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/-
Copyright (c) 2017 Mario Carneiro. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Mario Carneiro, Floris van Doorn, Violeta Hernández Palacios
! This file was ported from Lean 3 source module set_theory.cardinal.cofinality
! leanprover-community/mathlib commit bb16... | {"author": "leanprover-community", "repo": "mathlib4", "sha": "b9a0a30342ca06e9817e22dbe46e75fc7f435500", "save_path": "github-repos/lean/leanprover-community-mathlib4", "path": "github-repos/lean/leanprover-community-mathlib4/mathlib4-b9a0a30342ca06e9817e22dbe46e75fc7f435500/Mathlib/SetTheory/Cardinal/Cofinality.lean"... |
#include "algorithms/synthesis/syrec_synthesis.hpp"
#include "core/syrec/expression.hpp"
#include "core/syrec/program.hpp"
#include "core/syrec/variable.hpp"
#include "core/utils/timer.hpp"
#include <boost/dynamic_bitset.hpp>
#include <cmath>
#include <functional>
#include <memory>
#include <numeric>
namespace syrec... | {"hexsha": "c17aebffe3f6bca249775885cff4b6d780623abc", "size": 58253, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/algorithms/synthesis/syrec_synthesis.cpp", "max_stars_repo_name": "SmaranTum/syrec", "max_stars_repo_head_hexsha": "7fd0eece4c3376e52c1f5f17add71a49e5826dac", "max_stars_repo_licenses": ["MIT"]... |
from sklearn import metrics, preprocessing
import numpy as np
import csv
import pandas as pd
# we assume data is distributed normally
def processing_data(data):
# Normalization
scaler = preprocessing.StandardScaler()
data = data.astype(str).astype(int)
X = data.get('X')
Y = data.get('Y')
# Pr... | {"hexsha": "8c47c4a58b741e5feaf290ea9a4603533af24c32", "size": 994, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing.py", "max_stars_repo_name": "wruoting/Assignment3-ML", "max_stars_repo_head_hexsha": "d3766ffbfc8ddd050f2979cd47665af4d441d78d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import itertools as it
from typing import Tuple, List
import numpy as np
from .base_model import BaseModel
from pyffm.util import logistic
class FMModel(BaseModel):
def __init__(
self, num_latent, num_features, reg_lambda, use_linear=False, **kwargs
):
super().__init__(
num_featur... | {"hexsha": "0bcc07339f53002a66fc43939a66d6aa7c1b1f1c", "size": 2165, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyffm/engine/model/fm_model.py", "max_stars_repo_name": "mascaroa/pyffm", "max_stars_repo_head_hexsha": "2445ed2c048347ebbfc76d39990065eb76a8d784", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import pandas as pd
import gdal
import numpy as np
import os
import rasterio
import tqdm
class TrainingData:
"""Prepares training datasets using a raster stack, species occurrences and a set of band means and standard
deviations.
:param self: a class instance of TrainingData
:param oh: an Occurrence... | {"hexsha": "907ad7b0fb8e28b6060d5a5655b9d52b344d7059", "size": 5135, "ext": "py", "lang": "Python", "max_stars_repo_path": "sdmdl/sdmdl/data_prep/training_data.py", "max_stars_repo_name": "naturalis/trait-geo-diverse-angiosperms", "max_stars_repo_head_hexsha": "034ce4e807f7a83d31bf7b46435275ba91cfcb00", "max_stars_repo... |
from classifiers import SVM, KRL
import argparse
from utils import get_sequence, get_mismatch, load_data
from Kernels import Kernel, get_gram_cross
import numpy as np
import csv
from tqdm import tqdm
def get_gram_matrix(x_tr, x_te, k, n_mismatch, n_kernel):
dict_sequences = get_sequence(x_tr, k=k)
embeddings... | {"hexsha": "751d7057c93547c33fe96a7dcf1a23f0eb25b010", "size": 5062, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "HamzaG737/Kmml-challenge-code", "max_stars_repo_head_hexsha": "c7ae3e26a7e02e4951758c683f29c23c2ab9b3e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
\chapter{Development and Results}
%Once the state of the art has been studied and the main lines of work of energy reduction are known, analysis of all the techniques will be made.
