41007", "max_stars_repo_head_hexsha": "c8ed169a0a5f70fb87b99448e39a573c0df584b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 61,... |
""" Doctests for Nipy / NumPy-specific nose/doctest modifications
"""
# try the #random directive on the output line
def check_random_directive():
'''
>>> 2+2
<BadExample object at 0x084D05AC> #random: may vary on your system
'''
# check the implicit "import numpy as np"
def check_implicit_np():
'... | {"hexsha": "533fd95475ac173862b078394afa3c3287d4965d", "size": 3188, "ext": "py", "lang": "Python", "max_stars_repo_path": "nipy/testing/tests/test_doctesting.py", "max_stars_repo_name": "neurospin/nipy", "max_stars_repo_head_hexsha": "cc54600a0dca1e003ad393bc05c46f91eef30a68", "max_stars_repo_licenses": ["BSD-3-Clause... |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 3 08:55:10 2020
@author: akurnizk
"""
import utm
import csv
import math
import flopy
import sys,os
import calendar
import dateutil
import numpy as np
import pandas as pd
import matplotlib as mpl
mpl.rc('xtick', labelsize=22)
mpl.rc('ytick', labels... | {"hexsha": "a45ce8194e6638d4de6f4a086e97391bc831c32c", "size": 61390, "ext": "py", "lang": "Python", "max_stars_repo_path": "SLR_Dike_HR.py", "max_stars_repo_name": "akurnizk/diked_hr_estuary_gw", "max_stars_repo_head_hexsha": "2430c756c635952502a5120ca65369f02da8c23f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
[STATEMENT]
lemma eq_nextl_class_in_left_lang_im: "eq_nextl `` {u} \<in> left_lang ` states M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. eq_nextl `` {u} \<in> left_lang ` states M
[PROOF STEP]
apply (rule rev_image_eqI [of "nextl (init M) u"])
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. nextl (init M) u ... | {"llama_tokens": 230, "file": "Finite_Automata_HF_Finite_Automata_HF", "length": 3} |
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 4 16:42:32 2019
@author: Dominic
"""
import numpy as np
from builtins import super
def initialize_sigma2(X, Y):
(N, D), (M, _) = X.shape, Y.shape
diff = X[np.newaxis,...] - Y[:,np.newaxis,:]
err = diff * diff
return np.sum(err) / (D * M * N)
class ex... | {"hexsha": "13aeef4ab3368eafed99bd919573e8d83480f2af", "size": 8642, "ext": "py", "lang": "Python", "max_stars_repo_path": "Registration/registration_core.py", "max_stars_repo_name": "antonykamp/AutoDot", "max_stars_repo_head_hexsha": "4be9912b312aa6725c6d3d49f7e2fe034124e208", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import os
import torch
from torch.utils.data import Dataset
from utils import *
from .utils import make_classes_counts
class MOSI(Dataset):
data_name = 'MOSI'
label_modes = ['binary','five','seven','regression']
supported_feature_names = {'covarep':'COVAREP','opensmile':'OpenSmile-emobas... | {"hexsha": "93867918ae4f11c76d533b8f023b5114f1189872", "size": 7649, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/datasets/mosi.py", "max_stars_repo_name": "dem123456789/Speech-Emotion-Recognition-with-Dual-Sequence-LSTM-Architecture", "max_stars_repo_head_hexsha": "a072cb940201bbcdb2d0f4d0dfa1dde478fa446... |
from controllers.utility import compute_spline, line_parameters, line_profile, line_profile_n
import numpy as np
from controllers.processing_template import QSuperThread
from controllers.micro_services import profile_painter_2, profile_collector, mic_project_generator
class QProcessThread(QSuperThread):
"""
P... | {"hexsha": "45ef2419c337dfad3915974b4b0041cc7adf7d33", "size": 4322, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/controllers/processing_microtubule.py", "max_stars_repo_name": "super-resolution/line_profiler", "max_stars_repo_head_hexsha": "472ee9433298327263ea1e1423b37fbe78d2c861", "max_stars_repo_licen... |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 29 12:14:51 2021
@author: ag
"""
import numpy as np
from glob import glob
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
import corner
import hadcrut5
import re
import os
from settings import scenariocolors, baseline_period, datafolder
from mi... | {"hexsha": "8b0a0d185cbd309d62a84f743d7da35bc9bb68db", "size": 1815, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/Aslak/ar6_tas_comparison.py", "max_stars_repo_name": "cmip6moap/project01", "max_stars_repo_head_hexsha": "749dea5fe572b9853d019489b4deea77ce01efd8", "max_stars_repo_licenses": ["CC-BY-4.0"],... |
;;; -*- syntax: common-lisp; package: OMEGA; base: 10; mode: Keim -*-
;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;
;; ;;
;; Copyright (C) 1996 by AG Siekmann, Fachbereich Informatik, ;;
;; Universi... | {"author": "theoremprover-museum", "repo": "OMEGA", "sha": "b95b25f8bb16847a2e18d106510446a175f7145a", "save_path": "github-repos/isabelle/theoremprover-museum-OMEGA", "path": "github-repos/isabelle/theoremprover-museum-OMEGA/OMEGA-b95b25f8bb16847a2e18d106510446a175f7145a/theories/post/post-tactics.thy"} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import nibabel as nb
import numpy as np
import external.transformations as tf
import Trekker
import vtk
import time
import psutil
import dti_funcs as dti
def main():
SHOW_AXES = True
AFFINE_IMG = True
NO_SCALE = True
COMPUTE_TRACTS = True
n... | {"hexsha": "989461c5fddaf1217723099e09d6c61f85cacce1", "size": 14472, "ext": "py", "lang": "Python", "max_stars_repo_path": "tractography/vtk_inv_tracts_coil.py", "max_stars_repo_name": "vhosouza/xcoord", "max_stars_repo_head_hexsha": "9226a6f919b3edec933753ff17815092ab95df9a", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/python
"""
esr_visualizer.py: version 0.1.0
Todo:
convert rosbag can_raw ESR track data to image
History:
2016/10/28: Initial version to display visual radar data from ros topic 'esr_front'.
