text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import petsc4py
import sys
petsc4py.init(sys.argv)
from petsc4py import PETSc
#import mshr
from dolfin import *
import sympy as sy
import numpy as np
import ExactSol
import MatrixOperations as MO
import CheckPetsc4py as CP
from dolfin import __version__
import MaxwellPrecond as MP
import StokesPrecond
import time
d... | {"hexsha": "7191a084f2395b027e0daee714615d9681b160bf", "size": 9266, "ext": "py", "lang": "Python", "max_stars_repo_path": "MHD/FEniCS/MHD/Stabilised/SaddlePointForm/Test/SplitMatrix/ScottTest/Hartman2D/HartmanChannel.py", "max_stars_repo_name": "wathen/PhD", "max_stars_repo_head_hexsha": "35524f40028541a4d611d8c78574e... |
function [Fao,Fso] = blockframepairaccel(Fa, Fs, Lb, varargin)
%BLOCKFRAMEPAIRACCEL Precompute structures for block processing
% Usage: F = blockframepairaccel(Fa,Fs,Lb);
%
% `[Fao,Fso]=blockframepairaccel(Fa,Fs,Lb)` works similar to
% |blockframeaccel| with a pair of frames. The only difference from
% calling... | {"author": "ltfat", "repo": "ltfat", "sha": "4496a06ad8dddb85cd2e007216b765dc996ef327", "save_path": "github-repos/MATLAB/ltfat-ltfat", "path": "github-repos/MATLAB/ltfat-ltfat/ltfat-4496a06ad8dddb85cd2e007216b765dc996ef327/blockproc/blockframepairaccel.m"} |
[STATEMENT]
lemma replicate_eq_append_conv:
"(replicate n x = xs @ ys) = (\<exists>m\<le>n. xs = replicate m x \<and> ys = replicate (n-m) x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (replicate n x = xs @ ys) = (\<exists>m\<le>n. xs = replicate m x \<and> ys = replicate (n - m) x)
[PROOF STEP]
proof(induct... | {"llama_tokens": 4713, "file": "JinjaThreads_Basic_Auxiliary", "length": 40} |
[STATEMENT]
lemma (in category) cat_iso_functor_if_cf_lcomp_Hom_iso_functor:
assumes "\<FF> : \<BB> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>"
and "\<GG> : \<BB> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>"
and "Hom\<^sub>O\<^sub>.\<^sub>C\<^bsub>\<alpha>\<^esub>\<CC>(\<FF>-,-) \<... | {"llama_tokens": 5140, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_Yoneda", "length": 31} |
"""tools related to optimization
such as more objective functions
"""
import torch
import warnings
import numpy as np
from skimage import color
import os.path as op
import imageio
from glob import glob
def mse(synth_rep, ref_rep, **kwargs):
r"""return the MSE between synth_rep and ref_rep
For two tensors, :... | {"hexsha": "50220137c2bb794f392dbf398f2e1e91e5c3cc72", "size": 15038, "ext": "py", "lang": "Python", "max_stars_repo_path": "extra_packages/plenoptic_part/tools/optim.py", "max_stars_repo_name": "billbrod/foveated-metamers", "max_stars_repo_head_hexsha": "1b4e1c5423ba7915311cdcfe977b1c6a242bff52", "max_stars_repo_licen... |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
@doc raw"""SecretReference represents a Secret Reference. It has enough information to retrieve secret in any namespace
IoK8sApiCoreV1SecretReference(;
name=nothing,... | {"hexsha": "50a7cbef5c6361a90c0681b10926270f796a0984", "size": 2052, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ApiImpl/api/model_IoK8sApiCoreV1SecretReference.jl", "max_stars_repo_name": "memetics19/Kuber.jl", "max_stars_repo_head_hexsha": "0834cab05d2b5733cb365594000be16f54345ddb", "max_stars_repo_lice... |
// Copyright (c) 2014, Sailing Lab
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// 1. Redistributions of source code must retain the above copyright notice,
// this list of conditions an... | {"hexsha": "bffbc3715909338928ff82d60879da2cdc4de366", "size": 20993, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/petuum_ps_common/util/stats.hpp", "max_stars_repo_name": "ForrestGan/public", "max_stars_repo_head_hexsha": "2cada36c4b523cf80f16a4f0d0fdc01166a69df1", "max_stars_repo_licenses": ["BSD-3-Clause... |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import numpy as np
import tvm
import logging
import sys, time, subprocess
from tvm import autotvm
import json
import os
def schedule(attrs):
cfg, s, output = attrs.auto_config, attrs.scheduler, attrs.outputs[0]
th_vals, rd_vals = [attrs.get... | {"hexsha": "4a5a7ca12c0f92dee9f6670d595266a6c2af7f47", "size": 2306, "ext": "py", "lang": "Python", "max_stars_repo_path": "platforms/c-rocm/schedule/standard/default_small.py", "max_stars_repo_name": "ghostplant/antares", "max_stars_repo_head_hexsha": "3e4d2b364f01cf326e1a427aaee866dbd31dd9ea", "max_stars_repo_license... |
import numpy as np
from os import path
import matplotlib.pyplot as plt
import sklearn
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
import torch
import torch.nn as nn
import torch.optim as optim
from captum.attr import Laye... | {"hexsha": "863aa7a92842cc734b76b4031dfee42409f5956e", "size": 7105, "ext": "py", "lang": "Python", "max_stars_repo_path": "std/captum/16.py", "max_stars_repo_name": "quantapix/qnarre.com", "max_stars_repo_head_hexsha": "f51d5945c20ef8182c4aa11f1b407d064c190c70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import pandas as pd
import numpy as np
from src.config import config
if __name__ == '__main__':
PATH = config.get_dir()
country = config.get_country()
data = pd.DataFrame(pd.read_csv(PATH+'/final/%s/master.csv' % country))
if country == 'civ':
dhs = data[['Adm_1', 'Adm_2', 'Adm_3', 'Adm_4',
... | {"hexsha": "30f15d564138bcb6848dce2aea3d8b2bd003fb0a", "size": 2906, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/metrics/spatial_lag.py", "max_stars_repo_name": "ShipJ/GCRF", "max_stars_repo_head_hexsha": "3f0d9714ec6fba587dd680b4ebc2defff423bcaf", "max_stars_repo_licenses": ["FTL"], "max_stars_count": 1... |
[STATEMENT]
lemma child_of_parentD:
"has_parent l i \<Longrightarrow> left (parent i) = i \<or> right (parent i) = i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. has_parent l i \<Longrightarrow> left (parent i) = i \<or> right (parent i) = i
[PROOF STEP]
unfolding parent_def left_def right_def valid_def
[PR... | {"llama_tokens": 200, "file": "Refine_Imperative_HOL_IICF_Impl_Heaps_IICF_Abs_Heap", "length": 2} |
'''
Noise estimation.
'''
import numpy as np
def sigma_noise_spd_welch(y, fs, noise_range, method='expmeanlog'):
'''
Estimating the noise level by a spectral power density (welch algorithm)
approach.
ARGUMENTS
`````````
y : signal, [y] = N x T
fs : sampling rate
noise_range : noise ra... | {"hexsha": "15c53c2be4a5e275b85b31dfd1de8400dea40df3", "size": 1838, "ext": "py", "lang": "Python", "max_stars_repo_path": "neuralyzer/cia/smff/noise.py", "max_stars_repo_name": "michigraber/neuralyzer", "max_stars_repo_head_hexsha": "6c9dfd2f1f918afc6904809c443bfe8e8865b2b7", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import pandas as pd
from fit_super_simple import Fit
import rational_model
import sys
par_1 = float(sys.argv[1])
par_2 = float(sys.argv[2])
in_dir = '../../modeling/'
game = '0-1en01_simulation.csv'
player = 1
bg_dir = '/home/pkrafft/couzin_copy/light-fields/' + game.split('_')[-2] + '/'
d... | {"hexsha": "8ebe2c4a6e3cdc591d861c6c68271e09a570af22", "size": 552, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulations/old/player_model/single-super-simple-fit.py", "max_stars_repo_name": "hawkrobe/fish", "max_stars_repo_head_hexsha": "2000e46c397f7c95bba8ecb0c6afd26013929ff8", "max_stars_repo_licenses"... |
'''
Summary
=======
Defines a penalized ML estimator for Gaussian Mixture Models, using Expectation-Maximization.
