text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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module Types.IND where
open import Data.Nat
open import Data.Fin hiding (_+_)
open import Data.Product
open import Function
open import Relation.Binary.PropositionalEquality hiding (Extensionality)
open import Types.Direction
open import Auxiliary.Extensionality
open import Auxiliary.RewriteLemmas
private
varia... | {"hexsha": "4d60a5b5789108b822ca5566e0982c9ae49e5ed4", "size": 23563, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Types/IND.agda", "max_stars_repo_name": "peterthiemann/dual-session", "max_stars_repo_head_hexsha": "7a8bc1f6b2f808bd2a22c592bd482dbcc271979c", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
module Unmarshal
# package code goes here
# Helper function
function prettyPrint(verboseLvl, str)
tabs = ""
for cntr = 1:verboseLvl
tabs = tabs * "\t"
end
println("$(tabs)$(str)")
end
export unmarshal # returns a reconstructed variable from a JSON parsed string
using Requires
using JSON
im... | {"hexsha": "76bae07e0de82098751314e9cda6d337fae176fd", "size": 9270, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Unmarshal.jl", "max_stars_repo_name": "DhairyaLGandhi/Unmarshal.jl", "max_stars_repo_head_hexsha": "1478ff0829f1a835a8775bd8204fe814cbb05bb8", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import bpy
import bmesh
from mathutils import Vector
import numpy as np
# Blender import system clutter
import sys
from pathlib import Path
UTILS_PATH = Path.home() / "Documents/python_workspace/data-science-learning"
sys.path.append(str(UTILS_PATH))
import utils.blender_utils
import importlib
importlib.reload(utils... | {"hexsha": "701308a91fe12edf6e6037eef1f740d386fc4a96", "size": 2523, "ext": "py", "lang": "Python", "max_stars_repo_path": "cellular automata/blender-scripting/automata_blender.py", "max_stars_repo_name": "ChrizH/data-science-learning", "max_stars_repo_head_hexsha": "c5770955256ef535e22346f977fb070db98a135d", "max_star... |
\section{Introduction}
The involvement of the brain and spinal cord in motor control has been recognized since the earliest known clinical records of head and spinal injuries, dating back to ancient Egypt \citep{Louis1994,VanMiddendorp2010}. However, the mechanism used by the nervous system to generate movement was ... | {"hexsha": "9d035b9056673885615256497a810b57ce6973ca", "size": 34398, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/sections/mc-intro.tex", "max_stars_repo_name": "kampff-lab/shuttling-paper", "max_stars_repo_head_hexsha": "00ca13d59b45123cbc77483e0dd93e9b94dbe552", "max_stars_repo_licenses": ["CC-BY-4.0", "... |
#!/usr/bin/env python
"""
Class Timer which provides a context for timing blocks of code.
See Also: pisa.utils.profile module, which contains decorators for timing
functions and methods.
"""
from __future__ import absolute_import, division
from time import sleep, time
import numpy as np
from pisa.utils.format im... | {"hexsha": "1ff48a51591cc5dff0fc5665917a8aadd7603a95", "size": 2248, "ext": "py", "lang": "Python", "max_stars_repo_path": "pisa/utils/timer.py", "max_stars_repo_name": "wym109/pisa", "max_stars_repo_head_hexsha": "696803320f577d241651df900726b76a770d072a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
(*
* Copyright 2019, NTU
*
* This software may be distributed and modified according to the terms of
* the BSD 2-Clause license. Note that NO WARRANTY is provided.
* See "LICENSE_BSD2.txt" for details.
*
* Author: Albert Rizaldi, NTU Singapore
*)
theory NAND_Hoare_Typed
imports VHDL_Hoare_Typed NAND_Femto
b... | {"author": "rizaldialbert", "repo": "vhdl-semantics", "sha": "352f89c9ccdfe830c054757dfd86caeadbd67159", "save_path": "github-repos/isabelle/rizaldialbert-vhdl-semantics", "path": "github-repos/isabelle/rizaldialbert-vhdl-semantics/vhdl-semantics-352f89c9ccdfe830c054757dfd86caeadbd67159/NAND_Hoare_Typed.thy"} |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
from kfilter.simplify import *
from sympy import *
dt = Symbol('dt')
x0, v0 = symbols('x0, v0')
Q00,Q11 = symbols('Q_x, Q_v')
# x = x0 + v0*dt + 1/2*a*dt**2
# v = v0 + a*dt
x = Matrix([[x0],[v0]]) # vetor de estados
A = Matrix([[1, dt], [0, 1]]) # matriz de transição de est... | {"hexsha": "5564cbce9f822e342a909accfa60aef3b9e3cbe8", "size": 738, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/ex2.py", "max_stars_repo_name": "clnrp/kfilter", "max_stars_repo_head_hexsha": "5f9e67397f84eccbdd0b14867763b9315c2cd7c8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
#include <boost/mpl/aux_/preprocessed/mwcw/unpack_args.hpp>
| {"hexsha": "3fbbc483c1f8f3fe161aeb00c11ec57597e1a76d", "size": 60, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_mpl_aux__preprocessed_mwcw_unpack_args.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_lic... |
clear
rm a.out -f
g++ a.cpp SFMT.c -O3 -fopenmp
#gcc -O3 -finline-functions -fomit-frame-pointer -DNDEBUG -fno-strict-aliasing --param max-inline-insns-single=1800 -Wmissing-prototypes -Wall -std=c99 --param inline-unit-growth=500 --param large-function-growth=900 -DSFMT_MEXP=19937 \
#a.c SFMT.c
export OMP_NUM_THREA... | {"hexsha": "2c7c47e18c78adfd2c01558c8ea4fb7fa32b97a1", "size": 339, "ext": "r", "lang": "R", "max_stars_repo_path": "Trim/src/sfmt/.r", "max_stars_repo_name": "kkhuang81/AdaptiveSM", "max_stars_repo_head_hexsha": "e960c9f397171014c6d979adfb155ddbeeb11f82", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 23, "max... |
import numpy as np
from nengo.utils.progress import ProgressTracker
class GenericSimulator(object):
def __init__(self, dt=0.001, progress_bar=True):
self.dt = dt
self.progress_bar = progress_bar
self.n_steps = 0
self.data = {}
def run(self, time_in_seconds, progress_bar=None):
... | {"hexsha": "1b5195f66824ea9eab0ea7b8e1610ab7050cffd0", "size": 1116, "ext": "py", "lang": "Python", "max_stars_repo_path": "nengo_normal_form/generic.py", "max_stars_repo_name": "tcstewar/nengo_normal_form", "max_stars_repo_head_hexsha": "37ca02b20c4cc143a7bf9c27912ead36d23a04d7", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma knowledge_equiv_eq_NS: "
evs \<in> ns_public \<Longrightarrow>
knows A evs \<union> {Key (priEK B), Key (priSK B), Key (shrK B)} =
knows B evs \<union> {Key (priEK A), Key (priSK A), Key (shrK A)}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. evs \<in> ns_public \<Longrightarrow> knows ... | {"llama_tokens": 251, "file": "Inductive_Confidentiality_GeneralAttacker_Knowledge", "length": 2} |
import os
import numpy as np
import pandas as pd
import scipy.sparse as sparse
from lib5c.util.system import check_outdir
from lib5c.util.statistics import adjust_pvalues
from hic3defdr.util.printing import eprint
from hic3defdr.util.clusters import load_clusters
from hic3defdr.util.simulation import simulate
from h... | {"hexsha": "c3906544e33c805471d93075c473bc643090ebb1", "size": 10545, "ext": "py", "lang": "Python", "max_stars_repo_path": "hic3defdr/analysis/simulation.py", "max_stars_repo_name": "thomasgilgenast/hic3defdr", "max_stars_repo_head_hexsha": "7498ac468ccc21fa530d584944c1b12c73926755", "max_stars_repo_licenses": ["MIT"]... |
\name{circos.raster}
\alias{circos.raster}
\title{
Add raster images
}
\description{
Add raster images
}
\usage{
circos.raster(image, x, y, width, height,
facing = c("inside", "outside", "reverse.clockwise", "clockwise",
"downward", "bending.inside", "bending.outside"),