"""
import math
import numpy as np
import argparse
import sys
import numpy as np
import rospy
import datetime
impor... | {"hexsha": "cc3c6bfe8f9afa67f5b5f2fedf9a6a41e5ea7476", "size": 2830, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros/src/sensing/drivers/can/packages/kvaser/nodes/esr_ros_can_source/esr_visualizer.py", "max_stars_repo_name": "MichaelOdum/Udacity-SDC-Radar-Driver-Micro-Challenge", "max_stars_repo_head_hexsha"... |
import numpy as np
from sklearn import linear_model
from m2cgen import assemblers, ast
from tests import utils
def test_single_feature():
estimator = linear_model.LinearRegression()
estimator.coef_ = [1]
estimator.intercept_ = 3
assembler = assemblers.LinearModelAssembler(estimator)
actual = ass... | {"hexsha": "4f03849beb02e2580e45a3460f95bfc89fda1635", "size": 3867, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/assemblers/test_linear.py", "max_stars_repo_name": "ggerrein/m2cgen", "max_stars_repo_head_hexsha": "e916f555b42e3a1d46828942c6b4e5c365c6a624", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD
from datetime import datetime
from dlimage.mnist import MNISTLoader
def vectorize(j):
e = np.zeros(10)
e[j] = 1.0
return e
mndata = MNISTLoader('dlimag... | {"hexsha": "b616af95170df4d27dbcb8568021524f1b240575", "size": 1539, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/keras/ann_mnist_test.py", "max_stars_repo_name": "morningkyle/DLImage", "max_stars_repo_head_hexsha": "52e1ca27387126233f6ba6c8533d2d0e236a58a2", "max_stars_repo_licenses": ["MIT"], "max_... |
from PIL import Image
import numpy as np
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('inputImage', help='Enter the path to image')
parser.add_argument('outputFile', help='Enter the path to output File')
parser.add_argument(
'-w', '--width', help='Enter width of output image', type=int, ... | {"hexsha": "41e77b9999b827eb67f64c3e72542962e6f5f851", "size": 1201, "ext": "py", "lang": "Python", "max_stars_repo_path": "ascii_image/ascii_image.py", "max_stars_repo_name": "TheFenrisLycaon/Automation-scripts", "max_stars_repo_head_hexsha": "ce5b32e3b0960e78680ca3a1113c801101263530", "max_stars_repo_licenses": ["MIT... |
import numpy as np
from ...domain import interpolate_to_height_levels, interpolate_to_pressure_levels
from ...utils.interpolation import methods as interpolation_methods
def weighted_velocity(ds_column, pres_cutoff_start, pres_cutoff_end):
"""Weighted velocity: needs more work"""
height_factor = interpolatio... | {"hexsha": "155b6ade5df88e9cd3d5210f12b68e8eae6587ee", "size": 2777, "ext": "py", "lang": "Python", "max_stars_repo_path": "lagtraj/trajectory/integration/velocity_estimation.py", "max_stars_repo_name": "xychen-ocn/lagtraj", "max_stars_repo_head_hexsha": "fbcae751faa7f9b9f9a72d20abf71bb92e007bdd", "max_stars_repo_licen... |
import h5py
import math
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from mpl_toolkits import mplot3d
from net import CVAE_stgcn as CVAE
from utils import loader_stgcn as loader
from utils import losses
from utils.common import *
from torc... | {"hexsha": "0ef12d2f49f641d00ce3037fb81fa9c48d1b369c", "size": 14012, "ext": "py", "lang": "Python", "max_stars_repo_path": "generator_cvae/utils/processor_stgcn.py", "max_stars_repo_name": "1suancaiyu/STEP", "max_stars_repo_head_hexsha": "54195112990feaee137f5137775c736d07c2d26f", "max_stars_repo_licenses": ["MIT"], "... |
include("INCLUDEME.jl")
using Yao, Yao.Blocks, JLD2
# using Yao, Circuit, UnicodePlots, GradOptim, Utils, ArgParse, JLD2, FileIO
import Kernels
# n = 6
# qcbm = QCBM{n, 10}(get_nn_pairs(n))
@load "data.jld" output
layer(::Val{:first}) = rollrepeat(chain(Rx(), Rz()))
layer(::Val{:last}) = rollrepeat(chain(Rz(), Rx()... | {"hexsha": "bc67aa5f11b92aaf0ecdcf38fac248806e1fd317", "size": 612, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "check.jl", "max_stars_repo_name": "Roger-luo/QCBM", "max_stars_repo_head_hexsha": "db44eca3add49f26e2ff70addfe75e97a6684186", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max... |
[STATEMENT]
lemma invar_insert: "invar t \<Longrightarrow> invar (insert xs t)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. invar t \<Longrightarrow> invar (Trie_Map.insert xs t)
[PROOF STEP]
apply(induction xs t rule: insert.induct)
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. \<And>b m. invar (trie_map.Nd... | {"llama_tokens": 354, "file": null, "length": 3} |
[STATEMENT]
lemma enn2real_leD: "\<lbrakk> enn2real x < y; x \<noteq> \<top> \<rbrakk> \<Longrightarrow> x < ennreal y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>enn2real x < y; x \<noteq> \<top>\<rbrakk> \<Longrightarrow> x < ennreal y
[PROOF STEP]
by(cases x)(simp_all add: ennreal_lessI) | {"llama_tokens": 133, "file": "CryptHOL_GPV_Expectation", "length": 1} |
// Boost.Geometry (aka GGL, Generic Geometry Library)
// Unit Test
// Copyright (c) 2010 Alfredo Correa
// Copyright (c) 2010-2012 Barend Gehrels, Amsterdam, the Netherlands.
// Use, modification and distribution is subject to the Boost Software License,
// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy ... | {"hexsha": "86e4ee5ef3e7ea6cbef00c8f6bfbf2d0ce471cb6", "size": 1719, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/libs/geometry/test/geometries/boost_array_as_point.cpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bff... |
__description__ = \
"""
Plot barplot with epistatic coefficients.
"""
__author__ = "Zach Sailer"
import gpmap
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
import matplotlib as mpl
import numpy as np
from scipy.stats import norm as scipy_norm
class Bunch:
... | {"hexsha": "1c3e011942f345472ff9a4ff308d85d8c4906208", "size": 13815, "ext": "py", "lang": "Python", "max_stars_repo_path": "epistasis/pyplot/coefs.py", "max_stars_repo_name": "harmsm/epistasis", "max_stars_repo_head_hexsha": "741b25b3e28015aeeba8d4efc94af1e1d811cd63", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
"""Power Spectrum rebinning in log space, derriving gaussian-like
errors from the standard distribution. All is scaled to log10, which
makes the calculation of the parameters correct. Uses the uncertainty
fitting in fit.py."""
from numpy import log10,zeros,sqrt,abs,arange,concatenate,array,alltrue
from pylab import fi... | {"hexsha": "d71877d3655069253e2ee144a0eb1c865c327d80", "size": 3547, "ext": "py", "lang": "Python", "max_stars_repo_path": "powspec_rebin.py", "max_stars_repo_name": "martindurant/misc", "max_stars_repo_head_hexsha": "96cebe7aac9c417b5afe065099df6b8724578719", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "... |
# numpy官方教程： https://docs.scipy.org/doc/numpy-dev/user/quickstart.html
# numpy官方教程中文翻译： NumPy的详细教程
#1. 创建数组和数组变形
import numpy as np
#
# 创建数组
a = np.array([1,2,3,4,5,6])
print(a)
# 直接给a.shape赋值是最简单的变形方式
a.shape = (2,3)
print('变形之后：')
print(a)
# [1 2 3 4 5 6]
# [[1 2 3]
# [4 5 6]]
a.ravel() # 拉直数组
#array([1, 2, 3, ... | {"hexsha": "3a1cae33280c9eddcba11977a2b383a9c0c93b79", "size": 1732, "ext": "py", "lang": "Python", "max_stars_repo_path": "aura/AI Engineer/course2_20191117/a3_feature_engineering/5_feature_all/numpy_array_math_op.py", "max_stars_repo_name": "linksdl/futuretec-project-coursera_cerficates", "max_stars_repo_head_hexsha"... |
%!TEX root = main.tex
\section{Results}
\label{sec:results}
\begin{table*}[tp] \centering
\begin{minipage}[b]{0.35\textwidth} \centering
\caption{Resource utilization of design and submodules.}
\label{tab:results_resource_utilization}
\begin{tabular}{l r r r r}
\toprule
& LUT & FF & ... | {"hexsha": "199fce5d958255b3fccc61a71f71299dffb2b232", "size": 15060, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/results.tex", "max_stars_repo_name": "pfasante/high-speed_bcrypt", "max_stars_repo_head_hexsha": "5cf57c04c90b3ebd246ddb13ad65312772aa684a", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""Classification methods."""
import numpy as np
from machine_learning.constants import N_CLASSES, FOLDS, MAX_K, RANDOM_SEED
from machine_learning.utilities import k_fold_split_indexes, get_k_nn
def classification(method, error_func, train, test, **kwargs):
"""Perform classification for data and return error.