Supports these API functions common to any sklearn-like GMM unsupervised learning model:
* fit
Resources
=========
See COMP 136 CP3 assignment on course website for the complete problem description and a... | {"hexsha": "02886b6385b0d52ab1bdabf29114959ea0c42779", "size": 13194, "ext": "py", "lang": "Python", "max_stars_repo_path": "cp3/src/GMM_PenalizedMLEstimator_EM.py", "max_stars_repo_name": "tufts-ml-courses/comp136-spr-20s-assignments-", "max_stars_repo_head_hexsha": "c53cce8e376862eeef395aa0b55eca8b284a0115", "max_sta... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
#libraries
import numpy
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
numpy.random.seed(7)
# In[2]... | {"hexsha": "d5ee39f2f5b9cd80ebe34d0986a889a3509a52d1", "size": 1868, "ext": "py", "lang": "Python", "max_stars_repo_path": "imdb sentiment classification/IMDB Sentiment Classification.py", "max_stars_repo_name": "divyanshgarg97/IMDB-Sentiment-Classification", "max_stars_repo_head_hexsha": "3a6614cd52ff1b401aa99916d5db6... |
import pytorch_lightning as pl
import torch
from torchmetrics import Accuracy
from scheduler import WarmupCosineLR
from torch.optim.lr_scheduler import StepLR
import numpy as np
import models
def rand_bbox(size, lam):
W = size[2]
H = size[3]
cut_rat = np.sqrt(1. - lam)
cut_w = int(W * cut_rat)
cut_... | {"hexsha": "d3c3483f2911453b522293c5a9a4bbd55c46b935", "size": 5739, "ext": "py", "lang": "Python", "max_stars_repo_path": "module.py", "max_stars_repo_name": "paulgavrikov/pytorch-pretrained-cnns", "max_stars_repo_head_hexsha": "f13bd151ad48bbe81d66cd3ad04465cc347c5db4", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -*- coding: utf-8 -*-
"""17-35499-3 [Assignment-1].ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1gBZslsgZgEtGNCmcdUbZO8fNKFH393KN
# **Unzip CIFAR-10-images-master.zip**
"""
import zipfile
from google.colab import drive
zip_ref = zipfile.Zi... | {"hexsha": "041971211d0baf7feed52dae44fef2f7db110699", "size": 7898, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignment.py", "max_stars_repo_name": "AhsanulIslam/Manhattan_Distance-Euclidean_Distance_CIFAR-10", "max_stars_repo_head_hexsha": "d6806cfd6325ab0993e24e2489f2ee2688a288cd", "max_stars_repo_lice... |
[STATEMENT]
lemma fls_integral_of_nat:
"fls_integral (of_nat n :: 'a::division_ring fls) = of_nat n * fls_X"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fls_integral (of_nat n) = of_nat n * fls_X
[PROOF STEP]
by (rule fls_integral_of_nat'[OF inverse_1]) | {"llama_tokens": 124, "file": null, "length": 1} |
[STATEMENT]
lemma bij_fst_inv_inv_eq: "bij f \<Longrightarrow> fst (inv (%(x, u). inv f x) z) = f z"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bij f \<Longrightarrow> fst (inv (\<lambda>(x, u). inv f x) z) = f z
[PROOF STEP]
apply (rule fst_inv_equalityI)
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. bij f... | {"llama_tokens": 480, "file": null, "length": 5} |
""" Module for core algorithms related to tracing slits/orders
These should primarily be called by the TraceSlits class
"""
import inspect
import copy
from collections import Counter
import numpy as np
from scipy import ndimage
from scipy.special import erf
from scipy import signal
from scipy import interpolate
impo... | {"hexsha": "ed56f9031df8b029486c5d89c6da4fb70f3384b3", "size": 128919, "ext": "py", "lang": "Python", "max_stars_repo_path": "pypeit/deprecated/trace_slits.py", "max_stars_repo_name": "ykwang1/PypeIt", "max_stars_repo_head_hexsha": "a96cff699f1284905ce7ef19d06a9027cd333c63", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import SimpleITK as sitk
import numpy as np
class LungSplitter:
def __init__(self, split_thirds=False):
self.split_thirds = split_thirds
self.size_th = 0.05
self.coordinate_system = 'lps'
# TODO: Chest conventions not available in Slicer. We hard coded the values for the l... | {"hexsha": "40ea8a7a2761e23b9e67243c54a577ba264f73f1", "size": 7792, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripted/CIP_/CIP/logic/lung_splitter.py", "max_stars_repo_name": "liu3xing3long/SlicerCIP", "max_stars_repo_head_hexsha": "d946c429cf0ec3fb05db77b10658393619df9b15", "max_stars_repo_licenses": ["... |
import datetime
import logging
from pathlib import Path
import cartopy.crs as ccrs
import fiona
import geopandas as gpd
import matplotlib.image as mplimg
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import rasterio
import rasterio as rio
import shapely.geometry as sgeom
from... | {"hexsha": "be0d9b4af32160245c770c0915e0e58bc5e9928c", "size": 17801, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plotters_lib/grafico_lst.py", "max_stars_repo_name": "OHMC/productos-satelitales", "max_stars_repo_head_hexsha": "635549bfb10e679587c4cf4831a493f232d75f5c", "max_stars_repo_licenses": ["Apach... |
using GraphPlot
using MetaGraphs
using Plots
using Compose
using Colors
using LightGraphs
using Cairo
using Fontconfig
"""
Splits the graph given symbol.
