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// Copyright 2021 Peter Dimov.
// Distributed under the Boost Software License, Version 1.0.
// https://www.boost.org/LICENSE_1_0.txt
#include <boost/system.hpp>
#include <boost/core/lightweight_test.hpp>
#include <cerrno>
namespace sys = boost::system;
enum E
{
none = 0,
einval = EINVAL
};
namespace boost
... | {"hexsha": "e60bb7ab2e0f3acb0a8671f84d0c8e9d5c1dbed2", "size": 6826, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "console/src/boost_1_78_0/libs/system/test/ec_location_test2.cpp", "max_stars_repo_name": "vany152/FilesHash", "max_stars_repo_head_hexsha": "39f282807b7f1abc56dac389e8259ee3bb557a8d", "max_stars_rep... |
using Pkg
Pkg.activate(".")
Pkg.instantiate()
##
using DataFrames
using CSV
using Plots
using StatsPlots
using MCPhylo
using ProgressMeter
##
fpairs = CSV.read("../data/fpairs.txt", DataFrame, header=false)[:,1]
##
theme(:solarized_light)
#
upscale = 1 #8x upscaling in resolution
fntsm = Plots.font("sans-serif", po... | {"hexsha": "561a96d7a7112e87376da930eb56c52a6a23a577", "size": 7329, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "code/visualizePPP.jl", "max_stars_repo_name": "erathorn/phylogeneticTypology", "max_stars_repo_head_hexsha": "ba1fb23eed99b63708291bbbbdf49f1c2a3c1b07", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import torch
def grad_norm(net):
# returns the norm of the gradient corresponding to the convolutional parameters
# count number of convolutional layers
nconvnets = 0
for p in list(filter(lambda p: len(p.data.shape)>1, net.parameters())):
nconvnets += 1
out_... | {"hexsha": "c4a6a5bd04c0b94858f0d263c2f62431cd03e34e", "size": 7758, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/proj_utils.py", "max_stars_repo_name": "MLI-lab/early_stopping_double_descent", "max_stars_repo_head_hexsha": "31caa8bb6a901a94f70f8100c5c274dd45b6a10a", "max_stars_repo_licenses": ["Apache-1... |
# import os
# import fnmatch
#
# filename = '/Users/yanzhexu/Google Drive/Marley Grant Data/CEDM pilot data-selected/benign'
#
#
# for casefile in os.listdir(filename):
# if casefile.startswith('.'):
# continue
# if casefile.startswith('..'):
# continue
# if fnmatch.fnmatch(casefile, '*Icon*... | {"hexsha": "22e6aee64305bccdffd82a668e5b5b2df80b92a7", "size": 8899, "ext": "py", "lang": "Python", "max_stars_repo_path": "testparser.py", "max_stars_repo_name": "joshlyman/ROIShapeAnalysis", "max_stars_repo_head_hexsha": "91e369096662eab9c065d319c0da5a567f272aaa", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/-
Copyright (c) 2018 Johan Commelin. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johan Commelin
-/
import algebra.big_operators.ring
import data.real.pointwise
import algebra.indicator_function
import algebra.algebra.basic
import algebra.order.module
import algebra... | {"author": "nick-kuhn", "repo": "leantools", "sha": "567a98c031fffe3f270b7b8dea48389bc70d7abb", "save_path": "github-repos/lean/nick-kuhn-leantools", "path": "github-repos/lean/nick-kuhn-leantools/leantools-567a98c031fffe3f270b7b8dea48389bc70d7abb/src/data/real/nnreal.lean"} |
/* -*- Mode: c++; tab-width: 2; c-basic-offset: 2; indent-tabs-mode: nil -*- */
/* vim:set softtabstop=2 shiftwidth=2 tabstop=2 expandtab: */
/*
* Software License Agreement (BSD License)
*
* Copyright (c) 2014, Autonomous Intelligent Systems Group, Rheinische
* Friedrich-Wilhelms-Universität Bonn
* All rights r... | {"hexsha": "f8d3940cf5035cd6efb8b25e2c449e4c100dbef9", "size": 6790, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/openni_driver.cpp", "max_stars_repo_name": "AIS-Bonn/ros_openni2_multicam", "max_stars_repo_head_hexsha": "0bc4afe420fe619a84c8cd66c2e2cf8a73f10178", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display, Audio
import librosa
import torch
from fastai.basics import ItemBase
# Parent classes used to distinguish transforms for data augmentation and transforms to convert audio into image
class MyDataAugmentation:
pass
class MySoun... | {"hexsha": "93be3e5cc0a9a57806bacd671c6e7afac068a481", "size": 2708, "ext": "py", "lang": "Python", "max_stars_repo_path": "fastai_audio/audio_clip.py", "max_stars_repo_name": "JoshVarty/AudioTagging", "max_stars_repo_head_hexsha": "d1d038a08e783c11839638e83c691b333317d832", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import numpy as np
import scipy
import copy
import time
import pickle
import os
import math
import psutil
import itertools
import datetime
import shutil
from functions_utils import *
def train_initialization(data_, params, args):
al... | {"hexsha": "4735e4b6ce14fdbd5e7faaea47528c44fafb781e", "size": 10244, "ext": "py", "lang": "Python", "max_stars_repo_path": "training_utils.py", "max_stars_repo_name": "b-mu/kbfgs_neurips2020_public", "max_stars_repo_head_hexsha": "f9e8300211dee764e0a669d50a7176f83a28034a", "max_stars_repo_licenses": ["MIT"], "max_star... |
# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __... | {"hexsha": "30d6d4625869c2b0cbaf7c3299d26c986678054a", "size": 14746, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_test.py", "max_stars_repo_name": "skkestrel/faster-rcnn.pytorch", "max_stars_repo_head_hexsha": "d131ff43440d1c0f0a58c553a2a5695b7a223821", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import numpy.testing as npt
import pytest
@pytest.mark.usefixtures('nae_case')
class TestDataset:
@staticmethod
def test_op(nae_case):
b, ds = nae_case
actual = ds.op(b)
expect = ds.ys
npt.assert_array_almost_equal(actual, expect)
@staticmethod
d... | {"hexsha": "759920498e30545bfd3e4bfc1c83153ef5e27434", "size": 495, "ext": "py", "lang": "Python", "max_stars_repo_path": "impute/tests/test_base.py", "max_stars_repo_name": "nimily/low-rank-impute", "max_stars_repo_head_hexsha": "0b2fb5c911a4b7505e61f9a7e412bcb11bf3e89a", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Implementation of the class `OpenFOAMSimulation`.
"""
import os
import numpy
from scipy import signal
from matplotlib import pyplot
from ..simulation import Simulation
from ..force import Force
class OpenFOAMSimulation(Simulation):
"""
Contains info about a OpenFOAM simulation.
Inherits from class Simula... | {"hexsha": "b63231746a493639f147c227c1045c852f08b263", "size": 14299, "ext": "py", "lang": "Python", "max_stars_repo_path": "external/snake-0.3/snake/openfoam/simulation.py", "max_stars_repo_name": "mesnardo/cuIBM", "max_stars_repo_head_hexsha": "0b63f86c58e93e62f9dc720c08510cc88b10dd04", "max_stars_repo_licenses": ["M... |
from __future__ import print_function
import numpy as np
import Spectrum
import csv
import sys
from scipy import signal
from scipy import stats
from scipy.ndimage.filters import median_filter
import handythread
import multiprocessing
from functools import partial
import dm3_lib as DM3
#from ncempy.io import dm
import n... | {"hexsha": "30c5311c64605a32602bf8e71af255c3e3af8a3a", "size": 17656, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/SpectrumImage.py", "max_stars_repo_name": "shmouses/SpectrumImageAnalysisPy", "max_stars_repo_head_hexsha": "4374e604fb7b493ba84b9675041015b87084e07f", "max_stars_repo_licenses": ["BSD-3-Clau... |
"""
Utility functions for working with Jobman.