\input{Chapter21}
\clearpage
\input{Chapter22}
\input{Chapter23}
%%\input{Chapter33}
\input{Chapter24}
\clearpage
\input{Chapter25}
\clea... | {"hexsha": "83fb6237335c1f545cc18a783eaff5d995321dc8", "size": 344, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapter2.tex", "max_stars_repo_name": "alvarolop/tfg_latex", "max_stars_repo_head_hexsha": "9b5d4e89183ba775aae81c60da5ea170ffd22f79", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import torch
import cv2
import numpy as np
from operator import itemgetter
# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import ... | {"hexsha": "2e1cb9c70ea3cff2a4622af93c99a42e7198ddbd", "size": 8475, "ext": "py", "lang": "Python", "max_stars_repo_path": "player_detection.py", "max_stars_repo_name": "Basket-Analytics/BasketTracking", "max_stars_repo_head_hexsha": "5921dc7a7abd74ab6e1d2c0a78642cc53e4e5ad6", "max_stars_repo_licenses": ["MIT"], "max_s... |
# 2019-11-19 10:28:31(JST)
import sys
import numpy as np
# import collections
# import math
# from string import ascii_lowercase, ascii_uppercase, digits
# from bisect import bisect_left as bi_l, bisect_right as bi_r
# import itertools
# from functools import reduce
# import operator as op
# import re
# ... | {"hexsha": "54899451608d3a0f1a298387e581ae22a4f92634", "size": 1305, "ext": "py", "lang": "Python", "max_stars_repo_path": "jp.atcoder/abc143/abc143_e/8522563.py", "max_stars_repo_name": "kagemeka/atcoder-submissions", "max_stars_repo_head_hexsha": "91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e", "max_stars_repo_licenses": ... |
""" Test the optimizer classes """
import pytest
import numpy as np
from josim_tools.optimize import NumpyVectorArray
def test_numpy_vector_array():
""" Test NumpyVectorArray class """
array_a = np.array([1, 2, 3, 4])
array_b = np.array([5, 6, 7, 8])
array_c = np.array([9, 10, 11, 12])
array_d =... | {"hexsha": "2742d3223d9bd4c5c6b80adaa3b63f923ac5ed34", "size": 1727, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_optimizer.py", "max_stars_repo_name": "JoeyDelp/josim-tools", "max_stars_repo_head_hexsha": "e6b9eb3e6b45bea53dd1b355121ee4b09867eb07", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
"""
smoothconv(z,nas)
Smoothen field `z(ns,ns)` with a spectral method at scale `ns/nas`
Takes into account missing values.
"""
function smoothspec(zi,nas)
@compat iinan=findall(isnan.(zi))
@compat iinotnan=findall(.~isnan.(zi))
zi[iinan]=0.
nss=size(zi);
ns=nss[1];
... | {"hexsha": "70efbf491fae25157eda4348f8997250083ef5a8", "size": 759, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rf/smoothspec.jl", "max_stars_repo_name": "UnofficialJuliaMirror/RainFARM.jl-e9a4e08f-a0a3-5224-a821-6d0231c12d6b", "max_stars_repo_head_hexsha": "740f4edff721692e13168b132503aa62d5fea574", "max... |
import numpy as np
def affine(r, c, x0, dxx, dyx, y0, dxy, dyy):
"""
Returns the affine transform -- normally row, column to x,y position.
If this is the geotransform from a gdal geotiff (for example) the coordinates are the displayed pixel corners - not the center.
If you want the center of the pixel... | {"hexsha": "e80867d7c98bd047ac5367772a3be7550ba6f2cf", "size": 7370, "ext": "py", "lang": "Python", "max_stars_repo_path": "HSTB/shared/gridded_coords.py", "max_stars_repo_name": "noaa-ocs-hydrography/shared", "max_stars_repo_head_hexsha": "d2004e803c708dffa43d09d3ffea4e4045811b28", "max_stars_repo_licenses": ["CC0-1.0... |
#! /usr/bin/env python
############################# BEGIN FRONTMATTER ################################
# #
# TEA - calculates Thermochemical Equilibrium Abundances of chemical species #
# ... | {"hexsha": "00dbe4fc65d369635f2edfe00b67a4bf96f96cf9", "size": 14125, "ext": "py", "lang": "Python", "max_stars_repo_path": "run/runatm.py", "max_stars_repo_name": "SiddhantDeshmukh/TEA", "max_stars_repo_head_hexsha": "beaa882b7084d380a38a6bf5f219b0ee848afb9e", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars... |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
a module for basic VO Registry interactions.
A VO registry is a database of VO resources--data collections and
services--that are available for VO applications. Typically, it is
aware of the resources from all over the world. A registry can find ... | {"hexsha": "fb50620deb48ec23560b5aeaca15777bc86e9dde", "size": 35115, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyvo/registry/vao.py", "max_stars_repo_name": "bsipocz/pyvo", "max_stars_repo_head_hexsha": "290159d3e7218f6f8d9edee3145cfd2bee190130", "max_stars_repo_licenses": ["BSD-3-Clause-No-Nuclear-Licens... |
import os
import argparse
import ast
import gc
import logging
import math
import sys
import time
import numpy as np
import pandas as pd
import hparameters
import utility
import json
def main(m_params, start_counter=0):
param_dictionary = {} #Format is key:value = htune_number: dict containing variable param... | {"hexsha": "550085e3212a0656c80c84c10e89d8d9cbb196e7", "size": 6683, "ext": "py", "lang": "Python", "max_stars_repo_path": "hypertuning.py", "max_stars_repo_name": "Akanni96/TRUNET", "max_stars_repo_head_hexsha": "12dff08f2361848e13b0952540e2198db386eab8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_... |
!-----------------------------------------------------------------------
! Fortran2003 interface to CUDA Library for Skeleton 2D Electrostatic
! GPU-MPI PIC Code */
! written by Viktor K. Decyk, UCLA
module gpuppush2_c
use iso_c_binding
implicit none
!