Return a list with the group each vertex is in. Example: you split
the graph by the :type property, so if it has 3 types ["a", "b", "c"]
it will return a list of b... | {"hexsha": "2b801442a3ac5f614faabdacbd73b6d549ad672b", "size": 4197, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/plotting/plot.jl", "max_stars_repo_name": "valcarce01/Neo4j2Julia.jl", "max_stars_repo_head_hexsha": "15397b8dd9c08369abd5fbdbdb044ea7316dfd40", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import os
import numpy as np
from bert.tokenization.bert_tokenization import FullTokenizer
'''
Prediction
For prediction, we need to prepare the input text the same way as we did for training -
tokenize, adding the special [CLS] and [SEP] token at begin and end of the token sequence,
and pad to match the model input ... | {"hexsha": "48448aea757b2953f578f8d1070c3e2b35210756", "size": 1612, "ext": "py", "lang": "Python", "max_stars_repo_path": "egrader/predict.py", "max_stars_repo_name": "keemsunguk/EGrader", "max_stars_repo_head_hexsha": "8fe02099a28c5dae3e48b6c7df55eb703811f051", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
Riki Hayashi had lived in Davis from 1988 until 2010 when he moved to Virginia working in the field of his dreams Judging and writing for the DCI and Star City Games. While serving as the roadie for legendary Sacramento rock band Magnolia Thunderfinger, lead singer Skid Jones decided that Riki needed a cool nickname. R... | {"hexsha": "e20cb4c00be606b3e96517ce6fc5c5f1f9d6a4aa", "size": 6671, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Risky.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
# Estimating text loss in The Old Norse fornaldarsögur
This Python notebook is a derivative of the one which accompanies the following publication:
> Mike Kestemont and Folgert Karsdorp, "Het Atlantis van de Middelnederlandse ridderepiek. Een schatting van het tekstverlies met methodes uit de ecodiversiteit". *Spiege... | {"hexsha": "2d90b5da312f3dcc0d75c5493993b5c345469e5e", "size": 189428, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "analysis.ipynb", "max_stars_repo_name": "ThorkellMoon/FASNL_loss", "max_stars_repo_head_hexsha": "7c31eda543f8bfe443480a3739128a9adc251d2c", "max_stars_repo_licenses": ["Apache-2.0"... |
[STATEMENT]
lemma msetext_dersh_irrefl_from_trans:
assumes
trans: "\<forall>z \<in> set xs. \<forall>y \<in> set xs. \<forall>x \<in> set xs. gt z y \<longrightarrow> gt y x \<longrightarrow> gt z x" and
irrefl: "\<forall>x \<in> set xs. \<not> gt x x"
shows "\<not> msetext_dersh gt xs xs"
[PROOF STATE]
pro... | {"llama_tokens": 4784, "file": "Lambda_Free_RPOs_Extension_Orders", "length": 38} |
[STATEMENT]
lemma path_verts: "path_entry (g_E G) p n \<Longrightarrow> n \<in> set (g_V G)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. path_entry (g_E G) p n \<Longrightarrow> n \<in> set (g_V G)
[PROOF STEP]
proof (cases "p = []")
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. \<lbrakk>path_entry (g_E G) p... | {"llama_tokens": 1548, "file": "Dominance_CHK_Cfg", "length": 17} |
import datetime
import os
import uuid
from pathlib import Path
from addict import Dict as aDict
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from openghg.store.base import Datasource
from openghg.standardise.surface import parse_crds
from openghg.objectstore import get_local_bucket, get_ob... | {"hexsha": "7890ad746a1fe7349681ea539f5c946da8201888", "size": 16300, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/store/test_datasource.py", "max_stars_repo_name": "openghg/openghg", "max_stars_repo_head_hexsha": "9a05dd6fe3cee6123898b8f390cfaded08dbb408", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
# !/usr/bin/python3
# -*- coding: utf-8 -*-
import logging
import time
import numpy as np
from pybpodapi.com.arcom import ArduinoTypes
from pybpodapi.bpod_modules.bpod_modules import BpodModules
from pybpodapi.bpod.bpod_com_protocol import BpodCOMProtocol
from pybpodapi.com.protocol.send_msg_headers import SendMessag... | {"hexsha": "49e72c94efb907594c128c8fb1f2be03b39c07e0", "size": 6923, "ext": "py", "lang": "Python", "max_stars_repo_path": "pybpodapi/bpod/bpod_com_protocol_modules.py", "max_stars_repo_name": "ckarageorgkaneen/pybpod-api", "max_stars_repo_head_hexsha": "ebccef800ae1abf3b6a643ff33166fab2096c780", "max_stars_repo_licens... |
######## Stan diagnose example ###########
using StanDiagnose
bernoulli_model = "
data {
int<lower=0> N;
int<lower=0,upper=1> y[N];
}
parameters {
real<lower=0,upper=1> theta;
}
model {
theta ~ beta(1,1);
y ~ bernoulli(theta);
}
"
bernoulli_data = Dict("N" => 10, "y" => [0, 1, 0, 1, 0, 0, 0, 0, 0, 1]... | {"hexsha": "9a332746a36854cca23500acfccacc02e7900bf2", "size": 586, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Examples_Stan_Methods/Diagnose/bernoulli_diagnose.jl", "max_stars_repo_name": "Mechachleopteryx/Stan.jl", "max_stars_repo_head_hexsha": "a98b50009fac79d608c94fd2c91bdef9070acefc", "max_stars_repo_li... |
%% Copyright (C) 2016-2022 Colin B. Macdonald
%%
%% This file is part of OctSymPy.
%%
%% OctSymPy 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 l... | {"author": "cbm755", "repo": "octsympy", "sha": "c1ecd1e08f027d5101d0f4250dfc496aa98c8bcd", "save_path": "github-repos/MATLAB/cbm755-octsympy", "path": "github-repos/MATLAB/cbm755-octsympy/octsympy-c1ecd1e08f027d5101d0f4250dfc496aa98c8bcd/inst/@double/fresnels.m"} |
import numpy as np
from jbdl.rbdl.contact import calc_contact_jacobian_core
from jbdl.rbdl.contact.calc_contact_jacobian import calc_contact_jacobian_extend_core
import jax.numpy as jnp
from jbdl.rbdl.utils import xyz2int
# @partial(jit, static_argnums=(5, 6, 7, 8, 9, 10, 11, 12, 13))
def impulsive_dynamics_core(
... | {"hexsha": "4a7633b1dd021776381ff565319ddc1a45fc4bb5", "size": 2526, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/jbdl/experimental/contact/impulsive_dynamics.py", "max_stars_repo_name": "Wangxinhui-bot/jbdl", "max_stars_repo_head_hexsha": "4541fcbec9156c8a3dc496058230fdf2a3fa1bdf", "max_stars_repo_licens... |
import pytest
import numpy as np
import os
from pathlib import Path
from scipy import signal
import xarray as xr
import filtering
def velocity_series(nt, U0, f):
"""Construct a 1D velocity timeseries."""
t = np.arange(nt) + 1
t0 = nt // 2 + 1 # middle time index
u = U0 + (U0 / 2) * np.sin(2 * np.p... | {"hexsha": "ea0199600072ac4edc2a8b2e6aafdb1acc71b213", "size": 11449, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_filtering.py", "max_stars_repo_name": "angus-g/lagrangian-filtering", "max_stars_repo_head_hexsha": "1b077af83b8c25db35e876ec3cba930833212e86", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma split_strip_while_append:
fixes xs :: "'a list"
obtains ys zs :: "'a list"
where "strip_while P xs = ys" and "\<forall>x\<in>set zs. P x" and "xs = ys @ zs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>ys zs. \<lbrakk>strip_while P xs = ys; \<forall>x\<in>set zs. P x; xs = ys @ zs\<r... | {"llama_tokens": 1327, "file": null, "length": 11} |
#ifndef STAN_MATH_PRIM_SCAL_FUN_ASINH_HPP
#define STAN_MATH_PRIM_SCAL_FUN_ASINH_HPP
#include <stan/math/prim/scal/fun/constants.hpp>
#include <stan/math/prim/scal/fun/is_nan.hpp>
#include <stan/math/prim/scal/meta/likely.hpp>
#include <stan/math/prim/scal/fun/boost_policy.hpp>
#include <boost/math/special_functions/as... | {"hexsha": "985bc1e28eb11dd6fe308523fa27f311c2398b73", "size": 1068, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cmdstan/stan/lib/stan_math/stan/math/prim/scal/fun/asinh.hpp", "max_stars_repo_name": "yizhang-cae/torsten", "max_stars_repo_head_hexsha": "dc82080ca032325040844cbabe81c9a2b5e046f9", "max_stars_repo... |
subroutine qqb_dm_gg_Samps(p,i1,i2,i3,i4,i5,i6,msq1,msq2,msqsl)
implicit none
include 'dm_params.f'
include 'constants.f'
include 'zprods_decl.f'
!----- vector amplitude for
!----- q(i1)+g(i2)+g(i3)+qb(i4)+x(i5)+x(i6)
double precision p(mxpart,4)
!-----fills amplitude for q g... | {"hexsha": "349f701ca2e21cd90c173bac0f4c621b36ecc537", "size": 7296, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/DM/Scal_DM/qqb_dm_gg_Samps.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apac... |
#Licensed under Apache 2.0 License.
#© 2020 Battelle Energy Alliance, LLC
#ALL RIGHTS RESERVED
#.
#Prepared by Battelle Energy Alliance, LLC
#Under Contract No. DE-AC07-05ID14517
#With the U. S. Department of Energy
#.
#NOTICE: This computer software was prepared by Battelle Energy
#Alliance, LLC, hereinafter the Cont... | {"hexsha": "e0a53ff6ea530318b030afacbc839f13d4619125", "size": 13080, "ext": "py", "lang": "Python", "max_stars_repo_path": "developer_tools/dymola_python_testing/ModelicaPy/buildingspy/tests/test_simulate_Simulator.py", "max_stars_repo_name": "wanghy-anl/HYBRID", "max_stars_repo_head_hexsha": "a6942b322dfe0ba98b19e687... |
from app import Modeldb
from app import Metricdb
import pandas as pd
import numpy as np
import pickle
import requests
from sklearn import metrics
from random import randint
import os
def output(modeltype,model1,dftrainpath,ytrainpath,dftestpath,ytestpath,db,num,alpha1=1,n_neighbors1=5,leaf_size1=30,max_depth1=50,... | {"hexsha": "661e95dc5040b9fe2263a62e772488641b90cec3", "size": 6067, "ext": "py", "lang": "Python", "max_stars_repo_path": "makemodel.py", "max_stars_repo_name": "Shubham2443/Hands_on_ML", "max_stars_repo_head_hexsha": "5b4bd6d4f4804673af8b6ea71955679685d04449", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
! <get_user_input.for - A component of the City-scale
! Chemistry Transport Model EPISODE-CityChem>
!*****************************************************************************!