"""
import os
import sys
import yaml
import numpy as np
import itertools as it
from keras.callbacks import EarlyStopping, ModelCheckpoint
from adios.datasets import *
from adios.callbacks import HammingLoss
from adios.metrics import f1_measure
from adios.metrics import ... | {"hexsha": "454800219881270a9a8de50cda5bd7eaeec74ce1", "size": 8900, "ext": "py", "lang": "Python", "max_stars_repo_path": "adios/utils/jobman.py", "max_stars_repo_name": "alshedivat/adios", "max_stars_repo_head_hexsha": "242a68fd1f929b150b5c4229beed90c8acb79530", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from django.shortcuts import render
from keras.preprocessing import image
import numpy as np
import tensorflow as tf
from keras.models import load_model
import pickle
global graph,model
#initializing the graph
graph = tf.get_default_graph()
#loading our trained model
print("Keras model loading.......")
model = load... | {"hexsha": "5671e300c4f34d4b1c3dee71e50ac9397a480307", "size": 825, "ext": "py", "lang": "Python", "max_stars_repo_path": "food_ai/utils.py", "max_stars_repo_name": "risusanto/food-recognition-web", "max_stars_repo_head_hexsha": "1f4cea9edca8fabf4a300093abb4b8e090df3c22", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import pandas as pd
import pandas._testing as tm
class TestSeriesSubclassing:
def test_indexing_sliced(self):
s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"))
res = s.loc[["a", "b"]]
exp = tm.SubclassedSeries([1, 2], index=list("ab"))
tm.assert_series_e... | {"hexsha": "86330b7cc69937ddca6b4a69d3796dfe6f93618c", "size": 2084, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/tests/series/test_subclass.py", "max_stars_repo_name": "CJL89/pandas", "max_stars_repo_head_hexsha": "6210077d32a9e9675526ea896e6d1f9189629d4a", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import shutil
import numpy as np
from sklearn.linear_model import LinearRegression
import ujson as json
import os.path as ops
# import matplotlib.pyplot as plt
import os
from tools.generate_prediction_results import generate_prediction_result
class LaneEval(object):
lr = LinearRegression()
pixel_thresh = 20... | {"hexsha": "6cf7802869e0355ed4cba32fa4e69a1debbf5a36", "size": 8960, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/tusimple_eval.py", "max_stars_repo_name": "Steve-Mr/lanenet-lane-detection", "max_stars_repo_head_hexsha": "1c539d29ca119cc92a837f5bb54a169c33a57860", "max_stars_repo_licenses": ["Apache-2.0... |
'''Formats Biopython's Structure object as keras-compatable dataset.'''
import numpy as np
from package.dwt import DWT as DWT
class BatchManager():
def __init__(self, data, wavelet_size=4, verbose=False):
'''Manages a moving window over the dataset based on the given wavelet size.
Paramet... | {"hexsha": "b67489b43996b87de4e0b6c595265dcb3503d0aa", "size": 8038, "ext": "py", "lang": "Python", "max_stars_repo_path": "JustinShaw/ECNN/package/batch_manager.py", "max_stars_repo_name": "rathsidd/Dynamical-Analysis", "max_stars_repo_head_hexsha": "ba9a0a48e0a7a145b943ad0f672e7b600af0bdff", "max_stars_repo_licenses"... |
# -#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# @Author : Ch
# File : bias_module.py
# @Time : 2021/6/3 10:20
import numpy as np
import torch
import torch.nn as nn
from .utils import load_data
class PenaltyModule(nn.Module):
def __init__(self, cfg, statistics, penalty_type, fusion_weight):
... | {"hexsha": "74c1d8a86b42719338a07849af92de42e70827c1", "size": 13417, "ext": "py", "lang": "Python", "max_stars_repo_path": "maskrcnn_benchmark/modeling/roi_heads/relation_head/modules/bias_module.py", "max_stars_repo_name": "ChCh1999/RTPB", "max_stars_repo_head_hexsha": "1066a3bfe4fe1b41eff74fd152936880302a60a2", "max... |
import argparse
import sys
from copy import deepcopy
import logging
from pathlib import Path
import pandas as pd
from syntaxgym import utils
from syntaxgym.suite import Sentence, Region, Suite
from syntaxgym.agg_surprisals import *
from syntaxgym import _load_suite
import json
import numpy as np
parser = argparse.Arg... | {"hexsha": "b2cab5d9b5597ac47f83a8db5b1c47d44c297f38", "size": 2320, "ext": "py", "lang": "Python", "max_stars_repo_path": "score.py", "max_stars_repo_name": "pqian11/sg-eval", "max_stars_repo_head_hexsha": "b4013bd1c9b9a33e6a904308a811fa2c4dffd9d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
[STATEMENT]
lemma properL_notEmp[simp]: "properL cl \<Longrightarrow> cl \<noteq> []"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. properL cl \<Longrightarrow> cl \<noteq> []
[PROOF STEP]
unfolding properL_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. cl \<noteq> [] \<and> (\<forall>c\<in>set cl. proper c)... | {"llama_tokens": 147, "file": "Probabilistic_Noninterference_Language_Semantics", "length": 2} |
#!/usr/bin/env python
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
font = {'family' : 'Times New Roman' ,
'weight' : 'normal' ,
'size' : '25'}
plt.rc('font',**font)
plt.figure(figsize=(16,8))
data=np.loadtxt('rixs.da... | {"hexsha": "b29221565775df9c6834a2f9beb7c7e15828a68a", "size": 2680, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/more/RIXS/Ba2YOsO6/t2g_single_atom/plot_rixs_map.py", "max_stars_repo_name": "danielballan/edrixs", "max_stars_repo_head_hexsha": "57fbd11ba9aaeaa393c3e2f06af41e4e386749e4", "max_stars_re... |
#!/usr/bin/env python
"""
Some visualization utilities.
"""
import os
import numpy as np
from matplotlib import pyplot as plt
import mpl_toolkits.mplot3d as mplt
import scipy
import point_cloud
def plot_mesh(mesh, filepath = ''):
"""
Plot a mesh.