niceFacing = FALSE, sector.index = ge... | {"hexsha": "2cbbafd78c74bc18c333eb4f394fb4ea6e3e1f74", "size": 2955, "ext": "rd", "lang": "R", "max_stars_repo_path": "man/circos.raster.rd", "max_stars_repo_name": "calpan/circlize", "max_stars_repo_head_hexsha": "33f8f23663768367188e50e93d3f9b2b57edd0e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
[STATEMENT]
lemma "(pi * (real u * 2) = pi * (real (xa v) * - 2))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. pi * (real u * 2) = pi * (real (xa v) * - 2)
[PROOF STEP]
apply simp
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. pi * real u = - (pi * real (xa v))
[PROOF STEP]
oops | {"llama_tokens": 138, "file": null, "length": 2} |
export _At_mul_B,
_At_ldiv_B,
DEFAULT_COND_TOL,
hasfullrowrank,
issquare,
isinvertible,
cross_product,
nonzero_columns,
extend,
projection_matrix,
remove_zero_columns
# default tolerance for matrix condition number (see 'isinvertible')
const DEFAULT... | {"hexsha": "a87ec84979dc189416068deff194ba40edffa29e", "size": 7674, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Arrays/matrix_operations.jl", "max_stars_repo_name": "nablabits/LazySets.jl", "max_stars_repo_head_hexsha": "e839322ae970e5b61271b709f8a865184b32c8e5", "max_stars_repo_licenses": ["MIT"], "max_... |
import sys
# import libraries
import pandas as pd
import numpy as np
import seaborn as sns
import sqlite3
from sqlalchemy import create_engine
import matplotlib.pyplot as plt
# import statements
import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
import re
from nltk.corpus import st... | {"hexsha": "6f345a39f77ec571e665837daf25ee97a282a190", "size": 9358, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/train_classifier.py", "max_stars_repo_name": "angmx/DisasterResponseProject", "max_stars_repo_head_hexsha": "c2c29fb588570972d81622be79b66d0c41977861", "max_stars_repo_licenses": ["MIT"], "... |
MODULE param_db
! Module where global PARAMETERS of Program are defined
IMPLICIT NONE
!--- Constants for character lenght
INTEGER(kind=4),PARAMETER:: midn = 6 ,& !Maximum length for identifier
milb = 6 ,& !Maximum length for internal labels
... | {"hexsha": "d98c160c5fea7ab55fe01ce48b2a25860337b503", "size": 2037, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/mainp/param_db.f90", "max_stars_repo_name": "jerebenitez/IFE-simpact-openfoam", "max_stars_repo_head_hexsha": "2dbcbf3195b22fca1c80ad0da6b3822b6cad5cdf", "max_stars_repo_licenses": ["MIT"], ... |
import os
from torch.utils.data import DataLoader
from continuum.datasets import CIFAR10, InMemoryDataset
from continuum.datasets import MNIST
import torchvision
from continuum.scenarios import TransformationIncremental
import pytest
import numpy as np
from continuum.transforms.bg_swap import BackgroundSwap
DATA_PAT... | {"hexsha": "f7000bc963cc817a5a5dca6aba86f5ea6dde667e", "size": 3008, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_background_swap.py", "max_stars_repo_name": "pclucas14/continuum", "max_stars_repo_head_hexsha": "3b9b0fc3c2f21dcaeafbccfa29987cefe55f37a0", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import json
import os
import sys
import tensorflow as tf
from keras import backend as K
from keras import optimizers, utils
from keras.callbacks import CSVLogger
from keras.engine import Model
from keras.layers import Dropout, Flatten, Dense
from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D
from src... | {"hexsha": "3928dc4b6e9cf63c1016ce407fd029075ce5875a", "size": 36845, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train/TrainClassifierEnsemble.py", "max_stars_repo_name": "RamsteinWR/PneumoniaRSNA1", "max_stars_repo_head_hexsha": "08bdba51292307a78ef711c6be4a63faea240ddf", "max_stars_repo_licenses": ["M... |
from .parameters import Params
from .datasets import Datasets, FdDatasets
from .model import Model
from collections import OrderedDict
from ..detail.utilities import unique, lighten_color
from .detail.derivative_manipulation import numerical_jacobian
from .detail.utilities import print_styled, optimal_plot_layout
from ... | {"hexsha": "b331dfbb3196cba18b9dae5e392bb6590dbce66d", "size": 26815, "ext": "py", "lang": "Python", "max_stars_repo_path": "lumicks/pylake/fitting/fit.py", "max_stars_repo_name": "lumicks/pylake", "max_stars_repo_head_hexsha": "b5875d156d6416793a371198f3f2590fca2be4cd", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
[STATEMENT]
lemma INF_commute:
assumes "\<forall>x\<in>U\<^sub>2. \<forall>y\<in>U\<^sub>3. f x y \<in> U" and "B \<subseteq> U\<^sub>3" and "A \<subseteq> U\<^sub>2"
shows
"\<Sqinter>\<^sub>o\<^sub>w ((\<lambda>x. \<Sqinter>\<^sub>o\<^sub>w (f x ` B)) ` A) = \<Sqinter>\<^sub>o\<^sub>w ((\<lambda>j. \<Sqinter... | {"llama_tokens": 4506, "file": "Types_To_Sets_Extension_Examples_SML_Relativization_Lattices_SML_Complete_Lattices", "length": 29} |
# A complete use case
In this section we present a complete use case, based on the meaning classification dataset introduced in [Lorenz et al. (2021)](https://arxiv.org/abs/2102.12846) QNLP paper. The goal is to classify simple sentences (such as "skillful programmer creates software" and "chef prepares delicious meal... | {"hexsha": "f54e8c0e8e3674fd6144a462dfa25fe2df6f7f72", "size": 35474, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "docs/tutorials/training-usecase.ipynb", "max_stars_repo_name": "Thommy257/lambeq-pub", "max_stars_repo_head_hexsha": "502752346610a50fd26feb27ca6c7f5ceab5eff5", "max_stars_repo_licen... |
! Module to define simple error/exit codes
! and output messages.
!
MODULE Message_Handler
! Module use statements
USE File_Utility, ONLY: Get_Lun
! Disable all implicit typing
IMPLICIT NONE
! Visibilities
PRIVATE
! Module parameters
PUBLIC :: SUCCESS
PUBLIC :: INFORMATION
PUBLIC :: WARNING
PUB... | {"hexsha": "47bdbc638224ddf3f3bdda06f9d6cedbb5eaeb87", "size": 6437, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "var/external/crtm_2.3.0/libsrc/Message_Handler.f90", "max_stars_repo_name": "matzegoebel/WRF-fluxavg", "max_stars_repo_head_hexsha": "686ae53053bf7cb55d6f078916d0de50f819fc62", "max_stars_repo_l... |
import gym
from gym import spaces
from gym.utils import seeding
import pandas as pd
import numpy as np
from enum import Enum
import matplotlib.pyplot as plt
import csv
import gym_anytrading.datasets.b3 as b3
class TradingEnv(gym.Env):
def __init__(self):
self.n_stocks = 10
self.W = 2
self.... | {"hexsha": "02dfe05ca154c107fbf21c455a24cfca7cd6d1c0", "size": 5178, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_anytrading/envs/trading_env.py", "max_stars_repo_name": "tsb4/dayTradingEnv", "max_stars_repo_head_hexsha": "16d1970a41c8933970152f1f41e504340d48cb08", "max_stars_repo_licenses": ["MIT"], "max... |
'''Functions for estimating an adjustment to the posterior prediction
over subtypes when making predictions online.
Author: Peter Schulam
'''
import numpy as np
from scipy.optimize import minimize
from sklearn.linear_model import LogisticRegressionCV
from sklearn.cross_validation import KFold
from mypy.models impor... | {"hexsha": "302e3e3eb0268b8ccf8a0c17dc3d00dbb0d024cc", "size": 4717, "ext": "py", "lang": "Python", "max_stars_repo_path": "2015/07/online_old.py", "max_stars_repo_name": "pschulam/Notebook", "max_stars_repo_head_hexsha": "3404ce01a4ebdf23216ff01512a8f84b4f7758aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
An extension to the standard STScI data model for MIRI readnoise data, based
on the base MIRI data model.