!*
!* EPISODE - An urban-scale air quality model
!* ==========================================
!* Copyright (C) 2018 NILU... | {"hexsha": "ca697518b664fe8d18d8518ee40e796bb2bb6321", "size": 58733, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "JPS_CITY_CHEM/citychem-1.3/preproc/mcwind/src/get_user_input.for", "max_stars_repo_name": "mdhillmancmcl/TheWorldAvatar-CMCL-Fork", "max_stars_repo_head_hexsha": "011aee78c016b76762eaf511c78fab... |
/*
* @copyright Copyright (c) 2017 CERN and the Allpix Squared authors.
* This software is distributed under the terms of the MIT License, copied verbatim in the file "LICENSE.md".
* In applying this license, CERN does not waive the privileges and immunities granted to it by virtue of its status as an
* Intergovern... | {"hexsha": "737704fccaae3101b96f1cc501a6f78d934c7916", "size": 17691, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/modules/CapacitiveTransfer/CapacitiveTransferModule.cpp", "max_stars_repo_name": "schmidtseb/allpix-squared", "max_stars_repo_head_hexsha": "f0e47c35a4db405d4d49887e4e8eaffe69176bfa", "max_star... |
Take a 2-node neural network...
$$N = \begin{bmatrix} d_{11} & d_{12} \\ d_{21} & d_{22} \end{bmatrix} \quad \quad \vec{n}_1 = \begin{bmatrix} d_{11} \\ d_{21} \end{bmatrix} \quad \vec{n}_2 = \begin{bmatrix} d_{21} \\ d_{22} \end{bmatrix}$$
To find the properties of the delays and activation times for a 2-node networ... | {"hexsha": "13f9e143bf263b405a2425984ac9f97514167c2d", "size": 6594, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "neural_algebra/Neural Algebra.ipynb", "max_stars_repo_name": "mathnathan/notebooks", "max_stars_repo_head_hexsha": "63ae2f17fd8e1cd8d80fef8ee3b0d3d11d45cd28", "max_stars_repo_licenses... |
/**
* @file iqn_test.cpp
* @author Marcus Edel
*
* Test file for IQN (incremental Quasi-Newton).
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If not, see
* ... | {"hexsha": "b8758af114c8f890eac524d828e812ba4d4fe8ed", "size": 2635, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mlpack/tests/iqn_test.cpp", "max_stars_repo_name": "chigur/mlpack", "max_stars_repo_head_hexsha": "aff1eda03b7c279acb6d3e660d5c5a6f697d3735", "max_stars_repo_licenses": ["BSD-3-Clause-No-Nuclear... |
# coding=utf-8
# National Oceanic and Atmospheric Administration
# Alaskan Fisheries Science Center
# Resource Assessment and Conservation Engineering
# Midwater Assessment and Conservation Engineering
# THIS SOFTWARE AND ITS DOCUMENTATION ARE CONSIDERED TO BE IN THE PUBLIC DOMAIN
# AND THUS ARE AVAILABLE... | {"hexsha": "074fbac6033711d5944b8df6b86fb754c8b414e6", "size": 105487, "ext": "py", "lang": "Python", "max_stars_repo_path": "ref_code/pyEcholab/EK60.py", "max_stars_repo_name": "cyrf0006/echopype", "max_stars_repo_head_hexsha": "54d385b896ef9d54f7daa1d107c6719ab1fa14c5", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
# This file was generated, do not modify it. # hide
controls = [
Step(1), # to increment iteration parameter (`pipe.nrounds`)
NumberSinceBest(4), # main stopping criterion
TimeLimit(2/3600), # never train more than 2 sec
InvalidValue() # stop if NaN or ±Inf encountered
] | {"hexsha": "b53ba837500a0095d5da5da0b5c9bcee9d2d630c", "size": 312, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/end-to-end/telco/code/ex48.jl", "max_stars_repo_name": "alan-turing-institute/MLJTutorials", "max_stars_repo_head_hexsha": "f1613ac8968fceee0e4fe43d1fdb5b58c8a84bad", "max_stars_repo_l... |
import torch
import numpy as np
import pickle
from transformers import BertTokenizer, BertModel, BertForMaskedLM
BOS_TOKEN = '[CLS]'
EOS_TOKEN = '[SEP]'
MASK_TOKEN = '[MASK]'
class BertTok:
def __init__(self, pretrained_model='bert-large-uncased'):
self.tokenizer = BertTokenizer.from_pretrained(pretraine... | {"hexsha": "8a6cd772cee01981e747f19bbfa8f8972239bdfe", "size": 15029, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/BertModel.py", "max_stars_repo_name": "glicerico/wordcat_transformer", "max_stars_repo_head_hexsha": "f1b2f105c0878aeac5755003eafebd23edf49cad", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "62fe3f5b02df94b30db174cdbed67f56d74deca1", "size": 1872, "ext": "py", "lang": "Python", "max_stars_repo_path": "saccader/visual_attention/dram_test.py", "max_stars_repo_name": "deepneuralmachine/google-research", "max_stars_repo_head_hexsha": "d2ce2cf0f5c004f8d78bfeddf6e88e88f4840231", "max_stars_repo_licen... |
from datetime import datetime
import matplotlib.pyplot as plt
import scipy.stats as st
import numpy
import statistics
times = []
with open("csv/Sep", "r") as f:
for i,line in enumerate(f):
if i != 0:
parts = line.split(",")
if "bigmem" in parts[-2]:
elapsed_txt = ... | {"hexsha": "322d30392e1d4868f606935638ae867a5685150f", "size": 1920, "ext": "py", "lang": "Python", "max_stars_repo_path": "elapsed.py", "max_stars_repo_name": "mkualquiera/accsim-public", "max_stars_repo_head_hexsha": "0064433cdf8d4d6a67b8dde781756674ab0a42c2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# -*- coding: utf-8 -*-
"""
Windows と Mac の両方で日本語フォントを指定して日本語メッセージを表示させる．
@author: Hitoshi HABE (habe@kindai.ac.jp)
"""
import numpy as np
import matplotlib.pyplot as pl
import platform
from matplotlib import font_manager
# 日本語フォントの設定（はじまり）
# Windows と Mac のどちらで動かしているのかを判断して日本語フォントに指定を切り替える
systemname=platform.syst... | {"hexsha": "c765c5f2b58b36b680b54a3b1dc19491f9db8b9c", "size": 1168, "ext": "py", "lang": "Python", "max_stars_repo_path": "JPmessage.py", "max_stars_repo_name": "KindaiCVLAB/PythonSample", "max_stars_repo_head_hexsha": "e3b76259df8a1babd5ecb06deb815b177fc39ba0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#!/usr/bin/env python3
import cv2
import depthai as dai
import numpy as np
import argparse
import time
import shlex
import subprocess as sp
from time import monotonic
from datetime import datetime, timedelta
'''
Blob taken from the great PINTO zoo
git clone git@github.com:PINTO0309/PINTO_model_zoo.git
cd PINTO_mod... | {"hexsha": "727d25fedf717cb93e60df443c91e604082c202e", "size": 10244, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/custom-scripts/road-cracks-semantic-segmentation.py", "max_stars_repo_name": "aviogit/depthai-python", "max_stars_repo_head_hexsha": "ffeb646dff0819177b09f0dd8eb9720b154e7845", "max_star... |
from __future__ import absolute_import, print_function
import logging
import os
import numpy as np
from stop_words import get_stop_words
from topik.fileio import read_input
from topik import tokenizers, vectorizers, models, visualizers
from topik.visualizers.termite_plot import termite_html
BASEDIR = os.path.abspath... | {"hexsha": "00b15e1815dff5338bdc3ae87c417212193b4dd6", "size": 3392, "ext": "py", "lang": "Python", "max_stars_repo_path": "topik/simple_run/run.py", "max_stars_repo_name": "ContinuumIO/topik", "max_stars_repo_head_hexsha": "3f943dce48fe8ca805868151c33f155a2d200a7c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
import math
import random
import time
from typing import Tuple
import numpy as np
from sklearn.preprocessing import KBinsDiscretizer
# USES Q LEARNING
# import gym
import gym
env = gym.make('CartPole-v1')
def policy(state: tuple):
"""Choosing action based on epsilon-greedy policy"""
return np.argmax(Q_tabl... | {"hexsha": "e0c56486920725f7ab1c76b65d4fa1ef8a411f7d", "size": 2466, "ext": "py", "lang": "Python", "max_stars_repo_path": "Deep Learning/FirstCartPole.py", "max_stars_repo_name": "GarrettMaury7921/OpenAI_Tutorials", "max_stars_repo_head_hexsha": "6db988c249565264c94efda65739b2e87007b87e", "max_stars_repo_licenses": ["... |