:param mesh: mesh to plot
:type mesh: mesh.Mesh
:p... | {"hexsha": "c1f5425ce898265cfb3429650137a9f3d900800b", "size": 14784, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/lib/visualization.py", "max_stars_repo_name": "davidstutz/daml-shape-completion", "max_stars_repo_head_hexsha": "d0d1d1c26ba547d02c4102077aeb0a1ea46c4e50", "max_stars_repo_licenses": ["Unlic... |
\documentclass[fleqn]{article}
\usepackage[margin=1in]{geometry}
\usepackage{amsmath}
\usepackage[colorlinks=true]{hyperref}
\usepackage{tikz}
\usetikzlibrary{calc,patterns,angles,quotes}
\begin{document}
\begin{center}
{\bfseries Solution to assignment \#2}\\
Introduction to GR, 2020 Fall\\
International Centr... | {"hexsha": "6a69e90296a8be5d3ef67068ce65f70623b1d901", "size": 7108, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sol-assignment-2/Solution_assignment_2_icts_igr_2020_fall.tex", "max_stars_repo_name": "mdarifshaikh/GR-Fall-2020-ICTS", "max_stars_repo_head_hexsha": "4b651c965b58d899a98f6ebe838a6bc30c3c37ef", "ma... |
integer:: NN = 4, u
integer:: A1 = 0
integer(len=NN):: Data
do u = 0, NN
Data(u) = u * 9
A1 = A1 + Data(u)
print *, "Data[", u, "]=", Data(u)
end do
print *, "A1=", A1 | {"hexsha": "c5311730de0410069aaef37a21fc9fcb5fe23af7", "size": 170, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Auto-generated-tasks/Difficulty-1/Task-1/Solution.f90", "max_stars_repo_name": "Alexxx180/EasyTasks", "max_stars_repo_head_hexsha": "3661e062f0dc48e10d463816c4ab725d1eec6cc7", "max_stars_repo_lic... |
import pickle
import os
from time import time
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.grid_search import GridSearchCV
from sklearn.datasets import fetch_mldata
from sklearn import cross_validation
from sklearn.neighbors import KNeighborsClassifier
from scipy import ndimage
import... | {"hexsha": "a4dbb96c6bec5dc43fdffecce2a4ce8241f6b156", "size": 3010, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/digit_recognition.py", "max_stars_repo_name": "abhishekstha98/HandW-Recog", "max_stars_repo_head_hexsha": "419516810346376cb73095a45d1b47c95268a8bb", "max_stars_repo_licenses": ["Apache-2.0... |
import argparse
import os
import sys
parser = argparse.ArgumentParser()
parser.add_argument('--lr', type=float, default=2.5e-4)
parser.add_argument('--eps', type=float, default=1e-5)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--epochs', type=int, default=5)
parser.add_argument('--batch_siz... | {"hexsha": "538767ae1339c742663e926ce9bc2abb667c6adf", "size": 5873, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl/breakout.py", "max_stars_repo_name": "bcrafton/icsrl-deep-learning", "max_stars_repo_head_hexsha": "e3616982d1dda5f978d61d6591c91cb0da76ab02", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from attrbench.suite import SuiteResult
from attrbench.metrics import Metric
from attrbench.lib import AttributionWriter
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
import numpy as np
from os import path
from typing import Dict, Callable
import logging
class Suite:
"""
Represent... | {"hexsha": "7688b6f973fa875b0fc7ad580ee4e0beecfb5529", "size": 7327, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/attrbench/suite/suite.py", "max_stars_repo_name": "zoeparman/benchmark", "max_stars_repo_head_hexsha": "96331b7fa0db84f5f422b52cae2211b41bbd15ce", "max_stars_repo_licenses": ["MIT"], "max_star... |
// Copyright Takatoshi Kondo 2016
//
// 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)
#if !defined(MQTT_NULL_STRAND_HPP)
#define MQTT_NULL_STRAND_HPP
#include <boost/asio.hpp>
#include <mqtt/utility.hpp>
names... | {"hexsha": "eaf21d3ea5c2a3ad085483134eb84f7798893e54", "size": 824, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/mqtt/null_strand.hpp", "max_stars_repo_name": "airmap/mqtt_client_cpp", "max_stars_repo_head_hexsha": "778e386010eb605e0637d7272e5a193c2e6b28d6", "max_stars_repo_licenses": ["BSL-1.0"], "max_... |
from sstcam_sandbox import get_data, get_plot
from CHECLabPy.plotting.setup import Plotter
import pickle
import pandas as pd
from matplotlib.ticker import FuncFormatter
import datetime
import numpy as np
from matplotlib import pyplot as plt
from IPython import embed
class Timeseries(Plotter):
def plot(self, x, y)... | {"hexsha": "a002508b067324bb45ebb80b895fa8cf304cad35", "size": 2210, "ext": "py", "lang": "Python", "max_stars_repo_path": "sstcam_sandbox/d200122_pointing/open_pkl.py", "max_stars_repo_name": "watsonjj/CHECLabPySB", "max_stars_repo_head_hexsha": "91330d3a6f510a392f635bd7f4abd2f77871322c", "max_stars_repo_licenses": ["... |
\subsection{Electroweak theory}
\label{ewktheory}
The electroweak interaction is the unified description of two of the four known fundamental interactions of nature: electromagnetism and the weak interaction.
It is based on the gauge group $SU(2)_{L} \times SU(1)_{Y}$, in which $L$ is the left-handed fields and $Y$ is ... | {"hexsha": "be06c17e2543e5e002ea72d75e4b71002357e87e", "size": 4216, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/Theory/ewktheory.tex", "max_stars_repo_name": "zhuhel/PhDthesis", "max_stars_repo_head_hexsha": "55ec32affb5c105143798989d78043467c88da8e", "max_stars_repo_licenses": ["LPPL-1.3c"], "max_st... |
'''
Author: Liu Xin
Date: 2021-11-13 19:11:06
LastEditors: Liu Xin
LastEditTime: 2021-11-25 15:44:12
Description: 静态工具库
FilePath: /CVMI_Sementic_Segmentation/utils/static_common_utils.py
'''
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import warnings
from socket import g... | {"hexsha": "44be7e179d9f24df461429aeb0c1ef7aff9ab585", "size": 3060, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/static_common_utils.py", "max_stars_repo_name": "UESTC-Liuxin/CVMI_Sementic_Segmentation", "max_stars_repo_head_hexsha": "dc5bf6e940cf6961ef65abb6e7ec372f29d55249", "max_stars_repo_licenses"... |
#######################################################################
# print to stderr, since that is where Pkg prints its messages
eprintln(x...) = println(STDERR, x...)
# creating `GeoEfficiency` folder at the home directory.
println("INFO: Creating 'GeoEfficiency' folder at '$(homedir())'.....")
try
cp(joinpa... | {"hexsha": "447c97bf6c48f2765f41fdb6bd4dfabd2797f08a", "size": 569, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "deps/build.jl", "max_stars_repo_name": "JuliaPackageMirrors/GeoEfficiency.jl", "max_stars_repo_head_hexsha": "d41245477df85153aab7103cf9b6e118d23e43e3", "max_stars_repo_licenses": ["MIT"], "max_star... |
import sys, re, time, string
import numpy;
import scipy;
import scipy.special;
import nltk;
from inferencer import compute_dirichlet_expectation;
from inferencer import Inferencer;
class Hybrid(Inferencer):
def __init__(self,
hash_oov_words=False,
number_of_samples=10,
... | {"hexsha": "c1ef9455ce7a81c3e9f70bb0d1fede736243cc86", "size": 4946, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/fixvoc/hybrid.py", "max_stars_repo_name": "kzhai/InfVocLDA", "max_stars_repo_head_hexsha": "05a87890d613b07f7b0c2d2bb6c79aad39e2f75d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# LibFTD2XX.jl - High Level Module
#
# By Reuben Hill 2019, Gowerlabs Ltd, reuben@gowerlabs.co.uk
#
# Copyright (c) Gowerlabs Ltd.
#
# This module contains methods and functions for interacting with D2XX devices.
# It calls functions from the submodule `Wrapper` which in turn call Functions
# from the FT D2XX library. ... | {"hexsha": "b9734c92b944ac50ef5457538b29e85aaabcc548", "size": 23653, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/LibFTD2XX.jl", "max_stars_repo_name": "anders-jakobsson/LibFTD2XX.jl", "max_stars_repo_head_hexsha": "b6151ad1b147c596b6d92a06179e685a6a8e6266", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
clear;
t0=0.01;
tf=0.05;
b0=[1.2 2.2 1.8];
[t,b]=ode45('dfun2',[t0, tf],b0);
plot(b(:,1),b(:,2));
hold on
xlabel('x1');
ylabel('x2'); | {"author": "sfvsfv", "repo": "Mathematical-modeling", "sha": "cef1a3688246851f067777b3599b1b3831d3d948", "save_path": "github-repos/MATLAB/sfvsfv-Mathematical-modeling", "path": "github-repos/MATLAB/sfvsfv-Mathematical-modeling/Mathematical-modeling-cef1a3688246851f067777b3599b1b3831d3d948/\u7f8e\u8d5bA\u9898\u5e38\u89... |
import pycqed as pq
import matplotlib.pyplot as plt
import os
from pycqed.analysis import measurement_analysis as ma
from numpy.testing import assert_almost_equal
class TestSSRODiscriminationAnalysis:
@classmethod
def setup_class(cls):
cls.datadir = os.path.join(pq.__path__[0], 'tests', 'test_data')
... | {"hexsha": "bb8a71d85c8a875f95b9f9193dd879867146ebf8", "size": 1317, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycqed/tests/test_butterfly_analysis.py", "max_stars_repo_name": "nuttamas/PycQED_py3", "max_stars_repo_head_hexsha": "1ee35c7428d36ed42ba4afb5d4bda98140b2283e", "max_stars_repo_licenses": ["MIT"]... |
/-
Copyright (c) 2022 Kyle Miller. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kyle Miller
-/
import combinatorics.simple_graph.connectivity
/-!
# Acyclic graphs and trees
> THIS FILE IS SYNCHRONIZED WITH MATHLIB4.
> Any changes to this file require a correspondin... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/combinatorics/simple_graph/acyclic.lean"} |
//==============================================================================
// Copyright 2003 - 2011 LASMEA UMR 6602 CNRS/Univ. Clermont II
// Copyright 2009 - 2011 LRI UMR 8623 CNRS/Univ Paris Sud XI
//
// Distributed under the Boost Software License, Version 1.0.