... | {"hexsha": "d06e42ee0b373f4420c69a853056915fb5aa21df", "size": 8652, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine_learning/classification.py", "max_stars_repo_name": "soikkea/python-algorithms", "max_stars_repo_head_hexsha": "e1e1c3a81d24f80a4c3d7ba5a6b3da3be6a1ea19", "max_stars_repo_licenses": ["MIT"... |
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
from sklearn.datasets import make_blobs
from sklearn.mixture import GaussianMixture
# Set random seed for reproducibility
np.random.seed(1000)
# Total number of samples
nb_samples = 800... | {"hexsha": "08d6d5ee2a493d5f3f944b10366490b383592976", "size": 3934, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter09/gaussian_mixture.py", "max_stars_repo_name": "PacktPublishing/Machine-Learning-Algorithms-Second-Edition", "max_stars_repo_head_hexsha": "2ddacea1c9f81b4ef9a0a51c4230687350afba6c", "max_... |
import numpy as np
def _calcgrlimits1(gr):
meansattrname = 'means_'
try:
from sklearn.mixture import GaussianMixture as GMM
covarsattrname = 'covariances_'
except:
from sklearn.mixture import GMM
covarsattrname = 'covars_'
em = GMM(n_components=3)
em.fit(gr[np.i... | {"hexsha": "4596a6f1ed1b5d1bd37b73ba5463f9c6f607882d", "size": 928, "ext": "py", "lang": "Python", "max_stars_repo_path": "BasicVShale/Algo.py", "max_stars_repo_name": "gcruff/GRIPyPluginsUFF", "max_stars_repo_head_hexsha": "9ea136cedde4b7ae7013fd5cf5816b25923e97e9", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
# ===== VSB Verifications: Velocity Profiles =====
println("=== BEGINNING Verifications4.jl ===")
# === Imports ===
println(" IMPORTING PACKAGES...")
using VSB
using Plots
pyplot()
cd("/Users/Damyn/Documents/BYU/FLOW Lab/VSB/verifications")
# === Geomerty of problem ===
println(" GENERATING GEOMETRY...")
NPTS =... | {"hexsha": "3c3a9a8f71b3dbfb453c3c6fdad92044f8f9ed00", "size": 1790, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "verifications/VSB_Verification4.jl", "max_stars_repo_name": "DamynChipman/VSB.jl", "max_stars_repo_head_hexsha": "9d9ade28383134e1b20953dd85ab0dc8c6188eb8", "max_stars_repo_licenses": ["MIT"], "max... |
// Copyright (c) 2015-2020 Daniel Cooke
// Use of this source code is governed by the MIT license that can be found in the LICENSE file.
#include "vcf_record.hpp"
#include <algorithm>
#include <iterator>
#include <boost/lexical_cast.hpp>
#include "vcf_spec.hpp"
namespace octopus {
// public methods
const Genomic... | {"hexsha": "738056e344fe8ccd4a0c2105686dfbcefd4f71e4", "size": 25632, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/io/variant/vcf_record.cpp", "max_stars_repo_name": "roryk/octopus", "max_stars_repo_head_hexsha": "0ec2839c33b846107278696ee04ce6d7d0f69a54", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/env python3
###############################################################################
# #
# RMG - Reaction Mechanism Generator #
# ... | {"hexsha": "f70ae137d2eb886399cbe83df02b37e5d3c5be8f", "size": 11876, "ext": "py", "lang": "Python", "max_stars_repo_path": "arkane/encorr/ae.py", "max_stars_repo_name": "tza0035/RMG-Py", "max_stars_repo_head_hexsha": "38c49f7107d1b19e4a534408a1040ddd313b8596", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 250... |
from typing import Optional, Union, NamedTuple, Callable, Sequence
from functools import partial
import matplotlib as mpl
import matplotlib.pyplot as plt
import torch
import numpy as np
from .metrics import compute_precisions
class ContactAndAttentionArtists(NamedTuple):
image: mpl.image.AxesImage
contacts: m... | {"hexsha": "be9d6999560c9587a51f8c306f900f4fa07e6ebd", "size": 4950, "ext": "py", "lang": "Python", "max_stars_repo_path": "evo/visualize.py", "max_stars_repo_name": "rmrao/evo", "max_stars_repo_head_hexsha": "ac86e2b8a6f78d5e3a0b8bf47a978a12735ca8c4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7, ... |
__author__ = 'Cameron Summers'
import os
import unittest
import numpy as np
from nps_acoustic_discovery.output import probs_to_pandas, probs_to_raven_detections
from nps_acoustic_discovery.discover import AcousticDetector
from nps_acoustic_discovery.model import EventModel
THIS_DIR = os.path.dirname(os.path.abspath... | {"hexsha": "a5821aeb88b85fdf977da711cb62ca869da9ddda", "size": 3621, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_model.py", "max_stars_repo_name": "scaubrey/nps_acoustic_discovery", "max_stars_repo_head_hexsha": "03bf134f2cff9d2d564b4c9ac211dff9aaaf33a7", "max_stars_repo_licenses": ["CC0-1.0"], "ma... |
using Base: KeySet
#=
keys(::Dict{K,V})::KeySet{Symbol,Dict{Symbol,Any}} # KeySet for a Dict{Symbol,Any}
values(::Dict{K,V})::ValueIterator{Dict{Symbol,Any}} # ValueIterator for a Dict{Symbol,Any}
=#
(getkeys(dict::Dict{K,V})::Array{Symbol,1}) where {K,V} = dict.keys[getslotidxs(dict)]
(getvalues(dict... | {"hexsha": "462110f22ed9652a38a7342e2b9dd4a97be205c8", "size": 512, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/support/dict.jl", "max_stars_repo_name": "JuliaTagBot/GUI.jl", "max_stars_repo_head_hexsha": "b2ab393c6286ede8d0b7d00929098f99699de226", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4,... |
-- Andreas, 2012-04-18, bug reported by pumpkingod on 2012-04-16
module Issue610 where
import Common.Level
open import Common.Equality
data ⊥ : Set where
record ⊤ : Set where
record A : Set₁ where
constructor set
field
.a : Set
.get : A → Set
get x = helper x
module R where
helper : .A -> Set
helper x... | {"hexsha": "cce06a4dfb29c4c752263e32910325ba00270fe3", "size": 600, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Fail/Issue610.agda", "max_stars_repo_name": "redfish64/autonomic-agda", "max_stars_repo_head_hexsha": "c0ae7d20728b15d7da4efff6ffadae6fe4590016", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
\documentclass[11pt]{article}
\usepackage{setspace}
\usepackage{pxfonts}
\usepackage{graphicx}
\usepackage{geometry}
\geometry{letterpaper,left=.5in,right=.5in,top=1in,bottom=.75in,headsep=5pt,footskip=20pt}
\title{Lecture 4 -- Hodgkin-Huxley neuron model}
\author{Computational Neuroscience Summer Program}
\date{June... | {"hexsha": "11e7639b8022769e4c251473b3c00b20c7361f90", "size": 12256, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "hodgkin_huxley_simple/hodgkin_huxley_simple_lecture.tex", "max_stars_repo_name": "ContextLab/computational-neuroscience", "max_stars_repo_head_hexsha": "b0a3812a46fe4387de2655a9072f8910a7f212f3", "... |
SUBROUTINE READ1 (DM,MR,SCR1,SCR2,SCR3,PHIA,USET,NR1,LAMA,SCR4)
C
INTEGER DM,MR,IMR(7),SYSBUF,SCR1,SCR2,ISCR1(7),PHIA,
1 SCR4,SCR3,NAM(2)
DOUBLE PRECISION DCORE(1),SI,TERM
CHARACTER UFM*23
COMMON /XMSSG / UFM
COMMON /SYSTEM/ SYSBUF,NOUT,K... | {"hexsha": "b5880cf68962c4f74bc1ec2b8be7d007b2dd1a5d", "size": 5128, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/read1.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": 1... |
// Copyright 2017 Antony Polukhin.