# Copyright (c) 2012-2015. The Regents of the University of California (Regents)
# and Richard Plevin. See the file COPYRIGHT.txt for details.
import os
import numpy as np
from pygcam.matplotlibFix import plt
import pandas as pd
import seaborn as sns
from six import iteritems
from six.moves import xrange
from pygcam.... | {"hexsha": "816f3da1016afa19fb6d57b397b92edac4b96f70", "size": 45027, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygcam/mcs/analysis.py", "max_stars_repo_name": "JGCRI/pygcam", "max_stars_repo_head_hexsha": "e33042e7c9f33dfe471dc48e02965ecfb523bd83", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18... |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
usage_doc = "Usage of script: script_nama <size_of_canvas:int>"
choice = [0] * 100 + [1] * 10
random.shuffle(choice)
def create_canvas(size):
canvas = [[False for i in range(size)] for ... | {"hexsha": "ac153600763a3fcf9c79d7a96d39defdb303cc4f", "size": 2114, "ext": "py", "lang": "Python", "max_stars_repo_path": "cellular_automata/game_of_life.py", "max_stars_repo_name": "slowy07/pythonApps", "max_stars_repo_head_hexsha": "22f9766291dbccd8185035745950c5ee4ebd6a3e", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import matplotlib.pyplot as plt
import numba
import time as tm
import platform
import os
import sys
cythonc = True
try:
import psearch_pyc
except ImportError:
cythonc = False
# version information:
from collections import namedtuple
version_info = namedtuple('version_info','major minor mic... | {"hexsha": "020f3b30745c14095a71dbe2545c6932caebdc26", "size": 56730, "ext": "py", "lang": "Python", "max_stars_repo_path": "leavitt/psearch_py3.py", "max_stars_repo_name": "KyleLMatt/leavitt", "max_stars_repo_head_hexsha": "98b035b8e3b37623593e38cb82db6e945c272617", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from Utils import get_batch
import numpy as np
class CobamasVisualizer:
@classmethod
def plot_multi_plant_sample(cls, dataset, model, idx, v_scale=2, h_scale=3, save_path=None):
converters = dataset.converter_names
sensors = ... | {"hexsha": "87fcae391a9b7696de45f477be8289eef59cdba4", "size": 4021, "ext": "py", "lang": "Python", "max_stars_repo_path": "CobamasVisualizer.py", "max_stars_repo_name": "n-1-l-s/cobamas-sensor-od", "max_stars_repo_head_hexsha": "3781fdd24255b3e28ddb04d50de8395e3deaadd8", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
import csv
import glob
import torch
import numpy as np
import pandas as pd
from plantcelltype.graphnn.trainer import get_model
from plantcelltype.utils import create_h5
from ctg_benchmark.utils.io import load_yaml
from plantcelltype.utils.utils import load_paths
from plantcelltype.graphnn.trainer import datasets
from... | {"hexsha": "dc3f81538221358696d6bdbc4cb710cbd94d71fb", "size": 6671, "ext": "py", "lang": "Python", "max_stars_repo_path": "plantcelltype/graphnn/predict.py", "max_stars_repo_name": "hci-unihd/plant-celltype", "max_stars_repo_head_hexsha": "64e8fe2404bf224a94fbab99573365c1d1d5b3d9", "max_stars_repo_licenses": ["MIT"], ... |
# Copyright 2018 D-Wave Systems 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | {"hexsha": "a70e1f2e58dded5800f34eaf5a8cdf82a1aa4a5c", "size": 4116, "ext": "py", "lang": "Python", "max_stars_repo_path": "dimod/reference/samplers/exact_solver.py", "max_stars_repo_name": "joseppinilla/dimod", "max_stars_repo_head_hexsha": "e33ca5045e31ee2d9d58515f017fb6be5276cd8e", "max_stars_repo_licenses": ["Apach... |
/*
* SensorProcessorBase.cpp
*
* Created on: Jun 6, 2014
* Author: Péter Fankhauser, Hannes Keller
* Institute: ETH Zurich, ANYbotics
*/
#include <elevation_mapping/sensor_processors/SensorProcessorBase.hpp>
//PCL
#include <pcl/io/pcd_io.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#inclu... | {"hexsha": "ca4b595f909f5694370a17f33163909c9fddd4d3", "size": 11960, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "elevation_mapping/elevation_mapping/src/sensor_processors/SensorProcessorBase.cpp", "max_stars_repo_name": "mcx/GEM", "max_stars_repo_head_hexsha": "e1245a59f44213edcc8c105f1e9aaa092ea97169", "max_... |
import keras.engine.training
import keras.callbacks
import numpy as np
from typing import List
from typing import Tuple
from typing import Optional
from typing import Callable
import os
from datetime import datetime
import json
from DataIO import data_loader as dl
from abc import ABC, abstractmethod
from util.keras_ver... | {"hexsha": "0620fc3d19fecfecb517f97efd3d8cd89e969c7f", "size": 6730, "ext": "py", "lang": "Python", "max_stars_repo_path": "network_model/wrapper/abstract_model.py", "max_stars_repo_name": "yuga-n/ModelLearner", "max_stars_repo_head_hexsha": "3193efd5eb15172ba8231a34829942040fcb0fc5", "max_stars_repo_licenses": ["MIT"]... |
# Random Signals and LTI-Systems
*This jupyter notebook is part of a [collection of notebooks](../index.ipynb) on various topics of Digital Signal Processing.