// Se... | {"hexsha": "0ad6e8f1ddaf9074210461045e4733f87e1fcbbc", "size": 1320, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/boost/simd/arithmetic/include/boost/simd/toolbox/arithmetic/functions/simd/common/fma.hpp", "max_stars_repo_name": "timblechmann/nt2", "max_stars_repo_head_hexsha": "6c71f7063ca4e5975c9c0198... |
import time
import graphs.graph_utils as graph_utils
import numpy as np
import processing.processing_utils as processing
from nltk.tag import pos_tag
def generate_summary_dutta(text_as_sentences_without_footnotes, summary_size, threshold=0.05):
start_time = time.time()
sentences = sentences_for_dutta(text_as_... | {"hexsha": "d489f98d8148b81329a470d3ae32f59d405ab97e", "size": 4485, "ext": "py", "lang": "Python", "max_stars_repo_path": "thesis-project/graphs/dutta.py", "max_stars_repo_name": "AvramPop/bachelor-thesis", "max_stars_repo_head_hexsha": "b0f5f9d349b53e79acd7cc10140e551112e672a0", "max_stars_repo_licenses": ["MIT"], "m... |
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import collections
import copy
import dotdict
import json
import numpy as np
import os
import random
import regex
import tempfile
import torch
import torch.nn as nn
from glob import glob
from chinese_converter import to_traditional, to_simplified
from ... | {"hexsha": "7ee56436ee0bdaca9868713e47647df6fec4dc33", "size": 8944, "ext": "py", "lang": "Python", "max_stars_repo_path": "align/train.py", "max_stars_repo_name": "facebookresearch/bitext-lexind", "max_stars_repo_head_hexsha": "e4999cd08a977572e476b0a8c1381520ba02a443", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from utils import get_features_fewshot_full_library
import os, random
import torch
import torch.nn as nn
import argparse
model_names = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'densenet121', 'densenet161', 'densenet169', 'densenet201']
parser = argparse.Argument... | {"hexsha": "3896dc2a9d4f246634db176b5ecf68f5232266e7", "size": 6541, "ext": "py", "lang": "Python", "max_stars_repo_path": "classifier_ensemble_old.py", "max_stars_repo_name": "arjish/PreTrainedFullLibrary_FewShot", "max_stars_repo_head_hexsha": "c52d3374e5e1d5fe20f428cc2f599cb4b36847ef", "max_stars_repo_licenses": ["M... |
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.model_selection import GridSearchCV
from common_functions.rolling_windows_validation import rolling_windows_validation
class custom_gridsearch_cv(BaseEstimator):
def __init__(self, STRATEGY_SELECTED):
self.STRATEGY_SELECTED = STRATEGY... | {"hexsha": "b06903cf45a1e93d7b970c61edf37b02cc97b1da", "size": 4402, "ext": "py", "lang": "Python", "max_stars_repo_path": "pipoh/concrete_factories/gscv_folder/gscv_configuration.py", "max_stars_repo_name": "faprieto96/pyInvestment", "max_stars_repo_head_hexsha": "a5c1bdb7823df0df215c3ac55dc8427fd18b2e15", "max_stars_... |
// Copyright 2017 Rodeo FX. All rights reserved.
#include "mtoaScene.h"
#include <link.h>
#include <boost/foreach.hpp>
// Returns true if str ends with ending
inline bool endsWith(const std::string& str, const std::string& ending)
{
if (ending.size() > str.size()) {
return false;
}
return std:... | {"hexsha": "425edd905c6c7120facab5b755dda8aca7a07171", "size": 4004, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "walter/maya/walterMtoaConnection/mtoaScene.cpp", "max_stars_repo_name": "all-in-one-of/OpenWalter", "max_stars_repo_head_hexsha": "c2034f7fac20b36ffe3e500c01d40b87e84e2b97", "max_stars_repo_licenses... |
from typing import Callable, Optional, Tuple, Union
import numpy as np
import pandas as pd
from utipy.utils.messenger import Messenger, check_messenger
def print_nan_stats(
x: Union[np.ndarray, pd.DataFrame],
message: str,
messenger: Optional[Callable] = Messenger(
verbose=True,... | {"hexsha": "b2358cb81a24c84bdb30165148fabeb10b45b46a", "size": 1767, "ext": "py", "lang": "Python", "max_stars_repo_path": "utipy/array/nan_stats.py", "max_stars_repo_name": "LudvigOlsen/utipy", "max_stars_repo_head_hexsha": "c287f7eed15b3591118bba49ecdfc2b2605f59a0", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import pandas as pd
import numpy as np
#TODO remove *, at least use the name of the module
from zucaml.util import *
import zucaml.util as mlutil
def create_reset(df, item, time_ref, order):
if order is None:
order = [True, True]
df = df.sort_values([item, time_ref], ascending = order)
... | {"hexsha": "2392b7cc820dfea952b85d7903bedba80e85fbf8", "size": 5516, "ext": "py", "lang": "Python", "max_stars_repo_path": "zucaml/feature_engineering.py", "max_stars_repo_name": "GustavoBMG/earthquake-prediction", "max_stars_repo_head_hexsha": "a3278bb22385c78013269356990a60d60aba50f1", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
"""
ml_fits.py
Basic code for building models in scikit-learn and xgboost
For intermediate-level code, see ml_fits2.py
"""
import argparse
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
from sklearn.metrics import accuracy_score, mean_absolute_error
from sklearn.m... | {"hexsha": "879eac13b6c13afab975570a37678e8048edefc8", "size": 2720, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine-learning/ml_fits.py", "max_stars_repo_name": "nathanielng/python-snippets", "max_stars_repo_head_hexsha": "d310f074acc1ea7fdb41b2db3ab69406b96a18ca", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
import sys
sys.path.append(".")