//
// 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)
#include <boost/stacktrace/detail/void_ptr_cast.hpp>
#include <boost/core/lightweight_test.hpp>
int foo1_func(int... | {"hexsha": "10828fac66e771a8935c7469f70d1a050f3a63e9", "size": 2180, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/stacktrace/test/test_void_ptr_cast.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e188fe871... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from numpy import linalg as LA
from deeprobust.image.attack.base_attack import BaseAttack
class FGSM(BaseAttack):
def __init__(self, model, device = 'cuda'):
super(FGSM, self).__init__(mode... | {"hexsha": "8a9402e51d7a46bceedbeac9087c90e05a29fa15", "size": 2179, "ext": "py", "lang": "Python", "max_stars_repo_path": "deeprobust/image/attack/fgsm.py", "max_stars_repo_name": "HenryKenlay/DeepRobust", "max_stars_repo_head_hexsha": "ea8871d970257a9c11715cd059a5331177a00395", "max_stars_repo_licenses": ["MIT"], "ma... |
#!/usr/bin/python3
import os
import sys
import json
import csv
import numpy as np
_dir = sys.argv[1]
output_file = sys.argv[2]
config_fp = open(os.path.join(_dir, "list.json"), "rb")
json_str = config_fp.read()
config_fp.close()
config = json.loads(json_str.decode())
fp = open(os.path.join(_dir, output_file), 'wb'... | {"hexsha": "52fcf4147113b2a221d8dfe97ad7c0e095a0c9c8", "size": 1462, "ext": "py", "lang": "Python", "max_stars_repo_path": "RsNet/verify_models_cat.py", "max_stars_repo_name": "gehuangyi20/random_spiking", "max_stars_repo_head_hexsha": "c98b550420ae4061b9d47ca475e86c981caf5514", "max_stars_repo_licenses": ["MIT"], "max... |
import os
import sys
curDir = os.path.dirname(__file__)
sys.path.append('{0}/../scripts/'.format(curDir))
import pandas as pd
import numpy as np
from indicators import Indicators
from auto_sklearn_model import AutoSklearnModel
# start = pd.to_datetime('2012-01-01')
# end = datetime.date.today()
# ind_obj = Indicators... | {"hexsha": "a6b43f104fbd191e4343d63cde452ac21f8a3571", "size": 1389, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/test.py", "max_stars_repo_name": "jaeminsung/ml_stock_trading", "max_stars_repo_head_hexsha": "32f5305f5111ba86eb428e982ac6f0f18842558d", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
####### UTILITIES
import os
import numpy as np
import random
import torch
# random sequences
def randomly(seq):
shuffled = list(seq)
random.shuffle(shuffled)
return iter(shuffled)
# voting ensemble
def convert_to_10(a):
idx = a.argmax(axis = 1)
out = np.zeros_like(a,dtype = float)
out[np.ara... | {"hexsha": "bfc674ec0867c7e509a89aecbcb49028891dabf7", "size": 1046, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/utilities.py", "max_stars_repo_name": "lizzzi111/Kaggle_Leaf_Disease_Classification", "max_stars_repo_head_hexsha": "0ff3904164e81cb03ef0054d3574ac5b6cb3d897", "max_stars_repo_licenses": ["M... |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-2020, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
Geometry
======================
Functions for constructing molecular and solid state geometries with
symmetry adapted or crystalline structures.
"""
import numpy as np
import pan... | {"hexsha": "b6aed729e65029bd425e9b8640adf395f3c47d71", "size": 8234, "ext": "py", "lang": "Python", "max_stars_repo_path": "exatomic/algorithms/geometry.py", "max_stars_repo_name": "herbertludowieg/exatomic", "max_stars_repo_head_hexsha": "d177781a649ba3a12e5c1147672767ac4a388a6c", "max_stars_repo_licenses": ["Apache-2... |
\section{Summary}
\label{sec:summary}
It has been demonstrated that for the case of a high energy physics
event selection application, a drone neural network is able to accurately
approximate and learn the features of a neural network with a different
structure. The proposed algorithm design allows the drone to learn ... | {"hexsha": "7ca5685dc40a253be9246bdcb0cca4f38eb8c806", "size": 887, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "documents/paper/summary.tex", "max_stars_repo_name": "Tevien/NNDrone", "max_stars_repo_head_hexsha": "76dce457324ea03a8757d74f6403fbf60132294b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import pickle
import gzip
from sparse_gp import SparseGP
import scipy.stats as sps
import numpy as np
import sys
import os
sys.path.append('%s/../prog_common' % os.path.dirname(os.path.realpath(__file__)))
from cmd_args import cmd_args
gold_prog_list = []
with open('%s/../prog_data/gold_prog.txt' % os.path.dirname... | {"hexsha": "8885193a5db8179aa1b7379a3e6fd2d920f97dbb", "size": 3581, "ext": "py", "lang": "Python", "max_stars_repo_path": "prog_vae/sparse_gp_regression/regression.py", "max_stars_repo_name": "Hanjun-Dai/sdvae", "max_stars_repo_head_hexsha": "bd26ea949c496419634fd2cf4802fc8e19a9194c", "max_stars_repo_licenses": ["MIT"... |
from openpathsampling.engines.toy.pes import PES, PES_Add, OuterWalls, Gaussian
import numpy as np
class DoublewellPotential(PES_Add):
def __init__(self):
super(DoublewellPotential, self).__init__(
OuterWalls([1.0, 1.0], [0.0, 0.0]),
PES_Add(
Gaussian(-0.7, [7.5, 7.... | {"hexsha": "61d6f14d9e351464290ae22e0538cf23bb76d330", "size": 3848, "ext": "py", "lang": "Python", "max_stars_repo_path": "NucleationModel/potentials.py", "max_stars_repo_name": "MFrassek/CommittorEAE", "max_stars_repo_head_hexsha": "88a467e4500bc9ab69834209f4eaec9f2d0d7a61", "max_stars_repo_licenses": ["MIT"], "max_s... |
include "bug-2601829-mid.h"
end
| {"hexsha": "1e950eba4bf939eef9d4a2e69894e38d13599955", "size": 33, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/bug-reports-sf/bug-2601829.f90", "max_stars_repo_name": "OpenFortranProject/ofp-sdf", "max_stars_repo_head_hexsha": "202591cf4ac4981b21ddc38c7077f9c4d1c16f54", "max_stars_repo_licenses": ["B... |
import scannertools as st
from scannertools.prelude import *
from scipy.spatial import distance
import numpy as np
from typing import Sequence
import pickle
WINDOW_SIZE = 500
BOUNDARY_BATCH = 10000000
POSITIVE_OUTLIER = 2.5
NEGATIVE_OUTLIER = 1.0
@scannerpy.register_python_op(name='ColorHistogramShotLabels', batch=BO... | {"hexsha": "e6551c8fa9c1d373eea6378db05114407a7f0ee0", "size": 2097, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/esper/shot_labeling_functions.py", "max_stars_repo_name": "DanFu09/esper", "max_stars_repo_head_hexsha": "ccc5547de3637728b8aaab059b6781baebc269ec", "max_stars_repo_licenses": ["Apache-2.0"], ... |
MODULE maneig_I
INTERFACE
!...Generated by Pacific-Sierra Research 77to90 4.3E 14:07:38 1/ 5/07
!...Modified by Charlotte Froese Fischer
! Gediminas Gaigalas 10/05/17
SUBROUTINE maneig (IATJPO, IASPAR)
INTEGER, INTENT(OUT) :: IATJPO
INTEGER, INTENT(OUT) :: IASPAR
... | {"hexsha": "ea7093d32298cb9e0a0632ae8a758503db0cef01", "size": 377, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/appl/rci90/maneig_I.f90", "max_stars_repo_name": "sylas/grasp-continuum", "max_stars_repo_head_hexsha": "f5e2fb18bb2bca4f715072190bf455fba889320f", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Python > Numpy > Floor, Ceil and Rint