## Introduction
The response of a system $y[k] = \mathcal{H} \{ x[k] \}$ to a random input signal $x[k]$ is the foundation of statistical signal processing. I... | {"hexsha": "9fc1c1ab65ed565c5478553c967678b16003c728", "size": 75464, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Lectures_Advanced-DSP/random_signals_LTI_systems/introduction.ipynb", "max_stars_repo_name": "lev1khachatryan/ASDS_DSP", "max_stars_repo_head_hexsha": "9059d737f6934b81a740c79b33756f... |
#include <Eigen/Dense>
#include "partitionlist.hpp"
using namespace Eigen;
template <class uint>
class AssemblyOp : public EigenBase< AssemblyOp<uint> > {
private:
const MatrixXd& A;
const MatrixXd& B;
const PartitionList<uint>& states;
public:
// eigen boilerplate
typedef double Scalar;
... | {"hexsha": "48470bb31211f9110df72c231cdee6999a01f05e", "size": 12414, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "moment-solver/src/assemblyop.hpp", "max_stars_repo_name": "jasondark/dissertation", "max_stars_repo_head_hexsha": "3e1117ef0d14aa8d659f80df3edde1c266815856", "max_stars_repo_licenses": ["Unlicense"... |
module MaybeFin
import Data.Fin
%default total
%access public
data MaybeFin : Nat -> Type where
NoFin : MaybeFin Z
SomeFin : Fin (S k) -> MaybeFin (S k)
instance Cast (MaybeFin n) (Maybe (Fin n)) where
cast NoFin = Nothing
cast (SomeFin x) = Just x
| {"hexsha": "2d3644182ef09b3ed7dbd3e3e5af36a08b598da4", "size": 275, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "src/MaybeFin.idr", "max_stars_repo_name": "PolyglotSymposium/void.idr", "max_stars_repo_head_hexsha": "bdea1aeea6758928d79c47cefc0ac94bb1a7de1e", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
module ComradeSoss
#Turn off precompilations because of GG bug https://github.com/cscherrer/Soss.jl/issues/267
__precompile__(false)
using HypercubeTransform
using Reexport
@reexport using Soss
@reexport using Comrade
import Distributions
const Dists = Distributions
using MeasureTheory
using NamedTupleTools
using Nes... | {"hexsha": "603f654b9cdc7b280861b333f52a27fba3f09c42", "size": 1609, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ComradeSoss.jl", "max_stars_repo_name": "ptiede/ComradeSoss.jl", "max_stars_repo_head_hexsha": "15137764a1fc9e8cf6c9b9c3f88cb13accc18a19", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from os import listdir
from os.path import isfile, join
import glob
from datetime import datetime
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.ensemble import RandomForestRegressor
from sklear... | {"hexsha": "8d200a13a58139567cfb9659cf7d4184b292430a", "size": 1167, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/model/model.py", "max_stars_repo_name": "stiag0/proyInt2inventoryAdvisor", "max_stars_repo_head_hexsha": "c7bc475890777f30a1b9f21094c479300b7386a6", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma [cong]: "syntax_nomatch x y = syntax_nomatch x y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. syntax_nomatch x y = syntax_nomatch x y
[PROOF STEP]
by simp | {"llama_tokens": 72, "file": "Van_Emde_Boas_Trees_Separation_Logic_Imperative_HOL_Tools_Syntax_Match", "length": 1} |
\chapter{Production data}\label{appen:proddata}
In this Appendix, the number of tokens with the different phonetic characteristics are listed separately for CR and NCR girls. The minimum pitch and the maximum pitch are identical for several tokens because tokens with a pitch more than two standard deviations from t... | {"hexsha": "8974f7ecc8a706b9ab696e9fe2d8d09b64c1804a", "size": 3002, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "original/chapters/appendix2.tex", "max_stars_repo_name": "langsci/Drager", "max_stars_repo_head_hexsha": "556ce56d71fd4a8d2e13ed5905f9517ddf8c5b84", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_st... |
from __future__ import division, print_function
import sys
import os
import glob
import numpy as np
import tensorflow as tf
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from flask import Flask,... | {"hexsha": "1e4d3a857a05a738ff2f59a7b27a1a1c6cae9c26", "size": 1308, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "Tanx-123/BrainTumor-Detector", "max_stars_repo_head_hexsha": "dedad31784782e8656a25ae261674d55ce55971c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
from torchvision import datasets, transforms
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from argparse import ArgumentParser
from tqdm import tqdm
import time
import numpy as np
###########
# file imports / path issues
import os
import sys
from pathlib i... | {"hexsha": "a796f163a4e93eaf6cb2bb07f5f6946ddb16f586", "size": 4459, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tests/attribution_calculation/kernelshap/iterate_divorce_kernelshap.py", "max_stars_repo_name": "anonymous29387491/iclr2022", "max_stars_repo_head_hexsha": "60c5727f8519e64610b632d074510587fb7ff69... |
HANSARD REVISE * NUMERO 184
Le jeudi 18 fevrier 1999
REPONSE DU GOUVERNEMENT A DES PETITIONS
LES COMITES DE LA CHAMBRE
Defense nationale et anciens combattants
M. Pat O'Brien
L'expose budgetaire du ministre des Finances
M. Dennis J. Mills
LE DECES DU COMEDIEN YVON DUFOUR
LE DECES DE KIRK MILLER
LE CONSEIL POU... | {"hexsha": "8787b7b728710d6f545261234abbcace806c9c54", "size": 82110, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "data/Hansard/Training/hansard.36.1.house.debates.184.f", "max_stars_repo_name": "j1ai/Canadian_Hansards_Neural_Machine_Translation", "max_stars_repo_head_hexsha": "554666a89090fc1b1d1fb83601a2e9d... |
# Copyright 2021 The Cirq Developers
#
# 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 ... | {"hexsha": "dc0191ff2b160d67cb7ed2eb1495abf1641d0172", "size": 4773, "ext": "py", "lang": "Python", "max_stars_repo_path": "cirq-core/cirq/sim/act_on_args.py", "max_stars_repo_name": "stubbi/Cirq", "max_stars_repo_head_hexsha": "6d2cd16991bd7fde352010d31010f85d7eafc0ba", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import warnings
from astropy import units as u
from stdatamodels.validate import ValidationWarning
from jwst.datamodels import ReferenceFileModel
class WFC3GrismModel(ReferenceFileModel):
"""
A model for a reference file of type "specwcs" for HST IR grisms (G141 and
G102). This reference file contains th... | {"hexsha": "b51e5d5027f9ff08c931be65d4d2b05e65d52fbf", "size": 3270, "ext": "py", "lang": "Python", "max_stars_repo_path": "astrogrism/config/HST/reference_file_generators/wcs_ref_model.py", "max_stars_repo_name": "rosteen/astrogrism", "max_stars_repo_head_hexsha": "9e672c0ce80a322f2128cca4283c16a2afe6cda1", "max_stars... |
import json
import numpy as np
with open("data/widgets/annotations/all.json") as f:
all_annotations = json.load(f)
samples_count = len(all_annotations["annotations"])
validation_ids = np.random.choice(samples_count, size=62, replace=False)
valid_dict = all_annotations.copy()
valid_dict["annotations"] = [valid_dic... | {"hexsha": "e46b6d4f213118cbb0cedafc903bc8e3f366461e", "size": 711, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/split_coco.py", "max_stars_repo_name": "ntoxeg/siamese-triplet", "max_stars_repo_head_hexsha": "e4cc6c04dd1b4596ffce6f9920862ae846e7840e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
# -*- coding: utf-8 -*-
# @Time : 2020/9/7 9:41
# @Author : wlz
# @Project : TextLevelGNN
# @File : dataset.py
# @Software: PyCharm
# Dataset: https://github.com/yao8839836/text_gcn/tree/master/data
import os
import torch
import numpy as np
from utils.instance import Instance
def load_data(data_path):
a... | {"hexsha": "4bc542166e5e428cb3f8d588d6519a8e26c6e654", "size": 3370, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/dataset.py", "max_stars_repo_name": "LindgeW/TextLevelGNN", "max_stars_repo_head_hexsha": "e63c80a4be553c3f55c4810d736c46d7b98e7369", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
{-# LANGUAGE MultiParamTypeClasses #-}
{-# LANGUAGE FlexibleInstances #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE BangPatterns #-}
-- |
-- Module : Data.Matrix.Generic.Mutable
-- Copyright : Copyright (c) 2012 Aleksey Khudyakov <alexey.skladnoy@gmail.com>
-- License : BSD3
-- Maintainer... | {"hexsha": "460f838bec6d5a5d721852ad9e1ab69910ecbc4d", "size": 7396, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "Data/Matrix/Generic/Mutable.hs", "max_stars_repo_name": "Shimuuar/blas-lapack", "max_stars_repo_head_hexsha": "1b1bd3d1a61c4068a295a92ca369bb807f5868fb", "max_stars_repo_licenses": ["BSD-3-Clause... |
# -*- coding: utf-8 -*-
"""
This file contains the Kernel class. An object that
returns a kernel function
"""
import numpy as np
class Kernel():
def __init__(self, choice, param1=None, param2=None):
self.kernel = set_kernel_by_choice(choice, param1, param2)
self.choice = choice
def get_... | {"hexsha": "3b742725371fbf704922d3962fe17cc04eab8c30", "size": 1128, "ext": "py", "lang": "Python", "max_stars_repo_path": "mySVM/kernel.py", "max_stars_repo_name": "sheeshee/mySVM", "max_stars_repo_head_hexsha": "fc52a76191cd0422278a8b53ecffa9796f2493e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Functions to deal with scatter of halo mass estimates."""