from ai.action.movement.movements.basic import *
from ai.action.movement.movements import sit
import ai.actionplanner
def main(mars, times=3, lookaround=True):
sit.main(mars)
ai.actionplanner.ActionPlanner.sleep(0.2)
treading(mars, times, looka... | {"hexsha": "dd914d1d12ad75549d9cc9238359c94fcac4d5f9", "size": 7686, "ext": "py", "lang": "Python", "max_stars_repo_path": "ai/action/movement/movements/treading.py", "max_stars_repo_name": "elephantrobotics-joey/marsai", "max_stars_repo_head_hexsha": "d7cc2a807727dddb615b2a1640dba5f9656f1da0", "max_stars_repo_licenses... |
'''
Created on 24 Feb 2014
@author: maxz
'''
import numpy as np
from ..util.pca import PCA
def initialize_latent(init, input_dim, Y):
Xr = np.asfortranarray(np.random.normal(0, 1, (Y.shape[0], input_dim)))
if 'PCA' in init:
p = PCA(Y)
PC = p.project(Y, min(input_dim, Y.shape[1]))
Xr[:... | {"hexsha": "1e19a3216e0ece17dcd3be1189d799782f2ad022", "size": 874, "ext": "py", "lang": "Python", "max_stars_repo_path": "GPy/util/initialization.py", "max_stars_repo_name": "ekalosak/GPy", "max_stars_repo_head_hexsha": "ff82f12c3d321bfc3ce6615447fad25aea9de6bd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
# -*- coding: utf-8 -*-
from numpy import *
from gridworld import GridworldEnv
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('-alg', dest='alg', default='q', type=str) # q for q-learn, s for sarsa(λ)
parser.add_argument('-size', dest='size', default=5, type=int) ... | {"hexsha": "1699c1f31d6386ddb12850ce0a70ed27005956e1", "size": 3383, "ext": "py", "lang": "Python", "max_stars_repo_path": "PA4/train.py", "max_stars_repo_name": "xueguangl23/Machine_Learning_Coursework", "max_stars_repo_head_hexsha": "fa164ff330f24150e3c788ad653d6563bdda7e58", "max_stars_repo_licenses": ["Apache-2.0"]... |
### A Pluto.jl notebook ###
# v0.12.18
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
lo... | {"hexsha": "ddf26f7f492b6d42894fe2f1a67ee32f9954a312", "size": 2886, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/searchmnist.jl", "max_stars_repo_name": "sadit/NeighborhoodApproximationIndex.jl", "max_stars_repo_head_hexsha": "ce8a1c1df370e204a4ab4dc6f8fec54512f3ef7e", "max_stars_repo_licenses": ["MI... |
import numpy as np
import torch
import tqdm
from torch import nn
class MyLayerNormNoAffine(nn.Module):
def __init__(self, normalized_shape, eps=1e-5):
super().__init__()
self.normalized_shape = normalized_shape
self.eps = eps
def forward(self, x):
assert len(x.shape) == 4
... | {"hexsha": "e3c16eb28423ac0ce2909d7d0868e4708a1b4ada", "size": 2352, "ext": "py", "lang": "Python", "max_stars_repo_path": "dl_norms/layer_norm.py", "max_stars_repo_name": "dniku/dl-norms", "max_stars_repo_head_hexsha": "0f1eef942bd318ac988ec7dfa9caea300d17e82a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
#include <iostream>
#include <Eigen/Core>
#include "celerite/solver/direct.h"
#include "celerite/solver/cholesky.h"
#define DO_TEST(NAME, VAR1, VAR2) \
{ \
double base, comp, delta; \
base = VAR1... | {"hexsha": "ad05da339aa94803103e4cc41eedc1647addfe90", "size": 4082, "ext": "cc", "lang": "C++", "max_stars_repo_path": "cpp/src/test_solvers.cc", "max_stars_repo_name": "dfm/ess", "max_stars_repo_head_hexsha": "09ee14e516bb3bc3b517c0c1b6716eaeb28183b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 172.0, "ma... |
using OrdinaryDiffEq, DiffEqDevTools, ParameterizedFunctions, Plots, ODE, ODEInterfaceDiffEq, LSODA, Sundials
gr() #gr(fmt=:png)
using LinearAlgebra
f = @ode_def Orego begin
dy1 = p1*(y2+y1*(1-p2*y1-y2))
dy2 = (y3-(1+y1)*y2)/p1
dy3 = p3*(y1-y3)
end p1 p2 p3
p = [77.27,8.375e-6,0.161]
prob = ODEProblem(f,[1.0,2... | {"hexsha": "42ab6db3c69eb14ef5e450f83df1a745bc904511", "size": 6445, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "script/StiffODE/Orego.jl", "max_stars_repo_name": "SciML/SciMLBenchmarksOutput", "max_stars_repo_head_hexsha": "6e81c96f646ee1310f9c18f8c051a281bd8095dd", "max_stars_repo_licenses": ["MIT"], "max_s... |
import torch
import numpy
from pathlib import Path
from torchvision import datasets, models, transforms
import torch.nn as nn
import torchvision
import numpy as np
import matplotlib.pyplot as plt
# from tools.plotcm import plot_confusion_matrix
# from sklearn.metrics import confusion_matrix
from tqdm import tqdm
import... | {"hexsha": "6bd308f12328545f0f3d16625f3664108d103980", "size": 1932, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "JerryJack121/Automatic-optical-defect-detection", "max_stars_repo_head_hexsha": "d620649dfaba5b9a03c9c53caf628704b151d609", "max_stars_repo_licenses": ["MIT"], "m... |
"""
ai-utilities - machine_learning/label_rank.py
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT License.
"""
import numpy as np
import pandas as pd
def score_rank(scores):
"""
Add Rank to Series
:param scores: Series to Rank
:return: Ranked Series
"""
retur... | {"hexsha": "d176bf66ebfa22f0b2a33ebdc7aead60c40f1e05", "size": 1032, "ext": "py", "lang": "Python", "max_stars_repo_path": "azure_utils/machine_learning/label_rank.py", "max_stars_repo_name": "Bhaskers-Blu-Org2/ai-utilities", "max_stars_repo_head_hexsha": "b6c097dddff00ae4f7321da52653d9d6d8a94884", "max_stars_repo_lice... |
// __BEGIN_LICENSE__
// Copyright (c) 2009-2013, United States Government as represented by the
// Administrator of the National Aeronautics and Space Administration. All
// rights reserved.
//
// The NGT platform is licensed under the Apache License, Version 2.0 (the
// "License"); you may not use this file excep... | {"hexsha": "bec549df581c15b01f57cde178766cbdf74bddf1", "size": 27586, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/asp/Core/LocalAlignment.cc", "max_stars_repo_name": "Site-Command/StereoPipeline", "max_stars_repo_head_hexsha": "d6ff7ab9aebe625eb2f97f386eb681050480f312", "max_stars_repo_licenses": ["Apache-2... |
###############
# Repository: https://github.com/lgervasoni/urbansprawl
# MIT License
###############
import osmnx as ox
import pandas as pd
import geopandas as gpd
import numpy as np
from .tags import height_tags
from ..settings import storage_folder
# Format for load/save the geo-data ['geojson','shp']
geo_format... | {"hexsha": "031d86401010ca9cc67a6538821881ca06d14319", "size": 8009, "ext": "py", "lang": "Python", "max_stars_repo_path": "urbansprawl/osm/utils.py", "max_stars_repo_name": "Oslandia/urbansprawl", "max_stars_repo_head_hexsha": "afbc1da6ce640569571d26900a2cc97a063fb0a9", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import itertools
import pandas as pd
from sklearn.decomposition import FastICA
from sklearn import linear_model
from scipy.optimize import linear_sum_assignment
from graphviz import Digraph
import scipy.stats as stats
from tqdm import tqdm
"""
Input:
X:
shape (n_samples, n_variables)
... | {"hexsha": "d2c85c6d482d938e1b90f82dfcc1861867b7d181", "size": 18101, "ext": "py", "lang": "Python", "max_stars_repo_path": "lingam_fast.py", "max_stars_repo_name": "MorinibuTakeshi/LiNGAM-fast", "max_stars_repo_head_hexsha": "404204fbafcef7f1deb6bcdcb9bedc50d8a74594", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import pickle
import os
import sys
import pathlib
import numpy as np
from torch import optim
from torch.utils.data import DataLoader
import torch
from torch.nn.utils import clip_grad_norm_
from tqdm import tqdm
from tensorboardX import SummaryWriter
abs_path = pathlib.Path(__file__).parent.absolute()
sys.path.append(... | {"hexsha": "7cf6ec8b1a7f3043d18dc0b6ddabb02774287682", "size": 6258, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/train.py", "max_stars_repo_name": "leon2milan/pytorchPGN", "max_stars_repo_head_hexsha": "4f43afef5b97709060063cb547c658e23b42e552", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
module UniDerivative
"""
Compute derivative of a univariate function.
"""
abstract type DerivativeMethod end
struct CentralDiff <: DerivativeMethod
h
CentralDiff(h = cbrt(eps())) = new(h)
end
function df(param::CentralDiff, f, x::Real)::Real
(f(x + param.h / 2) - f(x - param.h / 2)) / param.h
end
struct... | {"hexsha": "f94d496dc91791dfa83137c3bb4761a37fd91031", "size": 508, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/UniDerivative.jl", "max_stars_repo_name": "gshaikov/Algopt.jl", "max_stars_repo_head_hexsha": "0fced0ae1963b68cf1c6118ab03e6eb6ccc0e6c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import os
import random
import numpy as np
import torch
from config import LEARNING_RATE
from torch.utils.tensorboard import SummaryWriter
checkpoint_dir = 'models'
GAMMA = 0.95
use_cuda = torch.cuda.is_available()
class DQN:
def __init__(self, model_name, replay_memory, target=False):
self.online_network... | {"hexsha": "2372799a96c1c4cd5ad4baefd6287c834161b20f", "size": 5964, "ext": "py", "lang": "Python", "max_stars_repo_path": "dqn.py", "max_stars_repo_name": "andy920262/Reinforcement-Learning-for-Self-Driving-Cars", "max_stars_repo_head_hexsha": "d6bd3571101c829eb7082a96305c355eb09eebcc", "max_stars_repo_licenses": ["Ap... |
/**
* Author: Junjie Shi, Ardalan Naseri
*
* This file is responsible for inferring the relationship between a pair of individuals.