# Use the floor, ceil and rint tools of NumPy on the given array.
#
# https://www.hackerrank.com/challenges/floor-ceil-and-rint/problem
#
import numpy
if numpy.version.version >= '1.14.':
numpy.set_printoptions(legacy='1.13')
a = numpy.array(input().split(), numpy.float)
p... | {"hexsha": "ca04715993538c653d8e18a3105341acf80d7cce", "size": 383, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/numpy/floor-ceil-and-rint.py", "max_stars_repo_name": "PingHuskar/hackerrank", "max_stars_repo_head_hexsha": "1bfdbc63de5d0f94cd9e6ae250476b4a267662f2", "max_stars_repo_licenses": ["Unlicens... |
"""
plot_horizontal_cross_section_from_netcdf.py: plot the horizontal cross section from the netcdf model file.
"""
import cartopy
import cartopy.crs as ccrs
import click
import matplotlib.pyplot as plt
import numpy as np
from cartopy.mpl.ticker import LatitudeFormatter, LongitudeFormatter
from netCDF4 import Dataset
... | {"hexsha": "3895d668fc157ffd88a141e886c83a49c414d651", "size": 4306, "ext": "py", "lang": "Python", "max_stars_repo_path": "seisflow/scripts/plot/plot_horizontal_cross_section_from_netcdf.py", "max_stars_repo_name": "ziyixi/seisflow", "max_stars_repo_head_hexsha": "722c2445f4a5316f42bfbc8b9010d31caad4c76e", "max_stars_... |
import galsim
import numpy
import os
class CosmosSampler(object):
_req_params = {}
_opt_params = { 'min_r50' : float, 'max_r50': float,
'min_flux' : float, 'max_flux': float,
'kde_factor' : float }
_single_params = []
_takes_rng = True # It doesn't actually need an... | {"hexsha": "9e2c48afb0e03164460d34f92e13ab4b089195d6", "size": 4855, "ext": "py", "lang": "Python", "max_stars_repo_path": "galsim_extra/cosmos_sampler.py", "max_stars_repo_name": "esheldon/galsim_extra", "max_stars_repo_head_hexsha": "1e93a35db16943f6ce0251cddcd253defe0d5c61", "max_stars_repo_licenses": ["BSD-3-Clause... |
from environments import rlgymenv
import policyopt
from policyopt import SimConfig, rl, util, nn, tqdm
import gym
import numpy as np
import argparse
def main():
np.set_printoptions(suppress=True, precision=5, linewidth=1000)
parser = argparse.ArgumentParser()
parser.add_argument('env', type=str)
pars... | {"hexsha": "62cabef574216bb87fcd466971937d805c300369", "size": 2031, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/evaluate_random_policy.py", "max_stars_repo_name": "Santara/RAIL", "max_stars_repo_head_hexsha": "f36998f4852d274132490cd16ccda7e8839888f7", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
Describe Users/StephHolm here.
20110807 15:27:19 nbsp Welcome to the Wiki! I saw your comment on Taste of Thai page, and I have to say KetMoRee across has free Thai Ices Tea refills, and has had it since Day One. Users/NikhilDahal
20120125 11:41:35 nbsp Hey, neat idea for a page! Ive been a fan of Tumbleweed for m... | {"hexsha": "8cbc147bf9f8ec223cf48a13ff64dad4b12abe76", "size": 15118, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/StephHolm.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import modeling
import optimization
import tokenization
import tensorflow as tf
import regex
import numpy as np
flags = tf.flags
FLAGS = flags.FLAGS
##... | {"hexsha": "a5d07d2732f7ea3966ad40c0c6539a3d5ce87876", "size": 27533, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_seq.py", "max_stars_repo_name": "quark-tech/quark_cws", "max_stars_repo_head_hexsha": "9105fbb4648ed7939456db45a9692ec849f4d9a8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
theory Cell_Decomp_Theorem_Helpers
imports Denef_Lemma_2_4 Denef_Lemma_2_3 Algebras_of_Cells
begin
locale common_decomp_proof_context = denef_I + denef_II
locale common_refinement_locale = common_decomp_proof_context +
fixes \<C> A c a1 a2 I f m
assumes f_closed: "f \<in> carrier (UP (SA m))"
assumes f_deg: " ... | {"author": "Aaroncri", "repo": "Macintyre-Theorem-in-Isabelle", "sha": "a13c75b5d3fc12fb3290b7c92326e13618612c6c", "save_path": "github-repos/isabelle/Aaroncri-Macintyre-Theorem-in-Isabelle", "path": "github-repos/isabelle/Aaroncri-Macintyre-Theorem-in-Isabelle/Macintyre-Theorem-in-Isabelle-a13c75b5d3fc12fb3290b7c92326... |
# Authors: Stephane Gaiffas <stephane.gaiffas@gmail.com>
# License: BSD 3 clause
import numpy as np
from scipy.linalg import toeplitz
from scipy.special import expit
from sklearn.datasets import make_classification
import pytest
def simulate_true_logistic(
n_samples=150,
n_features=5,
fit_intercept=True,... | {"hexsha": "8df35c47565a43d57a5d13fd28554d9e22cf9076", "size": 4782, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/utils.py", "max_stars_repo_name": "LinLearn/linlearn", "max_stars_repo_head_hexsha": "de5752d47bbe8e2fb62d41b0dcf2526f87545e1c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
import sys, os, time, uuid, random, multiprocessing, traceback
PY2 = sys.version_info < (3,)
PY3 = sys.version_info >= (3,)
if PY2:
import cPickle as pickle
else:
import pickle
import numpy as np
from striped.pythreader import PyThread, Primitive, synchronized
from threading import Event
import socket, traceback, ... | {"hexsha": "1b9195adaef90c73152400dfe856ba9927b3e41e", "size": 13561, "ext": "py", "lang": "Python", "max_stars_repo_path": "job_server/Contract.py", "max_stars_repo_name": "ivmfnal/striped", "max_stars_repo_head_hexsha": "eef1a4d544fa1b97fde39d7ee5ef779071218891", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
function varargout = scl_slope(varargin)
% file_array's scl_slope property
% For getting the value
% dat = scl_slope(obj)
%
% For setting the value
% obj = scl_slope(obj,dat)
%__________________________________________________________________________
% Copyright (C) 2005-2017 Wellcome Trust Centre for Neuroimaging
%
%... | {"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/@file_array/private/scl_slope.m"} |
program sesh
! SESH MAIN PROGRAM
!