import numpy as np
from scipy import interpolate
from astropy.table import Table, join
from . import utils
__all__ = ['compare_model_dsigma', 'get_scatter_summary', 'get_chi2_curve',
'get_dsig_chi2'... | {"hexsha": "f05cb39c09ac9f827ea3f1710f1f65d6b56bd61c", "size": 7294, "ext": "py", "lang": "Python", "max_stars_repo_path": "jianbing/scatter.py", "max_stars_repo_name": "mattkwiecien/jianbing", "max_stars_repo_head_hexsha": "0fbf82c973c6761d892115281b52ad9964c731db", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
""" This contains Thiel analysis plots """
# TO DO: change all the data to the form dfsim.metric
import px4tools
import numpy as np
import math
import io
import os
import sys
import errno
#import thiel_analysis
from bokeh.io import curdoc,output_file, show
from bokeh.models.widgets import Div
from bokeh.layouts im... | {"hexsha": "1634ce4759b281358769e56676743ca9a45c4a21", "size": 16613, "ext": "py", "lang": "Python", "max_stars_repo_path": "thiel_app/thiel_analysis_plots_old.py", "max_stars_repo_name": "zlite/PX4_flight_review", "max_stars_repo_head_hexsha": "66697465ac87a484af07fc310cbf9030bc15764e", "max_stars_repo_licenses": ["BS... |
<p align="center">
</p>
## Data Science Basics in Python
### Bootstrap for Uncertainty Models
#### Michael Pyrcz, Associate Professor, University of Texas at Austin
##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleSchol... | {"hexsha": "613f3453a78d38f22c80048214521324e17a2649", "size": 360715, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "PythonDataBasics_Bootstrap.ipynb", "max_stars_repo_name": "caf3676/PythonNumericalDemos", "max_stars_repo_head_hexsha": "206a3d876f79e137af88b85ba98aff171e8d8e06", "max_stars_repo_l... |
"""Landlab component that generates a random fire event in time.
This component generates a random fire event or fire time series from the
Weibull statistical distribution.
.. codeauthor:: Jordan Adams
This component generates random numbers using the Weibull distribution
(Weibull, 1951). No particular units must be... | {"hexsha": "e2f45fd2874076518f582a05c9fc9338633f6be3", "size": 4540, "ext": "py", "lang": "Python", "max_stars_repo_path": "landlab/components/fire_generator/generate_fire.py", "max_stars_repo_name": "SiccarPoint/landlab", "max_stars_repo_head_hexsha": "4150db083a0426b3647e31ffa80dfefb5faa5a60", "max_stars_repo_license... |
/**
* Copyright (c) 2017 Melown Technologies SE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the f... | {"hexsha": "28d562c91578ba6b112996fddca3582895309d98", "size": 26880, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tools/src/tools/slpk2vts.cpp", "max_stars_repo_name": "a180285/vts-tools", "max_stars_repo_head_hexsha": "114fe59b9dc46a7f1ab3204a977cf066115f018b", "max_stars_repo_licenses": ["BSD-2-Clause"], "ma... |
import random
import numpy as np
class EpsGreedyPolicy:
def __init__(self, qtable, n_actions, epsilon=0.1):
self.n_actions = n_actions
self.epsilon = epsilon
self.qtable = qtable
def set_epsilon(self, epsilon):
self.epsilon = epsilon
def __call__(self, obs, return_... | {"hexsha": "bf3c7311eb8bd4e9ca4ea51cb09b6d777ae3b097", "size": 1480, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl_tabular/policy.py", "max_stars_repo_name": "michalgregor/rl_tabular", "max_stars_repo_head_hexsha": "a7c6b6141dd9e40b96a8c9079f8c18f2af039fd1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import cv2 as cv
import numpy as np
units = 120
img_e = units * 4
floor = cv.imread('tile_texture7.jpg')
floor = cv.resize(floor, (units * 2, units * 2), interpolation=cv.INTER_CUBIC)
wall = cv.imread('tile_texture9.jpg')
wall_oh, wall_ow, _ = wall.shape
wall_projective_mat = cv.getPerspectiveTransform(
np.float3... | {"hexsha": "3a775dab3cfe15f3eaf3fde12ff39394168cdb88", "size": 906, "ext": "py", "lang": "Python", "max_stars_repo_path": "B4S1 - Computer Vision/Practice 05.py", "max_stars_repo_name": "abc1236762/UniversityHomework", "max_stars_repo_head_hexsha": "688f6fc45d610f84c0c24a6d5ab75ea70ea6a59f", "max_stars_repo_licenses": ... |
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Import
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Import Standard Libraries
import altair as alt
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import streamlit as st
import tensorflow as tf
import time as t
# Import User Libraries... | {"hexsha": "a30dfa8dac61008b1c393d44451cf013c14bf103", "size": 8179, "ext": "py", "lang": "Python", "max_stars_repo_path": "streamlit/components/imageClassifier.py", "max_stars_repo_name": "DCEN-tech/Mushroom_Py-cture_Recognition", "max_stars_repo_head_hexsha": "33a85ee401e0d812f53cab620c16466f2134a441", "max_stars_rep... |
from __future__ import print_function, absolute_import
import time
import torch
import numpy as np
import torch.nn.functional as F
from PIL import ImageFile
from utils.meters import AverageMeter
from .ranking import cmc, mean_ap
from .cnn import extract_cnn_feature
ImageFile.LOAD_TRUNCATED_IMAGES = True
def extra... | {"hexsha": "09b22b108f8c8ec96525a7907070d98fb0506203", "size": 5267, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluate/evaluators.py", "max_stars_repo_name": "YantaoShen/openBCT", "max_stars_repo_head_hexsha": "69e798c2dd6380572da7a88b68e0e9d31d9b08a4", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Deedy - One Page Two Column Resume
% LaTeX Template
% Version 1.1 (30/4/2014)
%
% Original author:
% Debarghya Das (http://debarghyadas.com)
%
% Original repository:
% https://github.com/deedydas/Deedy-Resume
%
% IMPORTANT: THIS TEMPLATE NEEDS TO BE COMPILED WITH XeLaTeX
%
% Th... | {"hexsha": "546eb18a5424bfa2a85864b75351b9e2d3472b5b", "size": 6881, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "resumeaug16.tex", "max_stars_repo_name": "annalorimer/annalorimer.github.io", "max_stars_repo_head_hexsha": "3f0bf64e53c69729c74e2c21720cb39e0bfe5212", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
# Basic libraries
import os
import io
import sys
import numpy as np
from os import walk
from tokenizers import ByteLevelBPETokenizer
# Parsing arguments
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--labeled_data_folder", type=str, default = "labeled_data", help="Labeled data folder")
parser... | {"hexsha": "d1330ca69bd44c7c55683a3db5ef87fc1a47bac8", "size": 2157, "ext": "py", "lang": "Python", "max_stars_repo_path": "create_data_for_tokenization.py", "max_stars_repo_name": "pooki3bear/phishytics-machine-learning-for-phishing", "max_stars_repo_head_hexsha": "c6033e62638abf6ed590cfa54be8918e7050a5c9", "max_stars... |
import numpy as np
import cv2
import os
def histEqulColor(img):
ycrcb = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)
channels = cv2.split(ycrcb)
cv2.equalizeHist(channels[0], channels[0])
cv2.merge(channels, ycrcb)
cv2.cvtColor(ycrcb, cv2.COLOR_YCR_CB2BGR, img)
return img
dir_path = os.path.dirna... | {"hexsha": "0ffae4a354aa52edb139b9dd9575b82e41c68af8", "size": 855, "ext": "py", "lang": "Python", "max_stars_repo_path": "histogram-equalization.py", "max_stars_repo_name": "11Skywalker11/dual-fisheye-video-stitching-in-Python3", "max_stars_repo_head_hexsha": "bb94db1597a643e7eda52de85e1e297888be0ccc", "max_stars_repo... |
import gtfs_kit as gk
import numpy as np
import pandas as pd
from syspy.spatial import spatial
def build_stop_clusters(
stops, distance_threshold=150, col='cluster_id', use_parent_station=False
):
"""
Apply agglomerative clustering algorithm to stops.