interface
subroutine cgpuppgppush2... | {"hexsha": "f70b12ba7faccbfd056d536ea3d13894a8a15106", "size": 7370, "ext": "f03", "lang": "FORTRAN", "max_stars_repo_path": "gpu/gpuppic2/gpuppush2_c.f03", "max_stars_repo_name": "gcasabona/cuda", "max_stars_repo_head_hexsha": "064cfa02398e2402c113d45153d7ba36ae930f7e", "max_stars_repo_licenses": ["W3C"], "max_stars_c... |
#!/usr/bin/env python
# coding: utf-8
# # Fit an H2O GBM on the Lending Club data
# ### Imports
# In[1]:
import pandas as pd
import numpy as np
import random, time, os, pickle
import matplotlib.pyplot as plt
from feature_engine import categorical_encoders as ce
from feature_engine import discretisers as dsc
from ... | {"hexsha": "2d3c0ed13fc069468328d3e76feb48d2d76de567", "size": 5379, "ext": "py", "lang": "Python", "max_stars_repo_path": "_build/jupyter_execute/H2O_with_LendingClub.py", "max_stars_repo_name": "Strabes/h2o-prod", "max_stars_repo_head_hexsha": "2bfd4c87302c2ca3219b0bc313f13c9e787d84ad", "max_stars_repo_licenses": ["M... |
"""
This module contains a function to plot the cross correlations computed with
stack_ccorr_tremor and the autocorrelations computed with
stack_acorr_tremor sorted by different criteria
"""
import obspy
from obspy.signal.cross_correlation import correlate
import matplotlib.pyplot as plt
import numpy as np
import pic... | {"hexsha": "b53bb362d4c66208336225967578237471093a47", "size": 20259, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_stack_sort.py", "max_stars_repo_name": "ArianeDucellier/timelags", "max_stars_repo_head_hexsha": "383c5702ad25405555d934c984ac8245722f8596", "max_stars_repo_licenses": ["MIT"], "max_star... |
from manimlib.imports import *
import numpy as np
from manim_reveal import SlideScene
# import manimtda
from manimtda.linalg import *
class LLCombine(SlideScene):
CONFIG={
"camera_config":{"background_color":"#F0F1EB"},
"video_slides_dir":"../video_slides"
}
def construct(self):
t... | {"hexsha": "b7553cf781954b615d1ec3aa3de6cd26dd9a979e", "size": 774, "ext": "py", "lang": "Python", "max_stars_repo_path": "animations/LL_combine.py", "max_stars_repo_name": "bnels/cse21", "max_stars_repo_head_hexsha": "daad575743bfa7c025c507b3a18a3dff445ca385", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
'''
Copyright 2016 Jihun Hamm
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, ... | {"hexsha": "52d0dff0c86a12650225e88f285c7b6939bf8ebc", "size": 4255, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_NN_genki.py", "max_stars_repo_name": "jihunhamm/MinimaxFilter", "max_stars_repo_head_hexsha": "fa9ee7aa126cbf651c4c9cbf076e4ba848fcfc46", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import nan_euclidean_distances
from statsmodels.distributions.empirical_distribution import ECDF
from matplotlib import pyplot as plt
import json
SAMPLES_FNAME = './data/samples_pathlens_100000.csv'
SIMILARITY_MATRIX_FNAME = './data/similarity_matri... | {"hexsha": "15a4937b744b381456bed3143e7c4122d36eff85", "size": 5772, "ext": "py", "lang": "Python", "max_stars_repo_path": "TEMP_pavlos/calculate_similarity_from_pathlens.py", "max_stars_repo_name": "cgeorgitsis/ai4netmon", "max_stars_repo_head_hexsha": "36c4c1695fd980705d3e3f76385cda14baf7f397", "max_stars_repo_licens... |
function qq = ppdiff(pp,j)
%PPDIFF Differentiate piecewise polynomial.
% QQ = PPDIFF(PP,J) returns the J:th derivative of a piecewise
% polynomial PP. PP must be on the form evaluated by PPVAL. QQ is a
% piecewise polynomial on the same form. Default value for J is 1.