:Reference:
The STScI jwst.datamodels documentation. See
https://jwst-pipeline.readthedocs.io/en/latest/jwst/datamodels/index.html
:History:
16 Jul 2014: Creat... | {"hexsha": "60a752ed6b592a204a484cb87efbc2c027a8aa28", "size": 6038, "ext": "py", "lang": "Python", "max_stars_repo_path": "miri/datamodels/miri_readnoise_model.py", "max_stars_repo_name": "eslavich/MiriTE", "max_stars_repo_head_hexsha": "05e25e1222e854fef5a72011f6618fa8fb5eaaff", "max_stars_repo_licenses": ["CNRI-Pyth... |
# coding:utf-8
import sys
import traceback
import talib
import numpy as np
import pandas as pd
from pandas import Series
from .base import *
from .talib_series import LINEARREG_SLOPE as SLOPE
def udf_cross(A, B):
if isinstance(A, float):
A1 = A0 = A
ls = len(B)
B1 = B.iloc[ls -2]
B0 = B.iloc[ls -1]
... | {"hexsha": "080e4566e264f52642e55eeab1ea93a25a56d266", "size": 10352, "ext": "py", "lang": "Python", "max_stars_repo_path": "easyquant/indicator/udf_formula.py", "max_stars_repo_name": "dizzy21c/easyqtrs", "max_stars_repo_head_hexsha": "4704674d2175d40afdc306afd8a002a486c83220", "max_stars_repo_licenses": ["MIT"], "max... |
import tensorflow as tf
import numpy as np
from sklearn import datasets
import math
import sys
neurons = int(sys.argv[1])
val_checks = int(sys.argv[2])
num_epochs = int(sys.argv[3])
iris = datasets.load_iris()
x = iris.data
y = iris.target
perm = np.random.permutation(150)
x = x[perm,:]
y = y[perm]
y_shaped = np.... | {"hexsha": "e0b99d28cd33a4bb223103741bd26820176e1ed5", "size": 1586, "ext": "py", "lang": "Python", "max_stars_repo_path": "resources/python/model.py", "max_stars_repo_name": "SvelaT/laravel-app", "max_stars_repo_head_hexsha": "d82b367ef2a9d2f2acc40087ac3a47c51edf4174", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
Support recovery on MEG data
============================
This example compares several methods that recover the support in the MEG/EEG
source localization problem with statistical guarantees. Here we work
with two datasets that study three different tasks (visual, audio, somato).
We reproduce the real data exper... | {"hexsha": "fb955c42cb75fce437df668183295e8a0200a20d", "size": 18473, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/plot_meg_data_example.py", "max_stars_repo_name": "ja-che/hidimstat", "max_stars_repo_head_hexsha": "67189c41279613312688de519d7ea635c7da84ae", "max_stars_repo_licenses": ["BSD-2-Clause"... |
# DAY 2 PROBLEM 2 ADVENT OF CODE
# Get the final position of the sub starting from (0,0)
commands = """forward 2
down 4
down 1
down 4
forward 3
down 6
down 5
forward 3
forward 8
down 2
down 3
up 8
down 5
up 7
down 7
forward 5
up 2
down 6
forward 7
forward 1
forward 2
forward 7
up 7
forward 6
down 3
down 1
up 9
down 2
... | {"hexsha": "fafb5573762afa90a99a2cf2b66f44a0195880a1", "size": 8876, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "2021/day2/dive_2.jl", "max_stars_repo_name": "CrosleyZack/advent_of_code", "max_stars_repo_head_hexsha": "5dee29c845b88027d1c4b17900e398fe9b3c1e44", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, ... | {"hexsha": "4c39dd45bd94f2646af7527fd33afa7ba43f8645", "size": 4485, "ext": "py", "lang": "Python", "max_stars_repo_path": "tao_triton/python/postprocessing/multitask_classification_postprocessor.py", "max_stars_repo_name": "thtang-nv/tao-toolkit-triton-apps", "max_stars_repo_head_hexsha": "de72ae4fe96986db620b542feed9... |
[STATEMENT]
lemma norm_sq_mtx_def3: "\<parallel>A\<parallel> = (SUP x. (\<parallel>A *\<^sub>V x\<parallel>) / (\<parallel>x\<parallel>))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<parallel>A\<parallel> = (SUP x. \<parallel>A *\<^sub>V x\<parallel> / \<parallel>x\<parallel>)
[PROOF STEP]
unfolding norm_sq_mtx... | {"llama_tokens": 237, "file": "Matrices_for_ODEs_SQ_MTX", "length": 2} |
#ifndef GLOBAL_TO_LOCAL_H
#define GLOBAL_TO_LOCAL_H
#include <ros/ros.h>
#include <uav_ros_lib/topic_handler.hpp>
#include <Eigen/Dense>
#include <mavros_msgs/HomePosition.h>
namespace tf_util {
/**
* @brief This class is used to transform global (Lat, Lon, Alt) to local (East, North,
* Up) coordinates and the oth... | {"hexsha": "b4b89b45581903d08898724f894bf15740d9cf61", "size": 1505, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/uav_ros_lib/global_to_local.hpp", "max_stars_repo_name": "lmark1/uav_ros_lib", "max_stars_repo_head_hexsha": "be75f43a498dc54a0f696fdaa5490ebcaecb87c4", "max_stars_repo_licenses": ["BSD-3-Cl... |
#!/bin/env python
""" Tool demonstrating how to convert from Python Dictionaries to XML"""
from xmltools import *
try :
from cxmltools import * # Use CXMLtools if possible
except :
pass
import sys
if __name__ == "__main__" :
input_stdin = False
output_stdout = False
if (len(sys.argv)==1) :
... | {"hexsha": "343396d15a57cf347e3c6f6d90849d2f09d003ab", "size": 1366, "ext": "py", "lang": "Python", "max_stars_repo_path": "deps/PicklingTools170Release/Python/dict2xml.py", "max_stars_repo_name": "dcanelhas/sdf_tracker-LS", "max_stars_repo_head_hexsha": "2685ce41fc1c8ae12d270c5e2b88afc987af9f45", "max_stars_repo_licen... |
[STATEMENT]
lemma iteratei_postfixed_correct :
assumes invar: "invar_trie (t :: ('key, 'val) trie)"
shows "set_iterator ((iteratei_postfixed ks0 t)::('key list \<times> 'val, '\<sigma>) set_iterator)
((\<lambda>ksv. (rev (fst ksv) @ ks0, (snd ksv))) ` (map_to_set (lookup_trie t)))"
[PROOF STATE]
proof (p... | {"llama_tokens": 9981, "file": "Collections_ICF_impl_Trie_Impl", "length": 65} |
import os
import alignfaces as af
import numpy as np
# plotting results in nice figures
from skimage.util import montage
import matplotlib.pyplot as plt
def slim_fig(ax):
ax.set_axis_off()
plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0,
hspace = 0, wspace = 0)
plt.margins(0,... | {"hexsha": "81ceb70ba1bee562beb7c8b31f7e90e76eb42a48", "size": 4256, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/demo_3_averaging/run_demo.py", "max_stars_repo_name": "SourCherries/auto-face-align", "max_stars_repo_head_hexsha": "365bd01c22da6f3a44190261786fcc585687ea50", "max_stars_repo_licenses": ["A... |
# Written by Mansur Yeşilbursa
import numpy as np
import pickle
def ml_sentence_splitter(text):
'''
Args:
text: given a string
Returns:
sentences: list of sentences in the string
'''
model_dir = '../models/sentence_splitting/'
with open(model_dir + 'model_liblinear... | {"hexsha": "9d3d5f59d840bb3b0f51c3de97332d6bc2f815bd", "size": 1563, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ml_sentence_splitter.py", "max_stars_repo_name": "garsontrier/turkish-nlp-preprocessor", "max_stars_repo_head_hexsha": "88180c21fe22b7d88e3d6bff82afbf1be7cadd75", "max_stars_repo_licenses": ["... |
import copy
import math
import numpy as np
from typing import Any, Mapping
from scipy.spatial.transform import Slerp
from scipy.spatial.transform import Rotation
from src.robots.motors import MotorCommand
from src.robots.robot import Robot
import lp_python_interface
def lerp(a: float, b: float, t: float) -> float:
... | {"hexsha": "17ba5eda36ce013d4299cacb3439c8fb5ecaa400", "size": 11967, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/controller/leg_controller.py", "max_stars_repo_name": "jrenaf/fast_and_efficient", "max_stars_repo_head_hexsha": "708b2e79fba19330190046f29383d298a95c3abd", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import copy
import math
import cv2
import time
import scipy.signal as ss
class Node(object):
def __init__(self, grid):
"""
:param grid: np.array nrow*ncolumn
author: weiwei
date: 20190828, 20200104
"""
self.grid = copy.deepcopy(grid)
s... | {"hexsha": "7ffa769731987463134bd280ea5bb4c4c7fceff7", "size": 10567, "ext": "py", "lang": "Python", "max_stars_repo_path": "0000_huri/tubepuzzlefast.py", "max_stars_repo_name": "liang324/wrs", "max_stars_repo_head_hexsha": "46eadec355c61a9c7bac1fa0f3cf419b2aac19aa", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""OpenCV Camera class for lens correction with Charuco calibration."""