! MANUAL: F.H. FROEHNER,
! "SESH - A FORTRAN IV CODE FOR CALCULATING THE SELF-
! SHIELDING AND MULTIPLE SCATTERING EFFECTS FOR
! NEUTRON CROSS SECTION DATA INTERPRETATION
! IN THE UNRESOLVED RESONANCE REGION",
! ... | {"hexsha": "2affe72ee8f4086375d8d4f6ebe92b4132d2d92a", "size": 26769, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/sesh.f90", "max_stars_repo_name": "brownjm1968/sesh", "max_stars_repo_head_hexsha": "6ae09fffedecd7f36e46392da75ca56004a74f26", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
*----------------------------------------------------------------------*
subroutine prop_evaluate(ndens,rank,label_den,trplt,
& env_type,op_info,str_info,orb_info)
*----------------------------------------------------------------------*
*
* for a given list of densities (all have rank "rank") evaluat... | {"hexsha": "0e34051df38e3b171bf870aab81dec0907a4d07c", "size": 2145, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "solve/prop_evaluate.f", "max_stars_repo_name": "ak-ustutt/GeCCo-public", "max_stars_repo_head_hexsha": "8d43a6c9323aeba7eb54625b95553bfd4b2418c6", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# License: MIT
# Author: Karl Stelzner
import os
import sys
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
from numpy.random import random_integers
from PIL import Image
from torch.utils.data._utils.collate import default_collate
import json
def progres... | {"hexsha": "ec32efa0e5e4c32a4aa33caf37b12b35cdac995d", "size": 13647, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/datasets.py", "max_stars_repo_name": "GpNico/Relations_MONet", "max_stars_repo_head_hexsha": "a9c6cabc8f34819af15f55e9f7203a9e055d351d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy
i = 122
| {"hexsha": "79bc8127c0a8f6177f933f1c0994f76f469c9ea0", "size": 21, "ext": "py", "lang": "Python", "max_stars_repo_path": "1.py", "max_stars_repo_name": "fxphero/FIRST", "max_stars_repo_head_hexsha": "6350eca5a34c748e8d7f483c455be82966ae9d52", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_star... |
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import numpy as np
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import mlflow
import mlflow.sklearn
... | {"hexsha": "a89ab77638e16e06f55ec970594a42224c26a953", "size": 1770, "ext": "py", "lang": "Python", "max_stars_repo_path": "how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-remote/train_diabetes.py", "max_stars_repo_name": "lobrien/MachineLearningNotebooks", "max_stars_repo_head_hexsha": "a56b69448c0... |
import tensorflow as tf
import numpy as np
import os
# Utility functions to apply data augmentations.
# some of the functions directly borrowed from https://www.wouterbulten.nl/blog/tech/data-augmentation-using-tensorflow-data-dataset/
def flip(x):
"""Flip augmentation
Args:
x: Image to flip
R... | {"hexsha": "6048b3782becf547b8d21e3a2d8a318447d7386d", "size": 3083, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "monatis/aiplatform-workshop", "max_stars_repo_head_hexsha": "946ea6f931da1cd8f8e51f78d025d51b1a842ec7", "max_stars_repo_licenses": ["WTFPL"], "max_stars_count": ... |
# Copyright 2019 The TensorNetwork Authors
#
# 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 ... | {"hexsha": "4ff9216c569a741ad4097d5083124bf17c334264", "size": 5201, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensornetwork/backends/pytorch/pytorch_backend.py", "max_stars_repo_name": "esgantivar/TensorNetwork", "max_stars_repo_head_hexsha": "bf7092a677136356ee8bd48bc963cde3490ae1c7", "max_stars_repo_lic... |
[STATEMENT]
lemma TBOUNDD: "TBOUND m t \<Longrightarrow> time m h \<le> t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. TBOUND m t \<Longrightarrow> time m h \<le> t
[PROOF STEP]
by (auto simp: TBOUND_def) | {"llama_tokens": 87, "file": "Van_Emde_Boas_Trees_Time_Reasoning_Time_Reasoning", "length": 1} |
function chi2 = chi_squared(y,fit,P,eb)
% returns *reduced* chi^2 value for use in data modelling
% "y" is a vector of data, "fit" is a vector of model values (size(fit)=size(y)), P is the number of
% parameters fit in the model, and eb is a vector of error bars (1-to-1 correspondnce with y)
% Ref: John R. Taylor, "An... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/1049-chisquared-m/chi_squared.m"} |
import numpy as np
import pandas as pd
import itertools as it
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_error
import funciones as f
from InputsRevolvente import InputsRevolvente
lista_nombres=['TSN']
ruta_real=['/Users/renzomartinch/Downloads/Comite_0622/TSN_Reales.csv']
ruta_teorico=['... | {"hexsha": "e09c479471cd0901796b0cb9d9cf4984880939d4", "size": 1312, "ext": "py", "lang": "Python", "max_stars_repo_path": "ExtraccionRev.py", "max_stars_repo_name": "joelortizlt/Monitoreo", "max_stars_repo_head_hexsha": "31434871ec2e9345fb471fdc5ffcdf8737ff7b6d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import pandas as pd
import numpy as np
from sklearn.linear_model import ARDRegression
from sklearn.linear_model import HuberRegressor
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.svm import SVR
def get_a... | {"hexsha": "4cda04bae2f36be16ae87df87d96c8966dd1d814", "size": 2406, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw/scikit-regression/regression_runner.py", "max_stars_repo_name": "petrroll/npfl104", "max_stars_repo_head_hexsha": "241646b02e91c14ac885dd6cc981b5bb63d4561c", "max_stars_repo_licenses": ["MIT"],... |
(*
* Copyright 2014, General Dynamics C4 Systems
*
* This software may be distributed and modified according to the terms of
* the GNU General Public License version 2. Note that NO WARRANTY is provided.
* See "LICENSE_GPLv2.txt" for details.
*
* @TAG(GD_GPL)
*)
theory LevityCatch
imports
Include
"../../..... | {"author": "SEL4PROJ", "repo": "jormungand", "sha": "bad97f9817b4034cd705cd295a1f86af880a7631", "save_path": "github-repos/isabelle/SEL4PROJ-jormungand", "path": "github-repos/isabelle/SEL4PROJ-jormungand/jormungand-bad97f9817b4034cd705cd295a1f86af880a7631/case_study/l4v/proof/refine/ARM/LevityCatch.thy"} |
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 7 22:58:58 2016
@author: isaacdk
"""
from __future__ import division, print_function
import matplotlib.pyplot as plt
import numpy as np
from scipy import interpolate
import scipy.optimize
#get data file
filename= 'scope2.csv'
xaxis_label = 'Time (s)'
yaxis_label = 'Volt... | {"hexsha": "44c9a78743465fd9c5907b094a6583c066a25905", "size": 3540, "ext": "py", "lang": "Python", "max_stars_repo_path": "lorentz_clean.py", "max_stars_repo_name": "flamingh2o/236_py", "max_stars_repo_head_hexsha": "6c7da0f4bd0724b94a180630ab53d357a8db2e4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import sys
sys.path.append("..")