%
% Example:
% x = linspace(-pi,pi,9)... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/13812-splinefit/ppdiff.m"} |
import numpy as np
from console_progressbar import ProgressBar
from aspire.aspire.common.config import AspireConfig
from aspire.aspire.common.exceptions import ErrorTooBig, WrongInput, DimensionsIncompatible
from aspire.aspire.common.logger import logger
from aspire.aspire.utils.data_utils import load_stack_from_file... | {"hexsha": "b9910b2bd919d4095b61e44f0e3f405db380af50", "size": 3390, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/aspire/aspire/utils/compare_stacks.py", "max_stars_repo_name": "janden/ASPIRE-Python", "max_stars_repo_head_hexsha": "5bcf831881fd0e42630c3b99671c5ed08de260ea", "max_stars_repo_licenses": ["MI... |
# coding: utf-8
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import numpy as np
class DataAugmenter(object):
def __init__(self):
super().__init__()
def augment(self, X, y, sample_weight=None):
... | {"hexsha": "109f933c432f8b8d8092e740ead9385201981651", "size": 2110, "ext": "py", "lang": "Python", "max_stars_repo_path": "old_models/data_augment.py", "max_stars_repo_name": "victor-estrade/SystGradDescent", "max_stars_repo_head_hexsha": "822e7094290301ec47a99433381a8d6406798aff", "max_stars_repo_licenses": ["MIT"], ... |
# Create the ffunctions necessary for our analysis
# import necessary libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# create max_min function to get details of maximum and minimum of the given column
def max_min(df, col):
'''
args: df: dataframe
... | {"hexsha": "268c9eb3310f6129e0cf6e3718e2a88d4f7b4519", "size": 6403, "ext": "py", "lang": "Python", "max_stars_repo_path": "pkg/analysis_fn.py", "max_stars_repo_name": "codeslash21/TMDB_data_analysis", "max_stars_repo_head_hexsha": "116bbd4ab7d431653e39c1188ce9e437133d4396", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import random
import numpy as np
from affordable.affordable import Affordable
class AbstractGame(Affordable):
def __init__(self, ctx, name):
super().__init__(ctx, name)
self.ctx = ctx
self.affordables = []
self.actions_list = []
self.states_list = []
self.policy =... | {"hexsha": "7602d009524c0f63925af85ca2bbbe7e0d9dc2a6", "size": 3621, "ext": "py", "lang": "Python", "max_stars_repo_path": "affordable/game.py", "max_stars_repo_name": "mountain/affordable", "max_stars_repo_head_hexsha": "31834c866ddce255d4b9a1ca28973e3eae2bf939", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from supreme._build import CExtension
import os.path
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration, get_numpy_include_dirs
config = Configuration('fast', parent_package, top_path)
c_files = [f for f in os.listdir(config.local_path) if f.endswith(... | {"hexsha": "9f3f14eb5cbf9761d3f5ebe21773f5ed6b66ee57", "size": 549, "ext": "py", "lang": "Python", "max_stars_repo_path": "supreme/lib/fast/setup.py", "max_stars_repo_name": "KirillDZR/supreme", "max_stars_repo_head_hexsha": "c296722599363bd0cbcce6877bd9de9b066cb74b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
import tactic.choose
/- choice -/
example (h : ∀n m : ℕ, n < m → ∃i j, m = n + i ∨ m + j = n) : true :=
begin
choose i j h using h,
guard_hyp i : ∀n m : ℕ, n < m → ℕ,
guard_hyp j : ∀n m : ℕ, n < m → ℕ,
guard_hyp h : ∀ (n m : ℕ) (h : n < m), m = n + i n m h ∨ m + j n m h = n,
trivial
end
example (h : ∀n m : ... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/test/choose.lean"} |
#
# Copyright 2019 Xilinx 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 agreed to in writing... | {"hexsha": "3d62cc08553d7dc6791a0dccd00089b93adaa40f", "size": 1320, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/Vitis-AI-Library/graph_runner/test/run-graph-task.py", "max_stars_repo_name": "hito0512/Vitis-AI", "max_stars_repo_head_hexsha": "996459fb96cb077ed2f7e789d515893b1cccbc95", "max_stars_repo_l... |
#pragma once
#include <boost/multi_index/hashed_index.hpp>
#include <boost/multi_index/member.hpp>
#include <boost/multi_index/ordered_index.hpp>
#include <boost/multi_index/random_access_index.hpp>
#include <boost/multi_index_container.hpp>
#include <chrono>
#include <memory>
#include <cga/lib/blocks.hpp>
#include <c... | {"hexsha": "6d18d1a1adbdb1a11e9b226c7bf939a03a9b77b3", "size": 2673, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cga/node/blockprocessor.hpp", "max_stars_repo_name": "cgacurrency/cga-node", "max_stars_repo_head_hexsha": "ad21cd224bed5de4bd0cec8c2aa42d35aea842a5", "max_stars_repo_licenses": ["BSD-2-Clause"], "m... |
from scipy.special import loggamma
import matplotlib.pyplot as plt
import numpy as np
from scipy.special import loggamma
from math import log
import collections
# This function is given, nothing to do here.
def simulate_data(num_samples, tails_proba):
"""Simulate a sequence of i.i.d. coin flips.
Tails are d... | {"hexsha": "f4a160d18a707641390965dbbfcb01de809eeb0c", "size": 5773, "ext": "py", "lang": "Python", "max_stars_repo_path": "exercise03/probabilistic_inference.py", "max_stars_repo_name": "Rylie-W/ML33_21WS", "max_stars_repo_head_hexsha": "6c489953ba227bee7b534106c4ab9d02c79910e4", "max_stars_repo_licenses": ["Apache-2.... |
set_bigfloat_precision(128)
temp = BigFloat(0)
f(x) = exp(-x) - sin(x)
steffensen(c) = c - (f(c)*f(c))/( f(c+f(c)) - f(c) )
temp2 = BigFloat(100)
witch = big"0.588532743981861077432452045702903688531271516109030533319914299511672553307351427738524061576027409562153528176982466770293849745782742957500713135"
for i =... | {"hexsha": "f26a1374c245a8e3a41a55c2d6e14af32851f71c", "size": 423, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Analiza_Numeryczna_M/02Lista/zad8.jl", "max_stars_repo_name": "Magikis/Uniwersity", "max_stars_repo_head_hexsha": "06964ef31d721af85740df1dce3f966006ab9f78", "max_stars_repo_licenses": ["MIT"], "max... |
function a = circulant ( m, n, x )
%*****************************************************************************80
%
%% CIRCULANT returns the CIRCULANT matrix.