*
*/
#include <algorithm>
#include <iostream>
#include <unordered_set>
#include <boost/functional/hash.hpp>
#include "parser.hpp"
#include "mapper.hpp"
#include "classifier.hpp"
/... | {"hexsha": "5a18f98cf1d044778f8b286647f6a6902ff3c856", "size": 3577, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "classifier.cpp", "max_stars_repo_name": "ZhiGroup/RAFFI", "max_stars_repo_head_hexsha": "e6b89602b6b898e82ec9f9ebc27074182606c724", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4.0, "max_s... |
import matplotlib
matplotlib.use('agg')
import sys
import random
import gc
sys.path = ['../'] + sys.path
from deepmass import map_functions as mf
from deepmass import cnn_keras as cnn
import numpy as np
import time
import os
from scipy.stats import pearsonr
import script_functions
print(os.getcwd())
rescale_fa... | {"hexsha": "cbf1f5a8ddc65acd59e645ee8fa465aa80a72bc2", "size": 5621, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_scripts/test_wiener_prototype.py", "max_stars_repo_name": "david-w-a/DeepMass", "max_stars_repo_head_hexsha": "c33a3a6ac108c2b13f4f45d45c721229184d04fe", "max_stars_repo_licenses": ["MIT"], "m... |
"""
Copyright 2013 Steven Diamond, Eric Chu
This file is part of CVXPY.
CVXPY is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
CVXPY is dis... | {"hexsha": "6ebba89b7b3c9b5aa46b3e33a97a406923df9423", "size": 3474, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tools/ecos/cvxpy/cvxpy/constraints/nonlinear.py", "max_stars_repo_name": "riadnassiffe/Simulator", "max_stars_repo_head_hexsha": "7d9ff09f26367d3714e3d10be3dd4a9817b8ed6b", "max_stars_repo_lic... |
using PyPlot, PrintFig
x = linspace(0,2pi,100);
fig = plt.figure();
plot(x,sin(x),color="red");
title("Test plot");
xlabel(L"$x$");
ylabel(L"$\sin(x)$");
printfig(fig) # Saves to file "FIG1.tex"
printfig(fig,filename="test.tex") # Saves to file "test.tex" | {"hexsha": "503b1399869964b204348cbf80a6a288038bd923", "size": 256, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/basic.jl", "max_stars_repo_name": "1oly/PrintFig.jl", "max_stars_repo_head_hexsha": "de453e951d511cbc94aad7bf09c1a538b801ffa2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
# Copyright 2021 Konstantin Herb, Pol Welter. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | {"hexsha": "2501a5abe58868bb9d5427c7e78c21cb1af6fa69", "size": 11720, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/pyparament/parament/parament.py", "max_stars_repo_name": "parament-integrator/parament", "max_stars_repo_head_hexsha": "2e871824ac40499cec69e516bf90be590dfde0ab", "max_stars_repo_licen... |
module SimpleTrees
using ..Factories: Inference,Argument,Parameter,FunctionFactory,TypeFactory
using ..Factories: addfields!,addparams!,addargs!,addwhereparams!,extendbody!
using ..Factories: MixEscaped,Escaped,UnEscaped,rawexpr
using ..TypeTraits: efficientoperations
export simpletreedepth,simpletreewidth
export Abs... | {"hexsha": "fb507dc2d49f3f0f056bb04687fc9600858cf383", "size": 17664, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Prerequisites/SimpleTrees.jl", "max_stars_repo_name": "JuliaTagBot/QuantumLattices.jl", "max_stars_repo_head_hexsha": "fcaedf19ecf783ea3cd0af611595d0d961e3b509", "max_stars_repo_licenses": ["A... |
import numpy as np
from copy import copy
from .util import interpret_array
class _AnyDensity(object):
# make Proposal and Density have the same __init__
def __init__(self, ndim, is_symmetric=False):
self.ndim = ndim
self.is_symmetric = is_symmetric
class Proposal(_AnyDensity):
def prop... | {"hexsha": "2c78d3c1e3445b45c8245e1eb06b7292fe157558", "size": 3637, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/hepmc/core/density.py", "max_stars_repo_name": "mathisgerdes/monte-carlo-integration", "max_stars_repo_head_hexsha": "533d13eeb538fec46f8d5ed00e780153b68ba7d9", "max_stars_repo_licenses": ["MI... |
# -*- coding: utf-8 -*-
"""
This script that provides basic plotting functionality for PhoREAL
Copyright 2019 Applied Research Laboratories, University of Texas at Austin
This package is free software; the copyright holder gives unlimited
permission to copy and/or distribute, with or without modification, as
long as ... | {"hexsha": "fe6c360e18d5ec243b41cd6e5975a525139ed03f", "size": 41673, "ext": "py", "lang": "Python", "max_stars_repo_path": "PhoREAL/source_code/icesatPlot.py", "max_stars_repo_name": "aboestpetersen/icesat2_canopy_heights", "max_stars_repo_head_hexsha": "556078d72036f18d00e645fb731ecca53320aa58", "max_stars_repo_licen... |
import numpy as np
from torch import Tensor
from train_utils import training_step, model_validation
def test_train_step(module_dict):
model = module_dict["model"]
optimizer = module_dict["optimizer"]
criterion = module_dict["criterion"]
train_dataloader = module_dict["train_dataloader"]
for ba... | {"hexsha": "a15c4e651ef15f05addd613512049846baad412d", "size": 779, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/train_utils/test_train_val_steps.py", "max_stars_repo_name": "mintusf/land_cover_segmentation", "max_stars_repo_head_hexsha": "6e136c076d50aa6a85b9628e5cdcbedcf0dd9836", "max_stars_repo_licen... |
from init import *
from sinc_fun import stability_inc_linear, stability_inc_log, stability_inc_exp
import numpy as np
def get_r(RI, ISI):
start_stability = 5
r = np.exp(np.log(0.9) * ISI / start_stability)
return r * np.exp(np.log(0.9) * RI / (start_stability * stability_inc_linear(start_stability, r))) +... | {"hexsha": "a92060918034fdac26b5ae13ea1c11602a03cd4f", "size": 777, "ext": "py", "lang": "Python", "max_stars_repo_path": "Analysis/optISI.py", "max_stars_repo_name": "L-M-Sherlock/space_repetition_simulators", "max_stars_repo_head_hexsha": "66dbc5fc23db70c86e0ca9d4b80fc00b1df78ab1", "max_stars_repo_licenses": ["MIT"],... |
dyn.load('/Library/Java/JavaVirtualMachines/jdk1.8.0_131.jdk/Contents/Home/jre/lib/server/libjvm.dylib')
library(rJava)
setwd("/Users/mengmengjiang/all datas/chap4")
library(xlsx)
#读取数据
q2 <- read.xlsx("f_eject_2.xls", sheetName = "single_k2", header = TRUE)
q3 <- read.xlsx("f_eject_2.xls", sheetName = "single_k3", he... | {"hexsha": "cb8e7203c629ff734017b8001d6ae1c692cc671d", "size": 8795, "ext": "r", "lang": "R", "max_stars_repo_path": "thesis/chap4/fig4-10.r", "max_stars_repo_name": "shuaimeng/r", "max_stars_repo_head_hexsha": "94fa0cce89c89a847b95000f07e64a0feac1eabe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
# Copyright (c) 2016 The UUV Simulator Authors.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | {"hexsha": "c2cf8cdec88f8f2fae6d25af3d5e2c25b97cafb5", "size": 1251, "ext": "py", "lang": "Python", "max_stars_repo_path": "uuv_simulator/uuv_evaluation/src/bag_evaluation/metrics/mean_abs_thrust.py", "max_stars_repo_name": "laughlinbarker/underice_ekf", "max_stars_repo_head_hexsha": "d74a83b2d02cef986fc904cf588a408382... |
#include <boost/units/is_unit_of_system.hpp>
| {"hexsha": "accfec373740d8ff132709f7ba4b8faa37c179de", "size": 45, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_units_is_unit_of_system.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1... |
###############################################################################