from .Camera import Camera
from pathlib import Path
import numpy as np
from threading import Event, Thread
import time
import subprocess
import os
import sys
try:
import cv2
from cv2 import aruco
except ImportError:
raise ImportError(... | {"hexsha": "24dd74c29eb7efac9aec5b69f8c9360548d553d9", "size": 22318, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera_fusion/CameraCorrected.py", "max_stars_repo_name": "a1rb4Ck/camera_fusion", "max_stars_repo_head_hexsha": "2c62ec2e9b26c88a5bc8180b3af63d39609b1c21", "max_stars_repo_licenses": ["MIT"], "m... |
# tests for tf_util
import numpy as np
import tensorflow as tf
from stable_baselines.common.tf_util import function, initialize, single_threaded_session, is_image
def test_function():
"""
test the function function in tf_util
"""
with tf.Graph().as_default():
x_ph = tf.placeholder(tf.int32, (... | {"hexsha": "d71374da03ba1f88fb12b683b4348d5b7f556744", "size": 1713, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_tf_util.py", "max_stars_repo_name": "TreeKid/stable-baselines", "max_stars_repo_head_hexsha": "129c1958160b95962b887c312cd2273aed35df60", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
REBOL []
do %rugby.r
code: {add1: func [ n [number!]][n + 1]}
do get-rugby-service http://localhost:8002
extend-env [add1] code
| {"hexsha": "6cb265a376424c35b217165cbc3d8a7c32b49c4e", "size": 129, "ext": "r", "lang": "R", "max_stars_repo_path": "xtest.r", "max_stars_repo_name": "mbk/rugby", "max_stars_repo_head_hexsha": "4c9507aae30b606e702349be8a3e6b9298e75ac7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1, "max_stars_repo_st... |
// (C) Copyright John Maddock 2006.
// Use, modification and distribution are subject to the
// Boost Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#ifndef BOOST_MATH_TOOLS_POLYNOMIAL_HPP
#define BOOST_MATH_TOOLS_POLYNOMIAL_HPP
#i... | {"hexsha": "8225736b9e5838303d7bcfccdd76d9094ff3e022", "size": 8032, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/math/tools/polynomial.hpp", "max_stars_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_stars_repo_head_hexsha": "4fb2d44945a630... |
[STATEMENT]
lemma dim_solution_set_not_zero_imp_infinite_solutions_no_homogeneous:
fixes A::"'a::{field, semiring_char_0}^'n::{mod_type}^'rows::{mod_type}"
assumes dim_not_0: "vec.dim (solution_set A 0) > 0"
and con: "consistent A b"
shows "infinite (solution_set A b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ... | {"llama_tokens": 2270, "file": "Gauss_Jordan_System_Of_Equations", "length": 28} |
[STATEMENT]
lemma get_update_eq [simp]:
"get (update a i v h) a = (get h a) [i := v]"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. get (update a i v h) a = (get h a)[i := v]
[PROOF STEP]
by (simp add: update_def) | {"llama_tokens": 98, "file": null, "length": 1} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# gauss2d.py
"""
Class for generating and fitting 2D Gaussian peaks
Supports both least squares and MLE fitting and gaussian peaks
parameterized by a single width, widths along each axis and widths
along arbitrary axes. Fitting can be done with manually specified
guesses o... | {"hexsha": "30bbb83c9edf3039bfdc37510ad437e42ef27c8a", "size": 36593, "ext": "py", "lang": "Python", "max_stars_repo_path": "peaks/gauss2d.py", "max_stars_repo_name": "david-hoffman/peaks", "max_stars_repo_head_hexsha": "b31a13fcb93005ed01e5295389f91491bafc71cd", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import numpy as np
from libsvmdata.datasets import fetch_libsvm
from celer import LogisticRegression
# from sklearn.linear_model import LogisticRegression
from sparse_ho.models import SparseLogreg
from sparse_ho.criterion import LogisticMulticlass
from sparse_ho import ImplicitForward
from sparse_ho.optimizers import... | {"hexsha": "01effe22c116fec5b3631b30a6556f6f5fea913e", "size": 2599, "ext": "py", "lang": "Python", "max_stars_repo_path": "expes/multiclass/plot_multiclass.py", "max_stars_repo_name": "LeoIV/sparse-ho", "max_stars_repo_head_hexsha": "f0a5792766a7f0c03bba28cddb983621174cb4ea", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import logging
import csv
import pickle
from tensorflow.python import debug as tf_debug
logging.basicConfig(level=logging.INFO)
DIR_DATASET = "../dataset"
FILENAME_PKL = "train.pkl"
PATH_IMG_PKL = os.path.join(DIR_DATASET, FILENAME_... | {"hexsha": "ff991666eb966e2fbab7e2d73771a4557f0ecff1", "size": 6177, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train.py", "max_stars_repo_name": "JaveyWang/5-char-real-number-recognition", "max_stars_repo_head_hexsha": "dca6cb748d466a774b4e5a1daa4a31af7e04a550", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
lemma length_coeffs_degree':
"length (coeffs p) = (if p = 0 then 0 else Suc (degree p))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length (coeffs p) = (if p = 0 then 0 else Suc (degree p))
[PROOF STEP]
by (cases "p = 0") (auto simp: length_coeffs_degree) | {"llama_tokens": 124, "file": "Factor_Algebraic_Polynomial_Roots_via_IA", "length": 1} |
#Kaplan-Meier Estimator
import numpy as np
import numpy.linalg as la
import matplotlib.pyplot as plt
from scipy import stats
from statsmodels.iolib.table import SimpleTable
class KaplanMeier(object):
"""
KaplanMeier(...)