%
% Formula:
%
% K = 1 + mod ( J-I, N )
% A(I,J) = X(K)
%
% Example:
%
% M = 4, N = 4, X = ( 1, 2, 3, 4 )
%
% 1 2 3 4
% 4 1 2 3
% 3 ... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/test_mat/circulant.m"} |
#' Get header info for a document.
#'
#' @export
#' @template all
#' @template return
#' @param dbname (character) Database name. Required.
#' @param docid (character) Document ID. Required.
#' @examples \dontrun{
#' (x <- Cushion$new())
#'
#' # create a database
#' if ("sofadb" %in% db_list(x)) {
#' invisible(db_del... | {"hexsha": "4e464143cfd67400b4decaa24e93200bc648e3f8", "size": 939, "ext": "r", "lang": "R", "max_stars_repo_path": "R/doc_head.r", "max_stars_repo_name": "FTwex/sofa-cloudant", "max_stars_repo_head_hexsha": "097577be2446865e17d41bcb015141eed19c7139", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
import numpy as np
import os
import yaml
import pandas as pd
import plotly.graph_objects as go
from keras.preprocessing.image import NumpyArrayIterator
from plotly.subplots import make_subplots
def report_dataframes(report_path):
'''
'''
categories = [f for f in os.listdir(report_path) if not f.startswi... | {"hexsha": "2b7e21b76dc65487ce651258046192a9796bcc68", "size": 12791, "ext": "py", "lang": "Python", "max_stars_repo_path": "hardy/data_reporting/reporting.py", "max_stars_repo_name": "EISy-as-Py/hardy", "max_stars_repo_head_hexsha": "de873b7fe6e2c010d9eaa5e0b30550c8850e0f71", "max_stars_repo_licenses": ["MIT"], "max_s... |
runlengthencoding <- function(x)
{
splitx <- unlist(strsplit(input, ""))
rlex <- rle(splitx)
paste(with(rlex, as.vector(rbind(lengths, values))), collapse="")
}
input <- "WWWWWWWWWWWWBWWWWWWWWWWWWBBBWWWWWWWWWWWWWWWWWWWWWWWWBWWWWWWWWWWWWWW"
runlengthencoding(input)
| {"hexsha": "abef9fbd8891e2430c2d02a442b6c6c02b43a90a", "size": 275, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Run-length-encoding/R/run-length-encoding-1.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_lic... |
*
* -----------------------------------------------------------------
* N U M T E R F
* -----------------------------------------------------------------
*
* Written by G. Gaigalas, *
* Vilnius, Lithuania December 1993 *
*
FUNCTI... | {"hexsha": "033fcdbdbabcc16cb3c8bbf39b82c87532b0610b", "size": 889, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/lib/libang/numterf.f", "max_stars_repo_name": "mansour2014/ATSP2K_plus", "max_stars_repo_head_hexsha": "30842b9f086d1e497aeb778e2a352d1e8e520ec3", "max_stars_repo_licenses": ["BSD-4-Clause-UC"]... |
@testset "FBM" begin
rng = MersenneTwister(42)
h = 0.3
k = FBMKernel(; h=h)
v1 = rand(rng, 3)
v2 = rand(rng, 3)
@test k(v1, v2) ≈
(
sqeuclidean(v1, zero(v1))^h + sqeuclidean(v2, zero(v2))^h -
sqeuclidean(v1 - v2, zero(v1 - v2))^h
) / 2 atol = 1e-5
@test repr(k) ... | {"hexsha": "3fe58dd0a6c82de86b63c852d7a8a53b330f1ce1", "size": 617, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/basekernels/fbm.jl", "max_stars_repo_name": "kaandocal/KernelFunctions.jl", "max_stars_repo_head_hexsha": "231909fb6271695926e126899ac797f5628a137b", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
class WeakClassifier():
""" weak classifier - threshold on the features
Args:
X (numpy.array): data array of flattened images
(row:observations, col:features) (float).
y (numpy.array): Labels array of shape (num observations, )
"""
def __init_... | {"hexsha": "7291c8a020701bf056d51523a3fc3365d5d3f9e9", "size": 4650, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignments/ps06/helper_classes.py", "max_stars_repo_name": "jperuggia/ComputerVision", "max_stars_repo_head_hexsha": "6a36cf96dec40fe4cd5584fbc2d8e384a74a66cf", "max_stars_repo_licenses": ["MIT"]... |
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!! !!
!! GNU General Public License !!
!! !!