# plot_maptwohz.py: make a plot of 2nd scale height vs. first scale height
###############################################################################
import sys
import pickle
import numpy
import matplotlib
matplotlib.use('Agg')
from ga... | {"hexsha": "bfdf77f10ac54ae11125772564389f4a62025b9c", "size": 2093, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/plot_maptwohz.py", "max_stars_repo_name": "jobovy/apogee-maps", "max_stars_repo_head_hexsha": "0316e95bdf9b85f3f4b758e8e67dc197d228288f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
[STATEMENT]
lemma Macaulay_list_distinct_lt:
assumes "x \<in> set (Macaulay_list ps)" and "y \<in> set (Macaulay_list ps)"
and "x \<noteq> y"
shows "lt x \<noteq> lt y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lt x \<noteq> lt y
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. lt x ... | {"llama_tokens": 3281, "file": "Groebner_Bases_Macaulay_Matrix", "length": 34} |
import tensorflow as tf
import numpy as np
import pandas as pd
import math
import pickle
import os
import json
from datetime import datetime
# from IPython import embed
import tensorflow.contrib.slim as slim
from scipy.sparse import coo_matrix
from graph import adjacency, distance_scipy_spatial
from sklearn.preprocessi... | {"hexsha": "3d231b9c6ce93b30fe0dc5f9bebbfb87dcecd7b2", "size": 7324, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "qlongyinqw/gcn-japan-weather-forecast", "max_stars_repo_head_hexsha": "645f8239b1a913c0b6fc7b7b9ecaf87c0e5e9ea8", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#include <Settings.h>
#include <string>
#include <utilstrencodings.h>
#include <DataDirectory.h>
#include <boost/algorithm/string/predicate.hpp>
#include <boost/algorithm/string/case_conv.hpp>
#include <boost/filesystem/fstream.hpp>
#include <boost/program_options/detail/config_file.hpp>
#include <set>
std::string Cop... | {"hexsha": "3ae551c3865ffc4145c31dabf9cca8a19f03eaa1", "size": 5469, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Settings.cpp", "max_stars_repo_name": "izzy-developer/core", "max_stars_repo_head_hexsha": "32b83537a255aeef50a64252ea001c99c7e69a01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
'''
Written by Mengzhan Liufu at Yu Lab, the University of Chicago
'''
import numpy as np
# TESTED
def data_buffering(lfp_client, dio_client, Detector):
while True:
current_data = lfp_client.receive()['lfpData']
Detector.data_buffer.append(current_data[Detector.target_channel])
# current_... | {"hexsha": "cbd58caeea3267e9cc47547ef453e76bdbe1ce2c", "size": 439, "ext": "py", "lang": "Python", "max_stars_repo_path": "Closedloop_control/data_buffering.py", "max_stars_repo_name": "JohnLauFoo/PhaseControl2022_Yu", "max_stars_repo_head_hexsha": "83a1859864ecba1cda267d613b8eb1972766bddd", "max_stars_repo_licenses": ... |
import numpy as np
from ipso_phen.ipapi.base.ip_abstract import BaseImageProcessor
from ipso_phen.ipapi.tools.csv_writer import AbstractCsvWriter
from ipso_phen.ipapi.tools.common_functions import add_header_footer
from ipso_phen.ipapi.tools.common_functions import time_method
class ImageCsvWriter(AbstractCsvWriter)... | {"hexsha": "be7ad10a1bc933f738058b76aa4c0f6388f00410", "size": 4841, "ext": "py", "lang": "Python", "max_stars_repo_path": "class_pipelines/ip_019s1804_dib.py", "max_stars_repo_name": "tpmp-inra/ipapi", "max_stars_repo_head_hexsha": "b0f6be8960a20dbf95ef9df96efdd22bd6e031c5", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Kyle Lee
from pathlib import Path
import os
import pandas as pd
import time
import matplotlib.pyplot as plt
import numpy as np
def loadSlang(fName):
with open(fName) as file:
for line in file:
if(line!="\n"):
line = line.strip("\n") # strip trailing newline
... | {"hexsha": "0e19c11210d1f058bdf523342ef46c0dbbbf35cb", "size": 3187, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/corpus.py", "max_stars_repo_name": "kaiXlee/keyword-replacement-in-corpus", "max_stars_repo_head_hexsha": "66fa9f8b3461a92d7cf7d008593ff07a23a1e036", "max_stars_repo_licenses": ["MIT"], "max_s... |
/*
* Copyright (c) 2018-2021 Aleksas Mazeliauskas, Stefan Floerchinger,
* Eduardo Grossi, and Derek Teaney
* All rights reserved.
*
* FastReso is distributed under MIT license;
* see the LICENSE file that should be present in the root
* of the source distribution, or alternately available at:... | {"hexsha": "d0aa1033203d4d8e91de367ad908576d54dd3b79", "size": 3389, "ext": "h", "lang": "C", "max_stars_repo_path": "TFastReso_AZYHYDRO.h", "max_stars_repo_name": "amazeliauskas/FastReso", "max_stars_repo_head_hexsha": "a694de368e79e3d21afa5d57b47209d5c9c55ed9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
# -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license, see LICENSE and LICENSE_ASTROML
"""
Bayesian Block implementation
=============================
Dynamic programming algorithm for finding the optimal adaptive-width histogram. Modified from the
bayesian blocks python implementation found in astroM... | {"hexsha": "15040846c50bdb0e0dbff66e77fe4858c5bdc7a6", "size": 5891, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/hepstats/modeling/bayesian_blocks.py", "max_stars_repo_name": "scikit-hep/statutils", "max_stars_repo_head_hexsha": "636d305c1e9d76dea2613ac0ce085789821ee9ad", "max_stars_repo_licenses": ["BSD... |
# License: Apache-2.0
from ..util import util
from feature_gen_str import string_length
from typing import List, Union
import numpy as np
import pandas as pd
import databricks.koalas as ks
from._base_string_feature import _BaseStringFeature
pd.options.mode.chained_assignment = None
class StringLength(_BaseStringFeatu... | {"hexsha": "c700bcd851b749493cd3a00f8f7079e4b9ce7a36", "size": 4142, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib.macosx-10.9-x86_64-3.9/gators/feature_generation_str/string_length.py", "max_stars_repo_name": "Aditya-Kapadiya/gators", "max_stars_repo_head_hexsha": "d7c9967e3a8e304a601b6a92ad834d03d3... |
import numpy as np
import cv2
from evaluation_DHT.basic_ops import Line
def sa_metric(angle_p, angle_g):
d_angle = np.abs(angle_p - angle_g)
d_angle = min(d_angle, np.pi - d_angle)
d_angle = d_angle * 2 / np.pi
return max(0, (1 - d_angle)) ** 2
def se_metric(coord_p, coord_g, size=(400, 400)):
c_... | {"hexsha": "6a31d86457a79c7e05992f01eeaf929f8427b4c3", "size": 794, "ext": "py", "lang": "Python", "max_stars_repo_path": "Modeling/H_Net/code/evaluation_DHT/metric.py", "max_stars_repo_name": "dongkwonjin/Semantic-Line-MWCS", "max_stars_repo_head_hexsha": "2533bbaa62dde955b560fa2ab6a78a9b1a0038ac", "max_stars_repo_lic... |
module RunBeast #module for running BEAST
export run_beast,
check_beast,
find_beast
const BEAST_JAR = "beast.jar"
const BEAST_HOME = "BEAST_HOME"
function find_beast(;beast_home::String="")
if isempty(beast_home)
beast_home = haskey(ENV, BEAST_HOME) ? ENV[BEAST_HOME] : pwd()
end
if... | {"hexsha": "05bd593bf36024252292770439d0e459eaaf7787", "size": 2082, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RunBeast.jl", "max_stars_repo_name": "gabehassler/BeastUtils.jl", "max_stars_repo_head_hexsha": "5d708de8184a47f6db5da60e39a36d9dd279e457", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# --------------
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Load the dataframe
df = pd.read_csv(path)