import scipy
import numpy as np
from numpy.linalg import matrix_rank, matrix_power, cholesky, inv
import torch
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import util.geometry_util as geo_util
from solvers.rigidity_solver.gradient import gradient_analysis
fr... | {"hexsha": "ca69a752dfd8694e331f6bff0afbc38bbf31875d", "size": 2322, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/optimization/tetrahedron_top_plane.py", "max_stars_repo_name": "Anthony102899/Lego-ImageGenerator", "max_stars_repo_head_hexsha": "52b19c8bb20f77a3394675e7c037c943a50c1e15", "max_stars_repo_... |
"""
Process iMaterialist Fashion 2019 https://www.kaggle.com/c/imaterialist-fashion-2019-FGVC6
"""
import argparse
import shutil
from pathlib import Path
import cv2
import numpy as np
import pandas as pd
from PIL import Image
from tqdm import tqdm
from iglovikov_helper_functions.utils.mask_utils import rle2mask
de... | {"hexsha": "403794f67f3a6ec6766bb7fcc1d8894cbfffb2dc", "size": 1788, "ext": "py", "lang": "Python", "max_stars_repo_path": "iglovikov_helper_functions/data_processing/prepare_cloths_segmentation/prepare_imaterialist2019.py", "max_stars_repo_name": "AIChuY/iglovikov_helper_functions", "max_stars_repo_head_hexsha": "4638... |
using Geotherm.Geometry: Point2D
using Geotherm.Integrate: runge_kutta_iter, runge_kutta
@testset "Test `runge_kutta`" begin
@test runge_kutta_iter(
Point2D(1.0, 1.0),
(x, y) -> x * 2 + y * 3,
) == Point2D(1.01, 1.05085856375)
@test runge_kutta(
Point2D(1.0, 1.0),
(x, y) -> ... | {"hexsha": "8a51fcf5a00ab252beba3a7ae2e30703877f83aa", "size": 530, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Integrate.jl", "max_stars_repo_name": "MineralsCloud/Geotherm.jl", "max_stars_repo_head_hexsha": "6c26803989558d8a9fe5f61c36c29dbdce234ade", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
test:main:zero
test:main:one:1
test:main:two:2:3
test:main:three:4:5:7
test:main:four:7:8:9:10
test:main:five:11:12:13:14:15
test:main:six:16:17:18:19:20:21
test:main:seven:22:23:24:25:26:27:28
test:main:eight:29:30:31:32:33:34:35:36
test:main:nine:37:38:39:40:41:42:43:44:45
test:main:ten:46:47:48:49:50:51:52:53:54:55
... | {"hexsha": "db6d18cb0541373e26dfd0dd42759ee032a3921a", "size": 415, "ext": "r", "lang": "R", "max_stars_repo_path": "test/unittest/usdt/tst.allargs.r", "max_stars_repo_name": "alan-maguire/dtrace-utils", "max_stars_repo_head_hexsha": "53b33a89ef7eaeba5ce06d50a4c73fe91c1fa99e", "max_stars_repo_licenses": ["UPL-1.0"], "m... |
using Pkg
Pkg.activate(".")
using Optim
using ForwardDiff
using JLD
using LineSearches
using AdvancedMH
using AdaptiveMCMC
using MCMCChains
using Distributions
using StatsBase
using Base.Threads
nthreads()
using Printf
using Plots
using Plots.PlotMeasures
using LaTeXStrings
using Distributions
using StatsPlots
@ti... | {"hexsha": "7c17fd7ca5cdd9d37d6a9e0daa492a057cfa45ca", "size": 15848, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/inference_example.jl", "max_stars_repo_name": "michael-c-brennan/DFXMTools", "max_stars_repo_head_hexsha": "bf0bdef30045d0b8e05094e74243caaa28651d0e", "max_stars_repo_licenses": ["MIT"], "ma... |
function [w,run] = train_bfgs(x,w,lambda)
% TRAIN_BFGS Train a logistic regression model by BFGS.
%
% W = TRAIN_BFGS(X,W) returns maximum-likelihood weights given data and a
% starting guess.
% Data is columns of X, each column already scaled by the output (+1 or -1).
% W is the starting guess for the parameters (a ... | {"author": "FuzhenZhuang", "repo": "Transfer-Learning-Toolkit", "sha": "24b5323b354aee844b8b7df9fcad17fdfb191dc4", "save_path": "github-repos/MATLAB/FuzhenZhuang-Transfer-Learning-Toolkit", "path": "github-repos/MATLAB/FuzhenZhuang-Transfer-Learning-Toolkit/Transfer-Learning-Toolkit-24b5323b354aee844b8b7df9fcad17fdfb19... |
All Boy Scouts Boy Scout Troops and Venture Crews in Davis cooperate every year to run the Boy Scout Christmas Tree Lot http://davischristmastrees.com/. For decades the lot was downtown at the Boy Scout Cabin, but after leaving in 2002 they moved the lot to Madson Place next to Center City Automotive Inc who also donat... | {"hexsha": "e1835c0236e02a3d15be838769da1996c271f6f5", "size": 721, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Boy_Scout_Christmas_Tree_Lot.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "... |
from conformer_rl.utils import chem_utils
import numpy as np
def test_tfd_matrix(mocker):
tf = mocker.patch('conformer_rl.utils.chem_utils.TorsionFingerprints')
tf.GetTFDMatrix.return_value = [3, 5, 7, 9, 11, 13, 15, 17, 19, 21]
mat = chem_utils.tfd_matrix('mol')
assert np.array_equal(mat, np.array(
... | {"hexsha": "c8ea54d6ef121dbc9058df6f66c5f5c54b1f1431", "size": 464, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/utils/test_chem_utils.py", "max_stars_repo_name": "ZimmermanGroup/conformer-rl", "max_stars_repo_head_hexsha": "beb98cbee6ba6efba686d7c6eebbf33fd737f279", "max_stars_repo_licenses": ["MIT"], ... |
\documentclass[a4paper]{article}
%import packages
\usepackage[utf8]{inputenc}
\usepackage{graphicx}
\usepackage{wrapfig}
\usepackage{float}
\usepackage{listings}
\usepackage{amsmath}
\usepackage{epigraph}
\usepackage{multicol}
\usepackage[a4paper, total={7in, 8in}]{geometry}
%define variables
\newcommand{\projectname... | {"hexsha": "a6fc52c4d9bda6124ec4fcd2749f1dc106e986f4", "size": 22312, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "0xchan_whitepaper.tex", "max_stars_repo_name": "n4n0GH/0xchan-whitepaper", "max_stars_repo_head_hexsha": "c973540b75542174fc10f3d473f9487f53bc460b", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/-
Copyright (c) 2022 Jujian Zhang. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Jujian Zhang, Scott Morrison
! This file was ported from Lean 3 source module category_theory.abelian.injective_resolution
! leanprover-community/mathlib commit 956af7c76589f444f2e13139... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/CategoryTheory/Abel... |
from sympy import *
x = Symbol('x')
#x**4-1/3*x**3-3/2*x**2
f = 1/2*x**2+1/4*x**4-1/2*x**2
fx = lambdify(x, f, modules=['numpy'])
df = diff(fx(x), x)
dfx = lambdify(x, df, modules=['numpy'])
raiz_primeira_df = solve(df)
segunda_df = diff(dfx(x), x)
seg_dfx = lambdify(x, segunda_df, modules=['numpy'])
print(f'Raizes ... | {"hexsha": "fecde5d26ee964f03b530d2d6828aafa49e55934", "size": 1063, "ext": "py", "lang": "Python", "max_stars_repo_path": "auxilia/Max_Min_calc.py", "max_stars_repo_name": "HigorAnjos/Fundamentos-VI", "max_stars_repo_head_hexsha": "e0aa3cb37e4c54d24ac7123ea3bd8038196e0edb", "max_stars_repo_licenses": ["MIT"], "max_sta... |
using OrdinaryDiffEq, ParameterizedFunctions, Plots, LSODA, DiffEqDevTools, Sundials
using LinearAlgebra
LinearAlgebra.BLAS.set_num_threads(1)
gr() #gr(fmt=:png)
f_hires = @ode_def Hires begin
dy1 = -1.71*y1 + 0.43*y2 + 8.32*y3 + 0.0007
dy2 = 1.71*y1 - 8.75*y2
dy3 = -10.03*y3 + 0.43*y4 + 0.035*y5
dy4 = 8.32*y... | {"hexsha": "dc99957b348a965d96c00b54a1851cba00490ccf", "size": 1591, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "diagrams/hires.jl", "max_stars_repo_name": "ordicker/OrdinaryDiffEq.jl", "max_stars_repo_head_hexsha": "6fdc99fa4da79633e0161f0bb8aaff3f39cd39bc", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
!
! Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
! See https://llvm.org/LICENSE.txt for license information.
! SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
!
! check that loops in containing routines don't update counter in container
program p
integer i,j,k
integer result(... | {"hexsha": "ed5b9021ad2bff2a0dabd13c1640c28d424cf19b", "size": 748, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/f90_correct/src/ip12.f90", "max_stars_repo_name": "abrahamtovarmob/flang", "max_stars_repo_head_hexsha": "bcd84b29df046b6d6574f0bfa34ea5059092615a", "max_stars_repo_licenses": ["Apache-2.0"]... |
from typing import Any, Optional, Callable, Tuple, List
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
from defaults import *
winit_funcs = {
'normal':nn.init.normal_,
'uniform':nn.init.uniform_,
'xavier-normal':nn.init.xavier_normal_,
'xavier-uniform':nn.init.... | {"hexsha": "183954e2a7128704e334fcfefdd2ca7843409ea4", "size": 14216, "ext": "py", "lang": "Python", "max_stars_repo_path": "mapnet/model.py", "max_stars_repo_name": "ForrestCKoch/MAPnet", "max_stars_repo_head_hexsha": "2d49f20ce3c29f6b97a5ae6260bea949e6e685b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
<a href="https://colab.research.google.com/github/aidenaislinn/python-for-text-analysis/blob/master/Kopie_van_glm.ipynb" target="_parent"></a>
# Neuroimaging week 2: modeling fMRI with the GLM
This week will be all about how most fMRI analyses are done: using the **GLM**.
We'll use example data for this notebook, the... | {"hexsha": "eccea2b739e090cc6f6c1e8883c3e2b957ed3be7", "size": 165919, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Kopie_van_glm.ipynb", "max_stars_repo_name": "aidenaislinn/python-for-text-analysis", "max_stars_repo_head_hexsha": "e27fb4a0a982246d2f5c7e9b40de9140f7956cd3", "max_stars_repo_licen... |
[STATEMENT]
lemma is_subprob_densityI[intro]:
"\<lbrakk>f \<in> borel_measurable M; \<And>x. x \<in> space M \<Longrightarrow> f x \<ge> 0; space M \<noteq> {}; (\<integral>\<^sup>+x. f x \<partial>M) \<le> 1\<rbrakk>
\<Longrightarrow> is_subprob_density M f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1... | {"llama_tokens": 384, "file": "Density_Compiler_Density_Predicates", "length": 2} |
import numpy as np
from utils.load_config import load_config
from models.RBF import RBF
"""
small script to test the RBF 2d convolution
run: python -m tests.RBF.t01_2d_rbf
"""
# define configuration
config_path = 'RBF_t01_2d_m0001.json'
# load config
config = load_config(config_path, path='configs/RBF')
data = np.z... | {"hexsha": "c24b23cf5dbde150ecf3b722ab72bc3a9cf2dcaa", "size": 906, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/RBF/t01_2d_rbf.py", "max_stars_repo_name": "michaelStettler/BVS", "max_stars_repo_head_hexsha": "947f1e505ccc3ebcf1926c8f52924d823bd1e101", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# (GEM) iJR904 (Reed et al., 2003) (download link: https://darwin.di.uminho.pt/models)
# TODO: change to alternative link!!!
# (GEM) Alternative link http://bigg.ucsd.edu/static/models/iJR904.mat
module Chemostat_Heerden2013
import Chemostat
const Ch = Chemostat
import UtilsJL
const UJL = UtilsJL
... | {"hexsha": "f358a8a82a574f0c5fb3d868dfef414ffbd83b76", "size": 618, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Chemostat_Heerden2013.jl", "max_stars_repo_name": "josePereiro/Chemostat_Heerden2013.jl", "max_stars_repo_head_hexsha": "00f97a57a64daf8d175b2eb22871c5fc83d2e6f0", "max_stars_repo_licenses": ["M... |
#!/usr/bin/env python
# coding: utf-8
# $\newcommand{\mb}[1]{\mathbf{ #1 }}$
# $\newcommand{\bs}[1]{\boldsymbol{ #1 }}$
# $\newcommand{\bb}[1]{\mathbb{ #1 }}$
#
# $\newcommand{\R}{\bb{R}}$
#
# $\newcommand{\ip}[2]{\left\langle #1, #2 \right\rangle}$
# $\newcommand{\norm}[1]{\left\Vert #1 \right\Vert}$
#
# $\newcomm... | {"hexsha": "4731a6e0e41551b3e94aafab36c006e655748843", "size": 15733, "ext": "py", "lang": "Python", "max_stars_repo_path": "working_files/bkeedmd/bilinearizable_sys_mpc_nn.py", "max_stars_repo_name": "Cafolkes/koopman_learning_and_control", "max_stars_repo_head_hexsha": "0152a2bd5079da4d672dbaac404b6c084410297d", "max... |
# SPDX-License-Identifier: Apache-2.0
"""
tf2onnx.tfjs_utils - utilities for parsing tfjs files into onnx graphs
Main functions of interest are graphs_from_tfjs and read_tfjs_graph
"""
import json
import os
import base64
import gzip
import struct
import logging
from onnx import numpy_helper, helper
import numpy as... | {"hexsha": "58fdd5d38f3b36c794dfc11e74a866e4c03d72f2", "size": 19828, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf2onnx/tfjs_utils.py", "max_stars_repo_name": "guschmue/tensorflow-onnx", "max_stars_repo_head_hexsha": "186b9540d705188de34faffd119aa6a4f5b150c0", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
from pathlib import Path
from tempfile import TemporaryDirectory
import json
import os
import re
import unittest
from pandas import DataFrame
from schematics.exceptions import DataError, ValidationError
from schematics.models import Model
from schematics.types import StringType
import numpy as np
import OpenEXR as ope... | {"hexsha": "7ef728c534c4d483dad53cfc864e86200ec5f178", "size": 10710, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/hidebound/core/tools_test.py", "max_stars_repo_name": "theNewFlesh/nerve", "max_stars_repo_head_hexsha": "4e430db20cf69cb065318806edf13656b5b035c0", "max_stars_repo_licenses": ["MIT"], "ma... |
#!/usr/bin/python
#==========================================================================
# summary.py
#==========================================================================
#
# -h --help Display this message
# -v --verbose Verbose mode
# -p --prefetcher Type of Prefetcher
# -s --stride number of ... | {"hexsha": "762fce0e272c5684ba72b3bec2614c070e9ff56a", "size": 12746, "ext": "py", "lang": "Python", "max_stars_repo_path": "multithreading/sum_sin.py", "max_stars_repo_name": "cmpArch207/benchmarks", "max_stars_repo_head_hexsha": "22c6d38880f14bf41ce809638c8a1de9368e8017", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma ev_alw_shift[iff]: "ev (alw P) (u @- v) \<longleftrightarrow> ev (alw P) v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ev (alw P) (u @- v) = ev (alw P) v
[PROOF STEP]
by (induct u) (auto) | {"llama_tokens": 103, "file": "Transition_Systems_and_Automata_Basic_Sequence_LTL", "length": 1} |
/-
Copyright (c) 2019 Johan Commelin. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johan Commelin
-/
import Mathlib.PrePort
import Mathlib.Lean3Lib.init.default
import Mathlib.data.finset.basic
import Mathlib.data.multiset.nat_antidiagonal
import Mathlib.PostPort
na... | {"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/data/finset/nat_antidiagonal.lean"} |
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