!! This file is part of the Flex... | {"hexsha": "7cc38fdf7069b1879822c3c5e6226bdba3d1dff0", "size": 22598, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "models/bgrid_solo/fms_src/atmos_bgrid/tools/bgrid_vert.f90", "max_stars_repo_name": "hkershaw-brown/feature-preprocess", "max_stars_repo_head_hexsha": "fe2bd77b38c63fa0566c83ebc4d2fac1623aef66"... |
#=
Provides calc_model_rv(theta, time)
Computes the velocity of the star due to the perturbations of multiple planets, as the linear superposition of the Keplerian orbit induced by each planet, i.e., neglecting mutual planet-planet interactions
=#
include("kepler_eqn.jl") # Code to solve Kepler's equ... | {"hexsha": "94565050f8750f6e9b7f58a649aaa044ea8f09d2", "size": 2359, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/radial_velocity/src/rv_model_keplerian.jl", "max_stars_repo_name": "scidom/PGUManifoldMC.jl", "max_stars_repo_head_hexsha": "766cf983b122678d47524c566ebd6fffa7f804d8", "max_stars_repo_lice... |
[STATEMENT]
lemma convex_rel_interior:
fixes S :: "'n::euclidean_space set"
assumes "convex S"
shows "convex (rel_interior S)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. convex (rel_interior S)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. convex (rel_interior S)
[PROOF STEP]
{
[PR... | {"llama_tokens": 2940, "file": null, "length": 35} |
function gpu_energy!(
pos::AbstractArray,
forces::AbstractArray,
N::Integer,
L::Real,
rc::Real,
a::Real,
b::Real,
λ::Integer,
temp::Real,
full_ener::AbstractArray,
vir::AbstractArray
)
total_energy = 0.0f0
virial = 0.0f0
force = 0.0f0
ener = 0.0f0
index =... | {"hexsha": "63337d88aa2083728284477cecea3a4e5955be4c", "size": 3134, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/kernels.jl", "max_stars_repo_name": "edwinb-ai/Cubed.jl", "max_stars_repo_head_hexsha": "c93becfd252fc11e1a3ece0c054966fd2e6010ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
import pandas as pd
import numpy as np
import argparse
import rdkit
from rdkit.Chem import AllChem
from rdkit import Chem, DataStructs
from joblib import Parallel, delayed
from tqdm import tqdm
rdkit.RDLogger.DisableLog('rdApp.*')
from dglt.contrib.moses.moses.utils import valid_smiles
parser = argparse.ArgumentPars... | {"hexsha": "9bc800048e5abe79ce6e934e47c54ac2813fb76c", "size": 6184, "ext": "py", "lang": "Python", "max_stars_repo_path": "dglt/contrib/moses/scripts/similarity.py", "max_stars_repo_name": "uta-smile/CD-MVGNN", "max_stars_repo_head_hexsha": "b48f4cd14befed298980a83edb417ab6809f0af6", "max_stars_repo_licenses": ["MIT"]... |
# Copyright 2021 The ParallelAccel 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 appl... | {"hexsha": "1458352817a1a39022d8f3155fb1eeb0f22114fc", "size": 16340, "ext": "py", "lang": "Python", "max_stars_repo_path": "parallel_accel/Simulator/asic_la/testutils.py", "max_stars_repo_name": "google/parallel_accel", "max_stars_repo_head_hexsha": "b58fda1c3a22f2aaa9a97337d602cd72c49ee8be", "max_stars_repo_licenses"... |
''' Incremental-Classifier Learning
Authors : Khurram Javed, Muhammad Talha Paracha
Maintainer : Khurram Javed
Lab : TUKL-SEECS R&D Lab
Email : 14besekjaved@seecs.edu.pk '''
import logging
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torchnet.meter impo... | {"hexsha": "1afa0403fcbc6cdf7fac4d73fa110fecc2a9b9e1", "size": 1623, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer/evaluator.py", "max_stars_repo_name": "Khurramjaved96/Dicta", "max_stars_repo_head_hexsha": "416638a3d1ad851b00394e55a7574ec978080d51", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
'''
Aggregate experiment data
- Experiment: AvidaGP L9
SUMMARY FILES
- experiment
- config + summary(evaluation) + systematics + summary(world summary)
- world
- task
'''
import argparse, os, sys, errno, csv, json
from scipy.stats import entropy
run_identifier = "RUN_" # String that identifies a run directory
d... | {"hexsha": "cb4312613a70116f6dbfe098b1a625bc3afc49cd", "size": 11343, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/2021-09-22-selection/analysis/aggregate.py", "max_stars_repo_name": "amlalejini/directed-digital-evolution", "max_stars_repo_head_hexsha": "eb19ea9182e2e9b203513433e90bd73d4768a5a1", ... |
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE Regression
#include <stdio.h>
#include <stdlib.h>
#include <string>
#include <boost/test/included/unit_test.hpp>
#include <xolotl/perf/EventCounter.h>
using namespace std;
using namespace xolotl::perf;
/**
* This suite is responsible for testing the EventCoun... | {"hexsha": "1d327c50c1839e16ad67b595c1a27c5f5b9a9cb8", "size": 1071, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/perf/EventCounterTester.cpp", "max_stars_repo_name": "ORNL-Fusion/xolotl", "max_stars_repo_head_hexsha": "993434bea0d3bca439a733a12af78034c911690c", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
#!/usr/bin/env python
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from scipy.misc import imread
from sklearn.cluster import KMeans
from sklearn.decomposition.pca import PCA