Add a column cluster_id with the cluster id.
I... | {"hexsha": "9d25fbbeeb84ead550c3c9ec9e048d96ef9ca6e7", "size": 3270, "ext": "py", "lang": "Python", "max_stars_repo_path": "quetzal/io/gtfs_reader/patterns.py", "max_stars_repo_name": "systragroup/quetzal", "max_stars_repo_head_hexsha": "bb7934bcae588cddf0f0da810d75114d1c64768f", "max_stars_repo_licenses": ["CECILL-B"]... |
# Copyright 2017 The TensorFlow 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 by applic... | {"hexsha": "831559b3a72397320d4d20a463f448864f1bef39", "size": 1977, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_feather_eager.py", "max_stars_repo_name": "pshiko/io", "max_stars_repo_head_hexsha": "a1793e6b41ed7a8db572249aba15a8e513a348a5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
#
# This file is part of the Actors.jl Julia package,
# MIT license, part of https://github.com/JuliaActors
#
include("delays.jl")
using Actors, Test, .Threads, .Delays
import Actors: spawn, info, diag, newLink
Base.:(==)(l1::Link, l2::Link) = hash(l1) == hash(l2)
t1 = Ref{Task}()
t2 = Ref{Task}()
t3 = Ref{Task}()... | {"hexsha": "de64ad32c5ccf8f608a37a7e78d4eebe7994f65e", "size": 5649, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_errorhandling.jl", "max_stars_repo_name": "omus/Actors.jl", "max_stars_repo_head_hexsha": "3790b60580134ec3f5a83211b011b3159dee04d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
function kernel = create_kernel(kernel_type, pars, nMax, lb, ub, bound_pars)
%% create convolution kernel
%% inputs:
% kernel_type: string, convolution kernel type. now support {'exp',
% 'exp2', 'vector'}
% pars: parameters for the selected kernel type
% nMax: length of the kernel
% lb: lower bound fo... | {"author": "zhoupc", "repo": "CNMF_E", "sha": "ccca6f9db7d1d15b7dd1266eb9b29e417f92e79f", "save_path": "github-repos/MATLAB/zhoupc-CNMF_E", "path": "github-repos/MATLAB/zhoupc-CNMF_E/CNMF_E-ccca6f9db7d1d15b7dd1266eb9b29e417f92e79f/OASIS_matlab/packages/oasis_kernel/create_kernel.m"} |
from hyper_param import *
from sklearn.metrics.pairwise import cosine_similarity
from scipy.spatial.distance import jensenshannon
from sklearn.feature_extraction.text import TfidfVectorizer
from matplotlib import pyplot as plt
TOP_K = 10
N_UNIQUE_QUESTIONS = 50 # Nedeed for time/memory reasons
N_UNIQUE_CONT... | {"hexsha": "eb853aa748b3ae4fbdac17766cf5dbcbcebb891e", "size": 5939, "ext": "py", "lang": "Python", "max_stars_repo_path": "ir_module/src/tf_idf_ir.py", "max_stars_repo_name": "Online-Trio/projecide_squad", "max_stars_repo_head_hexsha": "58d8bcd64240d9d8d2b918564e4558ed6eee4ecd", "max_stars_repo_licenses": ["Apache-2.0... |
import numpy as np
import pandas as pd
from skimage import io
from scipy.misc import imread, imsave
import os
import imageio
def get_masks(path_prediction):
prediction = imageio.imread(path_prediction)
# compute the axon mask
axon_prediction = prediction > 200
# compute the myelin mask
myelin_pr... | {"hexsha": "bffbf3b4ca4ca55baa08b1fea05c3e913b7929c3", "size": 1576, "ext": "py", "lang": "Python", "max_stars_repo_path": "AxonDeepSeg/visualization/get_masks.py", "max_stars_repo_name": "sagoyal2/edit_axondeepseg", "max_stars_repo_head_hexsha": "73ac9759fbe4887d87c0baa701828f9e24283b04", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python
import shlex, subprocess
import numpy as np
import pandas as pd
import math
import os
import shutil
import re
import sys
import util
import urllib
from os import sys, path
sys.path.insert(0, path.join(path.dirname(path.abspath(__file__)),'../'))
from IPython.core.debugger import Tracer
import... | {"hexsha": "0d2267ba6a145b5ee7d2038259132556c7ecaa8f", "size": 1258, "ext": "py", "lang": "Python", "max_stars_repo_path": "database/myutils.py", "max_stars_repo_name": "cozy9/Metascape", "max_stars_repo_head_hexsha": "261901657bef5e1060f1ae86a2a3913d1e4c87c4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
!Program which will exam if a x and y cord is inside or outside of a circle
program read_circle
implicit none
real::x,y,x1,y1,h,k,r,s
print*, 'Enter the value of x and y: '
read*,x,y
print*, 'Enter the value of h and k: '
read*,h,k
print*,'enter the value of x1,y1'
read*,x1,y1
r=sqrt... | {"hexsha": "dca6f920a65d5a288b6f1053f627622d8c0d36ce", "size": 625, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "examine_the_point_x1_y1_lies_ioro_circle.f90", "max_stars_repo_name": "ArkAngeL43/tron95", "max_stars_repo_head_hexsha": "154d72613536ba35c9ef611006003c5fd529eb00", "max_stars_repo_licenses": ["M... |
#include "bindings-math.h"
#include "../luaapi/context.h"
#include "../luaapi/types.h"
#include "../luaapi/macros.h"
#include "CVec.h"
#include "util/angle.h"
#include "util/math_func.h"
#include "CodeAttributes.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <iostream>
using std::cerr;
using std::... | {"hexsha": "dfdbad5527867cf544cb77032b362c44fce66237", "size": 4873, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gusanos/lua/bindings-math.cpp", "max_stars_repo_name": "JiPRA/openlierox", "max_stars_repo_head_hexsha": "1d9a490cb3b214c7f6dad3a7d582b54373b5b9dc", "max_stars_repo_licenses": ["CECILL-B"], "max... |
from typing import Union
import numpy as np
def uniform_dist(low: Union[float, int], high: Union[float, int]):
"""Random data generator for the uniform distribution.
Args:
low (Union[float, int]): The minimum value that can be generated
high (Union[float, int]): The maximum value that can be g... | {"hexsha": "ea3aa00e927382c7869873b3bd12a8b43d62481d", "size": 1217, "ext": "py", "lang": "Python", "max_stars_repo_path": "implementacion/framework/generators.py", "max_stars_repo_name": "brodriguez059/TFG.Producto", "max_stars_repo_head_hexsha": "e7d2068930e574d29debdde84352af73cbbd5d59", "max_stars_repo_licenses": [... |
REBOL [
System: "REBOL [R3] Language Interpreter and Run-time Environment"
Title: "REBOL 3 HTTP protocol scheme"
Rights: {
Copyright 2012 REBOL Technologies
REBOL is a trademark of REBOL Technologies
}
License: {
Licensed under the Apache License, Version 2.0
See: http://www.apache.org/licenses/LICENSE-2.0... | {"hexsha": "21f5b5d8005ad76ebd8287e5eb2f7047ac699b17", "size": 13321, "ext": "r", "lang": "R", "max_stars_repo_path": "src/mezz/prot-http.r", "max_stars_repo_name": "BrianHawley/rebol", "max_stars_repo_head_hexsha": "25033f897b2bd466068d7663563cd3ff64740b94", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
import numpy as np
import scipy.stats as sps
from simulations.toolbox import owner_position
##
##
## Simulation functions
##
##
#
# GMB simulation of asian option, possibility of choosing different distribution than normal
#
def asian_simulation_gbm_final(*,position_flag,initial_price, strike,
simulations, ste... | {"hexsha": "586c59fedb81f5f20d1f64e359aceb79c5b84101", "size": 12047, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulations/simulations.py", "max_stars_repo_name": "sedlakp/exotic-options-simulations", "max_stars_repo_head_hexsha": "f50886374111b9c4afa93d2c94f746e5796f657c", "max_stars_repo_licenses": ["MI... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.