KaplanMeier(data, endog, exog=None, censoring=None)
Create an object of... | {"hexsha": "bbba55503b0f916091ad183467e54bd6d8f8e2d4", "size": 17924, "ext": "py", "lang": "Python", "max_stars_repo_path": "statsmodels/sandbox/survival2.py", "max_stars_repo_name": "yarikoptic/statsmodels", "max_stars_repo_head_hexsha": "f990cb1a1ef0c9883c9394444e6f9d027efabec6", "max_stars_repo_licenses": ["BSD-3-Cl... |
\documentclass[a4paper]{article}
%\documentclass[a4paper]{scrartcl}
\usepackage{url}
\usepackage{amsfonts}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{subcaption}
\usepackage{float}
\usepackage{comment}
\usepackage{graphicx}
\usepackage{xcolor}
\renewcommand{\i}[1]{\textit{#1}}
\newcommand\blue[1]{\textcol... | {"hexsha": "5adf7456586cbdeda7327ed4e02ee9501796b76a", "size": 5273, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "notes/fomega/cek-cps-experiments/tex/results.tex", "max_stars_repo_name": "AriFordsham/plutus", "max_stars_repo_head_hexsha": "f7d34336cd3d65f62b0da084a16f741dc9156413", "max_stars_repo_licenses": [... |
// Boost sorting_algo library float_sort_test.cpp file ---------------------------//
// Copyright Steven Ross 2009. Use, modification and
// distribution is subject to the Boost Software License, Version
// 1.0. (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
// See h... | {"hexsha": "105322e2e7065fb19c471a4d5328a02a01f696dd", "size": 3435, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/algorithm/sorting/test/float_sort_test.cpp", "max_stars_repo_name": "spreadsort/algorithm_sorting", "max_stars_repo_head_hexsha": "bc425fcfe8c883f3f6c8a4068bb55d4b7da2d1da", "max_stars_repo_lic... |
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | {"hexsha": "4a47cf9bf9062b89c76554720a337296e9ea2703", "size": 10240, "ext": "py", "lang": "Python", "max_stars_repo_path": "ffn/inference/resegmentation.py", "max_stars_repo_name": "necrodancer/ffn", "max_stars_repo_head_hexsha": "43552bbc2585ca350d8495454e7580c47806e637", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import numpy as np
import qiskit.quantum_info as qi
def concurrence_single(dm):
# dm = qi.DensityMatrix(dm)
con = qi.concurrence(dm)
return con
def concurrence(dm_tensor):
con_list = list(map(concurrence_single, dm_tensor))
con_tensor = np.array(con_list)
return con_tensor | {"hexsha": "9931eecbfc2b91a2e1180514033ded05dc3f5ea4", "size": 299, "ext": "py", "lang": "Python", "max_stars_repo_path": "Toy-model/CP_werner_with_MA/utils/Concurrence_Measure.py", "max_stars_repo_name": "slohani-ai/data-centric-in-qis", "max_stars_repo_head_hexsha": "bbc545454f7d98a28a4fc83f2f6b14de253fcb6c", "max_st... |
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matplotlib.patches as mpatches
import numpy as np
def histogram(df, df_column, binwidth=10, including=10,
ultima_medalha=3, ultima_mencao=10, ax=None):
"""Gera histograma com os dados de notas, com linhas destacando
a zon... | {"hexsha": "bfee71b38b0d7562b527a897d553df6fc15da678", "size": 3935, "ext": "py", "lang": "Python", "max_stars_repo_path": "resultados/plots.py", "max_stars_repo_name": "chicolucio/estatisticas-oiq-2019", "max_stars_repo_head_hexsha": "017da73f3bef6a6f40d7dd1214621cad7c14be2e", "max_stars_repo_licenses": ["MIT"], "max_... |
import os.path
from os import path
from time import sleep
import time, random
import numpy as np
from absl import app, flags, logging
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
from yolov3_tf2.models import (
YoloV3, YoloV3Tiny
)
from yolov3_tf2.dataset import transform_images
from yolov3_tf... | {"hexsha": "6c8ce19ce2c8f70eeadf17b77833b7d7d12859bf", "size": 7564, "ext": "py", "lang": "Python", "max_stars_repo_path": "deep_sort/object_track.py", "max_stars_repo_name": "lilun-cheng/vehicle_highway_tracking", "max_stars_repo_head_hexsha": "99c9981c2d9f998d90df070d271b7b88f6dc0a35", "max_stars_repo_licenses": ["Ap... |
\par
\section{Driver programs for the {\tt DFrontMtx} object}
\label{section:DFrontMtx:drivers}
\par
%=======================================================================
\begin{enumerate}
%-----------------------------------------------------------------------
\item
\begin{verbatim}
testGrid msglvl msgFile n1 n2 n3... | {"hexsha": "bc351c7014311e2a4dbf52a05b0708a0573bce7e", "size": 4975, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ccx_prool/SPOOLES.2.2/FrontMtx/doc/drivers.tex", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars_repo_lice... |
MODULE ps_local_fftw_module
use rgrid_module, only: Ngrid
use ggrid_module, only: NGgrid, MGL, MG_0,MG_1, LLG, allgatherv_ggrid &
,construct_ggrid, destruct_ggrid
use fftw_module, only: ML1_c, ML2_c, N_ML3_c, ML3_c0 &
,zwork3_ptr0, zwork3_ptr1, plan_backward, z3_to_d1_fftw
use,i... | {"hexsha": "2e105b70a0694f044d98ffc0f19e7f44bbe8737d", "size": 1935, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/ps_local_fftw_module.f90", "max_stars_repo_name": "j-iwata/RSDFT", "max_stars_repo_head_hexsha": "2a961b2c8339a49de9bd09f55e7d6a45b6159d2e", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
"""Random policies.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tf_agents.policies import random_tf_policy
from tf_agents.specs import tensor_spec
from tf_agents.trajectories import policy_step
from p... | {"hexsha": "92e04289f60703e9b083a2674202d72d57289f30", "size": 3370, "ext": "py", "lang": "Python", "max_stars_repo_path": "policies/random_policy.py", "max_stars_repo_name": "StanfordVL/cavin", "max_stars_repo_head_hexsha": "581f70fefb3a869db739d8539f3b74759ab71777", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
Name: Karahan Mete
Long time Davis resident.
Educator
Business Management Consultant
R&D Agriculture: Introduced Black tea farming in California
Support sustainable agriculture.
Nondenominational Reverend
Practice Sufism
Social Justice Activist.
Concerned and seeking to promote human welfare
Born and raised in Turkey... | {"hexsha": "83b612ce8a742e27868d91794b7864530739dffe", "size": 363, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Karahan_Mete.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma "add_tvarsT T acc = acc \<union> tvsT T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. add_tvarsT T acc = acc \<union> tvsT T
[PROOF STEP]
unfolding add_tvarsT_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fold_atyps (case_typ (\<lambda>literal list. id) (\<lambda>idn s. insert (idn, s))) ... | {"llama_tokens": 1151, "file": "Metalogic_ProofChecker_Term", "length": 7} |
import os, sys
import csv
from os.path import isfile, join
from collections import Counter
import matplotlib.pyplot as plt
import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
import sklearn
import numpy
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets i... | {"hexsha": "0980e9359446296b60c4fc76c73be29175075ccf", "size": 5955, "ext": "py", "lang": "Python", "max_stars_repo_path": "otherCodeTaskSnippets/AgeGenderOPTICS.py", "max_stars_repo_name": "s2812135/Data_Challenges_WiSe2122", "max_stars_repo_head_hexsha": "a55372f444e7344af4e2e1f04e4244fb8cefeefe", "max_stars_repo_lic... |