from tqdm import tqdm
from utils import (kernel_classifier_distance_and_std_from_activations,... | {"hexsha": "f74a1f06253c573af8f4b81d28f7a232ac6ab9ae", "size": 8429, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/perceptual_scores.py", "max_stars_repo_name": "furgerf/GAN-for-dermatologic-imaging", "max_stars_repo_head_hexsha": "e90b06c46c7693e984a4c5b067e18460113cd23b", "max_stars_repo_licenses": ["Apa... |
*----------------------------------------------------------------------*
subroutine transpose_contr(contr,op_info,multi)
*----------------------------------------------------------------------*
* transpose a contraction
*----------------------------------------------------------------------*
implicit no... | {"hexsha": "b32a66c02e331559da43ecbe21f893baf461a5b9", "size": 6073, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "formula/transpose_contr.f", "max_stars_repo_name": "ak-ustutt/GeCCo-public", "max_stars_repo_head_hexsha": "8d43a6c9323aeba7eb54625b95553bfd4b2418c6", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 29 16:02:24 2021
@author: jacobfaibussowitsch
"""
import os
import pickle
from collections import namedtuple
import contextlib
import meshio
import pytest
import numpy as np
import scipy.sparse as scp
cur_dir = os.path.basename(os.getcwd())
if cur... | {"hexsha": "c9e98eecff1a8746e0070f3fcc5dc70e4e945977", "size": 3613, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyhesive/test/common.py", "max_stars_repo_name": "Johnson-Research-Group/Pyhesive", "max_stars_repo_head_hexsha": "327f204b445a0db251088a29c0c3706593833d3a", "max_stars_repo_licenses": ["MIT"], "m... |
\section{Buffer abstraction}
As we have seen, some of the statements available to the programmer make use of a write buffer. Since depending on the memory model chosen, we may encounter different behaviours when running the same program using different memory models, we face the challenge of rewriting the program in a... | {"hexsha": "bf8dc6685b4b42d1f999e494906236a7d7ef5562", "size": 14827, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/writeup/ba.tex", "max_stars_repo_name": "hetmeter/awmm", "max_stars_repo_head_hexsha": "8d65b1246898b27db1ac5a6542465f71e27b1603", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
! Copyright (c) 2018, NVIDIA CORPORATION. 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 appli... | {"hexsha": "fa3e011dcdda1abc62c1bd0391446adf73f184b4", "size": 1699, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/f90_correct/src/bi08.f90", "max_stars_repo_name": "kammerdienerb/flang", "max_stars_repo_head_hexsha": "8cc4a02b94713750f09fe6b756d33daced0b4a74", "max_stars_repo_licenses": ["Apache-2.0"],... |
import argparse
import ast
import glob
import importlib
import os
import time
import warnings
from pathlib import Path
from typing import Any
import networkx as nx
from joblib import Parallel, delayed
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=RuntimeWarning)
os... | {"hexsha": "baef553cea8dd919cb4485a35d16a5ea643083db", "size": 7197, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "Abdumaleek/infinity-mirror", "max_stars_repo_head_hexsha": "b493c5602d9e4bcf374b748e9b80e7c85be54a88", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "... |
function try_parse(s)
if all(isnumeric, s)
parse(Int, s)
else
s
end
end
struct BinaryOp
op
in_1
in_2
out
BinaryOp(op, in_1::AbstractString, in_2::AbstractString, out) = new(op, try_parse(in_1), try_parse(in_2), out)
end
struct UnaryOp
op
in_
out
UnaryO... | {"hexsha": "808cbeaa4ece3fb828f5493101d23a8b31bc737e", "size": 2584, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "2015/7.jl", "max_stars_repo_name": "pLOPeGG/JuliaOfCode", "max_stars_repo_head_hexsha": "894bca6427174b84ed385b8a008b79d15d63bd7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""tests for load.py"""
import tempfile
import unittest
import warnings
import numpy as np
# has to specify the exact file to avoid nosetests error on full tests
from sknetwork.data.load import load_netset, load_konect, clear_data_home, save, load
from sknetwork.data.to... | {"hexsha": "6744c577423b5a330a5ea78478bcc87af2dde21f", "size": 4079, "ext": "py", "lang": "Python", "max_stars_repo_path": "sknetwork/data/tests/test_load.py", "max_stars_repo_name": "altana-tech/scikit-network", "max_stars_repo_head_hexsha": "dedc9d3e694c7106e4709aae22dffb5142c15859", "max_stars_repo_licenses": ["BSD-... |
//---------------------------------------------------------------------------//
// Copyright (c) 2018-2020 Mikhail Komarov <nemo@nil.foundation>
//
// 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
//-----------------... | {"hexsha": "a9bea83898cce86069ffa458dbf1c1231618b503", "size": 911, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/crypto3/block/detail/shacal/shacal1_policy.hpp", "max_stars_repo_name": "NilFoundation/boost-crypto", "max_stars_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_star... |
'''Perimeter monitoring for MD simulations using MDAnalysis'''