#Code starts here
# probability p(A)for the event that fico credit score is greater than 700.
p_a = df[df['fico'].astype(float) > 700].shape[0]/df.shape[0]
print(p_a)
# ... | {"hexsha": "0502607df4513298052b56f8b7d998ea89b9fb82", "size": 2210, "ext": "py", "lang": "Python", "max_stars_repo_path": "loan-defaulters/code.py", "max_stars_repo_name": "imvishal23/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "bba87e3fa9cc2e71165eb7f05c9b280045b73c46", "max_stars_repo_licenses": ["MIT"], "m... |
import os
import os.path as path
import logging
from common.config import DATA_PATH, DEFAULT_TABLE
from common.const import UPLOAD_PATH
from common.const import input_shape
from common.const import default_cache_dir
from service.train import do_train
from service.search import do_search
from service.count import do_cou... | {"hexsha": "cbe7a09f14ac02bac48d1eaccd9a0b0fbd09e55b", "size": 4540, "ext": "py", "lang": "Python", "max_stars_repo_path": "solutions/pic_search/webserver/src/app.py", "max_stars_repo_name": "naetimus/bootcamp", "max_stars_repo_head_hexsha": "0182992df7c54012944b51fe9b70532ab6a0059b", "max_stars_repo_licenses": ["Apach... |
[STATEMENT]
lemma adds_antisym:
assumes "s adds t" "t adds s"
shows "s = t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. s = t
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. s = t
[PROOF STEP]
from \<open>s adds t\<close>
[PROOF STATE]
proof (chain)
picking this:
s adds t
[PROOF STEP]
o... | {"llama_tokens": 1388, "file": "Polynomials_Power_Products", "length": 23} |
from pandas import DataFrame, Series
from pandasUtils import isSeries, isDataFrame
from numpyUtils import isArray
from numpy import ndarray, asarray
##############################################################################################################################
# Geo Clusters Class
######################... | {"hexsha": "742b820f58f55182cfbf241fda2f82e3b8910826", "size": 5680, "ext": "py", "lang": "Python", "max_stars_repo_path": "convertData.py", "max_stars_repo_name": "tgadf/geocluster", "max_stars_repo_head_hexsha": "ee24038b81240ec47a202601e5c6b4dad8b63172", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import numpy as np
from scipy.linalg import lu_factor, lu_solve
class Truss:
"""
Simple static equilibrium solver for truss structures
Args
----
points : ndarray, shape (n_points, dim)
Point coordinates with spatial dimension ``dim``
lines : ndarray, shape (n_lines, 2)
Connect... | {"hexsha": "9621194e1500b84b02a01f5f5d7cc0aac0361717", "size": 7376, "ext": "py", "lang": "Python", "max_stars_repo_path": "ddtruss/truss.py", "max_stars_repo_name": "deeepeshthakur/ddtruss", "max_stars_repo_head_hexsha": "86aa945d577c6efe752099eee579386762942289", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
// Autogenerated from KST: please remove this line if doing any edits by hand!
#include <boost/test/unit_test.hpp>
#include "cast_to_top.h"
#include <iostream>
#include <fstream>
#include <vector>
BOOST_AUTO_TEST_CASE(test_cast_to_top) {
std::ifstream ifs("src/fixed_struct.bin", std::ifstream::binary);
kaitai... | {"hexsha": "b65bd7d78cbb75070fd23810b3338cdaa363c079", "size": 546, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "spec/cpp_stl_98/test_cast_to_top.cpp", "max_stars_repo_name": "DarkShadow44/kaitai_struct_tests", "max_stars_repo_head_hexsha": "4bb13cef82965cca66dda2eb2b77cd64e9f70a12", "max_stars_repo_licenses": ... |
from PIL import Image
import numpy
def readImage(path):
im = Image.open(path) # Can be many different formats.
pix = im.load()
print 'load image: {}'.format(path)
print 'image size: {}'.format(im.size) # Get the width and hight of the image for iterating over
# print pix[0, 0] # Get the ... | {"hexsha": "4839d3f8471000dcc0e62eb451b7e5ad34cbb3f8", "size": 1328, "ext": "py", "lang": "Python", "max_stars_repo_path": "loader.py", "max_stars_repo_name": "ifenglin/ERF_Python", "max_stars_repo_head_hexsha": "29fc87c52b104d3a2c191e68148e3e0b1ff76257", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This package contains utilities to run the test suite.
"""
import numpy as np
import astropy.units as u
from mskpy import photometry as P
from mskpy.photometry import hb
class TestHB():
"""Test photometry.hb.
From Dave Schleicher 2017 Mar 24... | {"hexsha": "6788b1abc622e23c9f0827ee8aa6898b49ff5e17", "size": 3090, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_photometry.py", "max_stars_repo_name": "mkelley/mskpy", "max_stars_repo_head_hexsha": "41f41fd69bae71853abdfd2afbd535cd0b79c530", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import re
import numba as nb
import numpy as np
import pandas as pd
# ===================================================================
# Useful functions
# ===================================================================
def isfloat(value):
"""
This function checks if a string can be converted to a flo... | {"hexsha": "71bfb974311f7d27503219ef91ed5dc2d73ce26a", "size": 2886, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_processing/data_funcs.py", "max_stars_repo_name": "yardasol/pride", "max_stars_repo_head_hexsha": "d63ee7711c7f5e4bd88b89dabd4140c562ac32e7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import importlib
import logging
import os
import time
from collections import defaultdict
from pathlib import Path
import joblib
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow.compat.v1.keras.backend as K
import toml
from keras_contrib.callbacks import CyclicLR
from logzero import setup_logger
... | {"hexsha": "9478bbafcd45b3051ef25f4b2690c5a665bde08f", "size": 27671, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyoaz/training/trainer.py", "max_stars_repo_name": "ameroueh/oaz", "max_stars_repo_head_hexsha": "7cf192b02adaa373b7b93bedae3ef67886ea53af", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
import tensorflow_datasets as tfds
units = 64
class att_block(tf.keras.layers.Layer):
'''
quoted from https://arxiv.org/pdf/1903.03878.pdf
'''
def __init__(self, x, y):
super(... | {"hexsha": "4c3f088b57c9eaf56e7fb1808196f95702fad0c0", "size": 1595, "ext": "py", "lang": "Python", "max_stars_repo_path": "backup/SMT_t.py", "max_stars_repo_name": "miffyrcee/tf", "max_stars_repo_head_hexsha": "1dd7bef0428c62704cb14d9e030731aa1badb037", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
abstract type AbstractAmplifier end
struct SimpleAmplifier <: AbstractAmplifier
name::String
in_min::typeof(0.0V)
in_max::typeof(0.0V)
gain::Float64
offset::typeof(0.0V)
current_max::typeof(0.0mA)
end
name(amp::SimpleAmplifier) = amp.name
in_min(amp::SimpleAmplifier) = amp.in_min
out_min(amp::... | {"hexsha": "1225cc846f2388cd356ab478e214641638e5e7f3", "size": 1500, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/amplifiers.jl", "max_stars_repo_name": "HolyLab/ImagineHardware.jl", "max_stars_repo_head_hexsha": "c22737a08f76fef1aeef42a957bbfc6d548c3025", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Flatten, Input, \
Concatenate, LeakyReLU
from tensorflow.keras import initializers, regularizers
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from rl.agents import DDPGAgent
from rl.memory import... | {"hexsha": "b4a9d579e3d8daa9eabca4b71dc7dff395cdef1e", "size": 16097, "ext": "py", "lang": "Python", "max_stars_repo_path": "Examples/PMSM_CurrentControl/ddpg_pmsm_dqCC_training.py", "max_stars_repo_name": "max-schenke/DESSCA", "max_stars_repo_head_hexsha": "1c9e22f5503e5fb32f307286baff228aee4a6e46", "max_stars_repo_li... |
[STATEMENT]
lemma lookup_fresh:
fixes z::"name"
assumes a: "z\<sharp>\<theta>" and b: "z\<sharp>x"
shows "z \<sharp>lookup \<theta> x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. z \<sharp> lookup \<theta> x
[PROOF STEP]
using a b
[PROOF STATE]
proof (prove)
using this:
z \<sharp> \<theta>
z \<sharp> x
goa... | {"llama_tokens": 183, "file": null, "length": 2} |
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