from sklearn import skbase
import numpy as np
#https://www.kaggle.com/c/ashrae-energy-prediction/discussion/113784#latest-656376
class DatetimeConvertCyclical(skbase.BaseEstimator, skbase.TransformerMixin):
def __init__(self):
self.time_periods = {'second': 24 * 60 * 60,
'minu... | {"hexsha": "189af03652f0c7065c863635cf916ea2100e2baf", "size": 912, "ext": "py", "lang": "Python", "max_stars_repo_path": "python-package/LiteMORT/LiteMORT_time.py", "max_stars_repo_name": "closest-git/LiteMORT", "max_stars_repo_head_hexsha": "4c04277f2c5c7500e00ce4e3d26c2641ea85377e", "max_stars_repo_licenses": ["MIT"... |
"""Utility functions used by the notebooks
"""
import json
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.options.display.max_columns = 48
pd.options.display.max_rows = 48
def cal_phrate_alex(d, stream, phrates=None, recompute=False):
"""Compute peak photon r... | {"hexsha": "cb08969a461738f57c15109c8e903f132edbe0bd", "size": 6689, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "tritemio/48-spot-smFRET-PAX-analysis", "max_stars_repo_head_hexsha": "63c61dc93c9a605796b883ab44ed33ed5f3761d6", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
This module helps construct sums of non-hermitian operators for measuring
specific sets of amplitudes. The general procedure for measuring individual
amplitudes was outlined in
Nature Communications 7, Article number: 10439 (2016)
doi:10.1038/ncomms10439
Each amplitude c_{j} is associated with an operator |a><j|... | {"hexsha": "94f6a771c24cc7357dcd7c86486c13f4ea818790", "size": 5623, "ext": "py", "lang": "Python", "max_stars_repo_path": "grove/measurements/amplitude_measurement.py", "max_stars_repo_name": "mkeshita/grove", "max_stars_repo_head_hexsha": "dc6bf6ec63e8c435fe52b1e00f707d5ce4cdb9b3", "max_stars_repo_licenses": ["Apache... |
# Plots a spectrogram as a figure. This should be a good app to
import argparse
import matplotlib.pyplot as plt
import numpy
import mir3.data.self_similarity_matrix as ssm
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('infile', help="""spectrogram file""")
parser.add_a... | {"hexsha": "5ddfbbfd3f736f37d6ac4b9000072bd0bc797fb3", "size": 1408, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/pyplot/plot-selfsimilarity.py", "max_stars_repo_name": "pymir3/pymir3", "max_stars_repo_head_hexsha": "c1bcca66a5ef1ff0ebd6373e3820e72dee6b0b70", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Convolution1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.embeddings ... | {"hexsha": "3d1d52b30beec205d711fe78972f366744eadf8d", "size": 4746, "ext": "py", "lang": "Python", "max_stars_repo_path": "ECS_demo/core.py", "max_stars_repo_name": "reconjohn/dev", "max_stars_repo_head_hexsha": "aa2248338d9f3b54c345baf1dcd9531592586925", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_... |
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import argparse
import warnings
import astropy.units as u
from astropy.table import Table
from astropy.modeling.fitting import LevMarLSQFitter
from G21 import G21, G21_drude_asym
def clean_pnames(pnames):
"""
function to clean of the _? ... | {"hexsha": "532e691ca3d154c5e3dcf83aba77da16c3c9582c", "size": 4283, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/fit_mir_ext_powerlaw_ob12.py", "max_stars_repo_name": "karllark/spitzer_mir_ext", "max_stars_repo_head_hexsha": "72b5af2610761b1056f966299c48f37714d3486c", "max_stars_repo_licenses": ["BSD-3... |
from .. import ParallelSampler
import numpy as np
from . import metropolis
from cosmosis.runtime.analytics import Analytics
import os
#We need a global pipeline
#object for MPI to work properly
pipeline=None
METROPOLIS_INI_SECTION = "metropolis"
def posterior(p):
return pipeline.run_results(p)
class Metropoli... | {"hexsha": "957514dd22e3d1915bee016f9f786f49752a2c4f", "size": 8875, "ext": "py", "lang": "Python", "max_stars_repo_path": "cosmosis/samplers/metropolis/metropolis_sampler.py", "max_stars_repo_name": "annis/cosmosis", "max_stars_repo_head_hexsha": "55efc1bc2260ca39298c584ae809fa2a8e72a38e", "max_stars_repo_licenses": [... |
import jax
import jax.numpy as jnp
import pytreearray as pta
def default_norm(res, t):
if isinstance(res, jnp.ndarray):
return jnp.sqrt(jnp.mean(jnp.abs(res) ** 2))
else:
return jnp.sqrt(
jax.tree_util.tree_reduce(
lambda x, y: x + y,
jax.tree_map(l... | {"hexsha": "62e31712d45ca31b3d4be7a8847e9cd0a726b7bc", "size": 851, "ext": "py", "lang": "Python", "max_stars_repo_path": "ode4jax/_src/base/residuals.py", "max_stars_repo_name": "PhilipVinc/netket_dynamics", "max_stars_repo_head_hexsha": "6e8009098c279271cb0f289ba9e85c039bb284e4", "max_stars_repo_licenses": ["Apache-2... |
#!/usr/bin/env python3
import json
import numpy
import os.path
import statistics
import sys
import operator
import functools
import numbers
import math
from collections import defaultdict, Counter
def main():
paths = sorted(sys.argv[1:], key=get_time)
games = list(collect_data(paths))
stats = get_stats(... | {"hexsha": "84bdce1ea747e3344f7b3650458b8fd009188d95", "size": 10854, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/results_stats.py", "max_stars_repo_name": "elsid/CodeSide", "max_stars_repo_head_hexsha": "2c08f73114cd1e4d29cde61b342d1ef4e052e5cc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
[STATEMENT]
lemma sup_left_zero[simp]:
"top \<squnion> -x = top"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. top \<squnion> - x = top
[PROOF STEP]
by (metis complement_bot sub_commutative sup_right_zero) | {"llama_tokens": 89, "file": "Subset_Boolean_Algebras_Subset_Boolean_Algebras", "length": 1} |
#include <boost/property_tree/ptree.hpp>
#include <iostream>
using boost::property_tree::ptree;
int main()
{
ptree pt;
pt.put("C:.Windows.System", "20 files");
ptree &c = pt.get_child("C:");
ptree &windows = c.get_child("Windows");
ptree &system = windows.get_child("System");
std::cout << system.get_valu... | {"hexsha": "7178b6be4a2fd49bc9a185d54493bdd255cc60d9", "size": 347, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Example/propertytree_01/main.cpp", "max_stars_repo_name": "KwangjoJeong/Boost", "max_stars_repo_head_hexsha": "29c4e2422feded66a689e3aef73086c5cf95b6fe", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""TRUNAJOD ttr tests."""
import string
from collections import namedtuple
import numpy as np
import pytest
from TRUNAJOD import ttr
Token = namedtuple("Token", ["lemma_", "pos_"])
@pytest.fixture
def test_doc():
"""Fixture to use a doc for tests."""
doc = [
Token(lemma_="hola", pos_="hola"),
... | {"hexsha": "68b7ed9945a1b25acdffd6837ee0418f2b5e830b", "size": 3409, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/ttr_test.py", "max_stars_repo_name": "dpalmasan/TRUNAJOD2.0", "max_stars_repo_head_hexsha": "b718cacf2ec6bf1e868b7cb2c2b89bd4d08f37cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import Arena
from MCTS import MCTS
from go.Game import Game
from go.GoPlayers import *
from go.pytorch.NNet import NNetWrapper as NNet
import numpy as np
from utils import *
"""
use thisss script to play any two agents against each other, or play manually with
any agent.
"""
args = dotdict({
'size': 9, ... | {"hexsha": "b7bbfd666fe9a020e9d906bbf97885af30d37c2c", "size": 2362, "ext": "py", "lang": "Python", "max_stars_repo_path": "pit.py", "max_stars_repo_name": "jiz322/GoAgent", "max_stars_repo_head_hexsha": "d8a082348b7c0ce0e5cd83d449ad82bf4cc84c56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_... |
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM, TimeDistributed
import random as rnd
import numpy as np
def genModel( nChars, nHidden, numLayers = 1, dropout = 0.5, recurrent_dropout = 0.5 ):
"""Generates the RNN model with nChars characters ... | {"hexsha": "e951dc979213a017eb432c538cc086273035740f", "size": 1661, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/rnnModel.py", "max_stars_repo_name": "m0baxter/twitterBot", "max_stars_repo_head_hexsha": "446b0b76d80a5d2666e69013d9aaf13cb60cc65f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
module IdentityVectorsTests
using Test
using Gridap.Arrays
l = 10
a = IdentityVector(l)
b = collect(1:l)
test_array(a,b)
c = rand(l)
d = lazy_map(Reindex(c),a)
@test d === c
end # module
| {"hexsha": "3e67241e3e6b95d1b70d77777007f166b9853074", "size": 191, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/ArraysTests/IdentityVectorsTests.jl", "max_stars_repo_name": "aerappa/Gridap.jl", "max_stars_repo_head_hexsha": "1fb3dc9abf8c47685637901bd14a74e4355a9492", "max_stars_repo_licenses": ["MIT"], "... |
from __future__ import absolute_import, division, print_function, unicode_literals
import fragment
import numpy as np
import os
import tensorflow as tf
import utils
OUTPUT_DIR = "data/processed"
def preprocess_fragment(fragment):
np_data = fragment.np_data
ds = np_data.shape
tf_input = np_data.reshape(1... | {"hexsha": "bf9b5d1c35fc7f82bcd55b6d44a60ba623a68bb7", "size": 2031, "ext": "py", "lang": "Python", "max_stars_repo_path": "load_data.py", "max_stars_repo_name": "sbenthall/deeptune", "max_stars_repo_head_hexsha": "ac74b09367f951df17c986a4890242bee9b2fcb7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
# -*- coding: utf-8 -*-
"""
1-D model
Module Name : Graph
Graph module for 2-D data
Fanghe @ gatech MoSE 3229
Version:
+ python => 3.5
+ Anaconda recommend
USTC-AEMOL
Gatech-Apollo
"""
import numpy as np
import matplotlib.pyplot as plt
def graph_output(data, time_step, fig_type = "contour"):
"""
Thi... | {"hexsha": "c723ca9d42534fdb0c71fbaa0ab2e65391fba7ef", "size": 1160, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_model.py", "max_stars_repo_name": "zfh1997/P1dD_model", "max_stars_repo_head_hexsha": "506da52a1c2251aee69e8d14cb354d4a6398e9df", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import argparse
import json
import os
from pprint import pprint
from collections import Counter
import torch
import numpy as np
from sklearn.metrics import (
precision_recall_fscore_support,
confusion_matrix,
accuracy_score,
)
from .data import SlotFeatures
from .wikievents import WikiEventsArgumentDatase... | {"hexsha": "55a8808f416767aa9be9fecb25ef62925cd91d75", "size": 8435, "ext": "py", "lang": "Python", "max_stars_repo_path": "a2t/slot_classification/run_evaluation.py", "max_stars_repo_name": "techthiyanes/Ask2Transformers", "max_stars_repo_head_hexsha": "7ea530eb39db3514f0d9147b783fcd14a184c599", "max_stars_repo_licens... |
import pickle
import os
import numpy as np
from gala import imio, agglo, features, classify
fman = features.default.snemi3d()
def train(index):
out_fn = 'training-data-%i.h5' % index
if os.path.exists(out_fn):
data, labels = classify.load_training_data_from_disk(out_fn,
... | {"hexsha": "2a709f51c95cc669dd78cdec425df1f397b2e9ec", "size": 2192, "ext": "py", "lang": "Python", "max_stars_repo_path": "crossval4x.py", "max_stars_repo_name": "jni/gala-scripts", "max_stars_repo_head_hexsha": "fc0ee0d418496f0ec8da01e8bd8e2d12024accaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
(*
The well-ordering theorem. The proof mostly follows Section III.2 of
Bourbaki.
*)
theory WellOrder
imports Interval Wfrec
begin
section \<open>Operation of adjoining a greatest element to an order\<close>
(* Abbreviated to ++ in this theory only *)
definition adjoin_greatest :: "[i, i] \<Rightarrow> i" (... | {"author": "bzhan", "repo": "auto2", "sha": "2e83c30b095f2ed9fa5257f79570eb354ed6e6a7", "save_path": "github-repos/isabelle/bzhan-auto2", "path": "github-repos/isabelle/bzhan-auto2/auto2-2e83c30b095f2ed9fa5257f79570eb354ed6e6a7/FOL/WellOrder.thy"} |
# Gets parameters from a fit, puts them in a dataframe, prints them pretty.
from StringIO import StringIO
import pandas as pd
import numpy as np
import prettytable
def get_params(fit_params, u_param=None):
afit_params = np.asarray(fit_params)
rfit_params = np.around(afit_params, decimals=6)
ffit_params... | {"hexsha": "5f7442ef97fde7fb4185bc8d40641e0e851de9dc", "size": 1538, "ext": "py", "lang": "Python", "max_stars_repo_path": "userlib/analysislib/paco_analysis/fit_table.py", "max_stars_repo_name": "specialforcea/labscript_suite", "max_stars_repo_head_hexsha": "a4ad5255207cced671990fff94647b1625aa0049", "max_stars_repo_l... |
//------------------------------------------------------------------------------
/*
This file is part of cbcd: https://github.com/cbc/cbcd
Copyright (c) 2012, 2013 cbc Labs Inc.
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted, pro... | {"hexsha": "9cf53d0e985ee61b7f65cea92e9ef0b2b55621a0", "size": 5160, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cbc/app/paths/Flow.cpp", "max_stars_repo_name": "sergeym610/ripple_fork", "max_stars_repo_head_hexsha": "a2c3dbf8cd61adf02d32a32ea6c2c21e547a1367", "max_stars_repo_licenses": ["BSL-1.0"], "max_s... |
import OpenMORe.model_order_reduction as model_order_reduction
from OpenMORe.utilities import *
import matplotlib.pyplot as plt
import numpy as np
import os
#######################################################################################
# In this example it's shown how to use the Varimax rotation to increase ... | {"hexsha": "94bf606b4769dce0cf6a55b2875b0b810ce2c844", "size": 1992, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/others_general/varimaxRotation.py", "max_stars_repo_name": "gdalessi/clustering", "max_stars_repo_head_hexsha": "79988ee565c9d1b00bbcd3c1dbd9a69d9c1c80f1", "max_stars_repo_licenses": ["MI... |
import unittest
import os
import numpy as np
from gym.spaces.box import Box
from pathlib import Path
# BARK imports
from bark.runtime.commons.parameters import ParameterServer
# BARK-ML imports
from bark_ml.library_wrappers.lib_tf2rl.runners.gail_runner import GAILRunner
from bark_ml.library_wrappers.lib_tf2rl.agents... | {"hexsha": "2e6dbe889433bcfc959f8d5b75f2d2a299acc053", "size": 4228, "ext": "py", "lang": "Python", "max_stars_repo_path": "bark_ml/tests/py_library_tf2rl_tests/py_gail_training_tests.py", "max_stars_repo_name": "GAIL-4-BARK/bark-ml", "max_stars_repo_head_hexsha": "c61c897842c2184ee842428e451bae3be2cd7242", "max_stars_... |
# libraries
const libeng = Ref{Ptr{Cvoid}}()
const libmx = Ref{Ptr{Cvoid}}()
const libmat = Ref{Ptr{Cvoid}}()
# matlab engine functions
const eng_open = Ref{Ptr{Cvoid}}()
const eng_close = Ref{Ptr{Cvoid}}()
const eng_set_visible = Ref{Ptr{Cvoid}}()
const eng_get_visible = Ref{Ptr{Cvoid}}()
cons... | {"hexsha": "63ef6f4036916dc67ec3bc9c01de16de245f1334", "size": 3187, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/init.jl", "max_stars_repo_name": "blegat/MATLAB.jl", "max_stars_repo_head_hexsha": "9c67743e609997060fe1aeab7d9137b7c1dceb67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 163, "max_s... |
//------------------------------------------------------------------
// **MEDYAN** - Simulation Package for the Mechanochemical
// Dynamics of Active Networks, v4.0
//
// Copyright (2015-2018) Papoian Lab, University of Maryland
//
// ALL RIGHTS RESERVED
//
// See the MEDYAN web page ... | {"hexsha": "1552f6d427b30c8d9925889bd09fc1e2cfdb1c8a", "size": 64266, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Output.cpp", "max_stars_repo_name": "allen-cell-animated/medyan", "max_stars_repo_head_hexsha": "0b5ef64fb338c3961673361e5632980617937ee6", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_... |
#pragma once
#include <boost/filesystem.hpp>
#include "../kernel/string/string.h"
///////////////////////////////////////////////////////////////////////////////
/// Content addressable storage
///////////////////////////////////////////////////////////////////////////////
namespace ork::file {
//////////////////////... | {"hexsha": "7d8c71c40bcff30674a5db6fd9fce2c4d64626f6", "size": 832, "ext": "inl", "lang": "C++", "max_stars_repo_path": "ork.core/inc/ork/file/cas.inl", "max_stars_repo_name": "tweakoz/orkid", "max_stars_repo_head_hexsha": "e3f78dfb3375853fd512a9d0828b009075a18345", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_co... |
abstract type AbstractBoolDomain <: AbstractDomain end
"""
struct BoolDomain <: AbstractDomain
Boolean domain, uses a IntDomain in it. (true is 1 and false is 0)
"""
struct BoolDomain <: AbstractBoolDomain
inner::IntDomain
function BoolDomain(trailer::Trailer)
return new(IntDomain(tr... | {"hexsha": "bcef5df773a7f0322d15b686162609329fbc7f27", "size": 2559, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/CP/variables/BoolDomain.jl", "max_stars_repo_name": "pitmonticone/SeaPearl.jl", "max_stars_repo_head_hexsha": "0c0ca5ec5cce81515acd202ea2d87c985c0c3fea", "max_stars_repo_licenses": ["BSD-3-Clau... |
# various analytic mass profiles: Hernquist, NFW, Plummer, Isothermal, Miyamoto-Nagai (for disks)
import numpy as np
import astropy.units as u
from astropy import constants
from .cosmo_tools import *
G = constants.G.to(u.kpc * u.km**2. / u.Msun/ u.s**2.)
class NFW:
def __init__(self, Mvir, r, cvir):
"""
... | {"hexsha": "558f9222afd8e7f80c149473abdf4b62988f80b9", "size": 6369, "ext": "py", "lang": "Python", "max_stars_repo_path": "jellyfish/profiles.py", "max_stars_repo_name": "ekta1224/jellyfish", "max_stars_repo_head_hexsha": "3271019434448b5916dcc920d640b81375b74c05", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#################################################################################
# The Institute for the Design of Advanced Energy Systems Integrated Platform
# Framework (IDAES IP) was produced under the DOE Institute for the
# Design of Advanced Energy Systems (IDAES), and is copyright (c) 2018-2021
# by the softwar... | {"hexsha": "fe04f8bcc7df98ea6bc088666d6ad4f6bcc0960b", "size": 8985, "ext": "py", "lang": "Python", "max_stars_repo_path": "idaes/core/util/misc.py", "max_stars_repo_name": "OOAmusat/idaes-pse", "max_stars_repo_head_hexsha": "ae7d3bb8e372bc32822dcdcb75e9fd96b78da539", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_c... |
import numpy as np
import scipy.spatial as spatial
def bilinear_interpolate(img, coords):
""" Interpolates over every image channel
http://en.wikipedia.org/wiki/Bilinear_interpolation
:param img: max 3 channel image
:param coords: 2 x _m_ array. 1st row = xcoords, 2nd row = ycoords
:returns: array of interp... | {"hexsha": "50b1a3151683891fe29f85b69660417956b904a6", "size": 4473, "ext": "py", "lang": "Python", "max_stars_repo_path": "face_morpher/facemorpher/warper.py", "max_stars_repo_name": "ivan-uskov/faces", "max_stars_repo_head_hexsha": "59a27c305888e8e000cb1549f8b06216449b1f05", "max_stars_repo_licenses": ["MIT"], "max_s... |
function marginal = marginal_nodes(engine, nodes, t, add_ev)
% MARGINAL_NODES Compute the marginal on the specified query nodes (hmm)
% marginal = marginal_nodes(engine, nodes, t, add_ev)
%
% 'nodes' must be a single node.
% t is the time slice.
if nargin < 3, t = 1; end
if nargin < 4, add_ev = 0; end
assert(length(n... | {"author": "bayesnet", "repo": "bnt", "sha": "bebba5f437b4e1e29169f0f3669df59fb5392e62", "save_path": "github-repos/MATLAB/bayesnet-bnt", "path": "github-repos/MATLAB/bayesnet-bnt/bnt-bebba5f437b4e1e29169f0f3669df59fb5392e62/BNT/inference/dynamic/@hmm_inf_engine/marginal_nodes.m"} |
from __future__ import print_function
from functools import reduce
import re
import numpy as np
from keras.preprocessing.sequence import pad_sequences
def tokenize(sent):
'''Return the tokens of a sentence including punctuation.
>>> tokenize('Bob dropped the apple. Where is the apple?')
['Bob', 'dropped'... | {"hexsha": "caec9aec834d34c2ad236bf5dbd9f557a2747098", "size": 3934, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/babi_sitter.py", "max_stars_repo_name": "jayanthkoushik/eve", "max_stars_repo_head_hexsha": "25d290361c21941f77bb9dd8150048132863184a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8... |
#!/usr/bin/env python3
import numpy as np
#import scipy.interpolate as spi
#from scipy.interpolate import griddata
from scipy.interpolate import NearestNDInterpolator
from scipy.interpolate import LinearNDInterpolator
import matplotlib.pyplot as plt
## if using plot_pcolor function as-is:
#params = {'text.latex.pream... | {"hexsha": "fcac1dbae184266b47daf1a9cb982409c3a42ca4", "size": 5720, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/gen3L-sodium/setup_tube0.py", "max_stars_repo_name": "willietheboy/srlife-dev", "max_stars_repo_head_hexsha": "d4c2d28b40d2ee1bf64c7555a913b0a49adffe0f", "max_stars_repo_licenses": ["MIT"... |
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
caminho_base='/home/prbpedro/Development/repositories/github/bootcamp_artificial_intelligence/src/input/'
dados_completos=pd.read_csv(caminho_base + 'airline-passengers.csv')
print(dados_completos.head())
dados_completos.info()
dados_comp... | {"hexsha": "39c0a2affa46406897897c0485059e82505e07a4", "size": 6887, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/modulo2/airline_passengers_analysis.py", "max_stars_repo_name": "prbpedro/bootcamp_machine_learning", "max_stars_repo_head_hexsha": "1713e121cd333c8e80ef05aac0365e886ed9dab1", "max_stars_repo_... |
\section{Mundane Objects}\label{sec:mundaneObjects}
\rowcolors{2}{lightgray}{white}
\begin{longtable}{l | r | r}
Name & Size & Price (In Gold)\\ \hline
Bedroll & M & 5\\
Blanket & M & 3\\
Chain, \passus{2} & S & 20\\
Crowbar & S & 10\\
Fire Steel & T & 10\\
Grappling Hook & S & 50\\
Lantern & S & 100\\
Lock, V... | {"hexsha": "378ba956246ebed1409a7f8608e37622642fb69d", "size": 4097, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "items/items/mundane.tex", "max_stars_repo_name": "NTrixner/RaggedLandsPenAndPaper", "max_stars_repo_head_hexsha": "73781f7cd7035b927a35199af56f9da2ad2c2e95", "max_stars_repo_licenses": ["MIT"], "max... |
import flywheel
import logging
import warnings
import argparse
import os
import pandas as pd
import numpy as np
from fw_heudiconv.cli import tabulate
from fw_heudiconv.cli.export import get_nested
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('flaudit')
def get_sessions(client, project_label, s... | {"hexsha": "e18fd38b59912cf93c62549fb046557df67c4acf", "size": 12818, "ext": "py", "lang": "Python", "max_stars_repo_path": "flaudit/cli/gather_data.py", "max_stars_repo_name": "PennBBL/flaudit", "max_stars_repo_head_hexsha": "dd022fc93367fc850d05a0e2e901ded246efd724", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
"""
Learning with networks that can process sequential data.
"""
from sklearn.base import ClassifierMixin, RegressorMixin, BaseEstimator, TransformerMixin
from sklearn.preprocessing import LabelEncoder, FunctionTransformer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
... | {"hexsha": "d80859d9b44fcd1b985abe8569f0068b3afb1be0", "size": 10485, "ext": "py", "lang": "Python", "max_stars_repo_path": "noxer/rnn.py", "max_stars_repo_name": "noxer-org/noxer", "max_stars_repo_head_hexsha": "45fa20ac7452c4b9c8ab5ea3f93ab47f41ad29cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
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