import glob
import MDAnalysis
from MDAnalysis.analysis import distances
import numpy as np
# Please change the workdir here..
workdir = 'REPLACEME'
top = glob.glob("{0}/**/{1}".format(workdir.rstrip("/"), '*-in-noh2o.pdb'), recursive=True)[0]
traj = glob.... | {"hexsha": "7c5ee3e45405f6b55c51f2eb6061f733e3056e9f", "size": 1977, "ext": "py", "lang": "Python", "max_stars_repo_path": "perimetermonitor.py", "max_stars_repo_name": "dmachalz/mdanalysis", "max_stars_repo_head_hexsha": "b23fb34cfc78d2d67de510c375c8cb691f39dd5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#include "Collisions.h"
#include "Box.h"
#include "Points.h"
#define EIGEN_DONT_ALIGN_STATICALLY
#include <Eigen\Dense>
using namespace std;
using namespace Eigen;
void CD(const Mesh& mesh, const shared_ptr<Obstacles> obs, std::vector<std::shared_ptr<btc::Collision> > &cls)
{
MatrixXd verts2(3, mesh.nodes.size());
... | {"hexsha": "e4e16ffbf9b65a14add4aa46e8c47a4156591c0f", "size": 3152, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Collisions.cpp", "max_stars_repo_name": "sueda/eol-cloth", "max_stars_repo_head_hexsha": "cc8f24eef81283c541b859c05dd8ceed7813271f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 85.0, ... |
#!/usr/bin/env python
# Copyright (c) 2009, South African Astronomical Observatory (SAAO) #
# All rights reserved. #
"""
SPECSENS calulates the calibration curve given an observation, a standard star,
and the extinction curve for the site. The task assumes a 1... | {"hexsha": "497cd8ead6b7e020dfcb3c2d86a18526fe891282", "size": 6366, "ext": "py", "lang": "Python", "max_stars_repo_path": "saltspec/specsens.py", "max_stars_repo_name": "Richard-Tarbell/pysalt", "max_stars_repo_head_hexsha": "2815d5533c7e60b7042f2bc3cf46cecdd38fc609", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
[STATEMENT]
lemma a_star_refl:
shows "M \<longrightarrow>\<^sub>a* M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. M \<longrightarrow>\<^sub>a* M
[PROOF STEP]
by blast | {"llama_tokens": 72, "file": null, "length": 1} |
/*
* Copyright (c) 2019 Opticks Team. All Rights Reserved.
*
* This file is part of Opticks
* (see https://bitbucket.org/simoncblyth/opticks).
*
* 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 ... | {"hexsha": "94f67d3070287ee18b97447ceac64ac0c84542d5", "size": 3312, "ext": "hh", "lang": "C++", "max_stars_repo_path": "boostrap/BLogDeprecated.hh", "max_stars_repo_name": "hanswenzel/opticks", "max_stars_repo_head_hexsha": "b75b5929b6cf36a5eedeffb3031af2920f75f9f0", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
/******************************************************************************
Triangle class.
Copyright (c) 2010 - 2012
Alexander Rukletsov <rukletsov@gmail.com>
Dzmitry Hlindzich <dzmitry.hlindzich@ziti.uni-heidelberg.de>
All rights reserved.
Redistribution and use in source and binary forms, with o... | {"hexsha": "39d87ec40412b6caa144238577500d713da96a1c", "size": 4949, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Bo/core/triangle.hpp", "max_stars_repo_name": "rukletsov/bo", "max_stars_repo_head_hexsha": "bfece9e8f910b0c8f522733854405bf0a801b0e8", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count"... |
[STATEMENT]
lemma table_classes_SXcpt [simp]:
"table_of Classes (SXcpt xn)
= Some \<lparr>access=Public,cfields=[],methods=SXcpt_mdecls,
init=Skip,
super=if xn = Throwable then Object else SXcpt Throwable,
superIfs=[]\<rparr>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1.... | {"llama_tokens": 5337, "file": null, "length": 4} |
import Assignment1Support
import EvaluationsStub
import BagOfWords
import AddNoise
import collections
import operator
import numpy as np
### UPDATE this path for your environment
kDataPath = "..\\Data\\SMSSpamCollection"
(xRaw, yRaw) = Assignment1Support.LoadRawData(kDataPath)
(xTrainRawOriginal, yTrainRawOriginal,... | {"hexsha": "18a76ab9d13b5ec0bc6493ade9a4429c899145ec", "size": 3422, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/StartingPoint2.py", "max_stars_repo_name": "isibord/DecisionTree", "max_stars_repo_head_hexsha": "8e3336e9c7ec60ebfcecd02a1b7d7ae5dd6f7054", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# /*
# * @Author: dorming
# * @Date: 2021-01-14 15:21:38
# * @Last Modified by: dorming
# * @Last Modified time: 2021-01-14 15:21:38
# */
import numpy as np
class B(object):
def __init__(self, *args, **kwargs):
self.a = 1
self.b = 2
print(self)
print("init", args, kwargs)... | {"hexsha": "a32b73223a9cac93d880eb5bad6d13778b902427", "size": 774, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/augmentation/test_case.py", "max_stars_repo_name": "zongdaoming/CMT", "max_stars_repo_head_hexsha": "fc3773bb6c6b1ab091688addfffca3fb1e382ae4", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""Wigner thermal conductivity base class."""
# Copyright (C) 2022 Michele Simoncelli
# All rights reserved.
#
# This file is part of phono3py.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of ... | {"hexsha": "a6d754d02afaaec9e56e7b1525835ec99305736d", "size": 8628, "ext": "py", "lang": "Python", "max_stars_repo_path": "phono3py/conductivity/wigner.py", "max_stars_repo_name": "MSimoncelli/phono3py", "max_stars_repo_head_hexsha": "b28b45a025c279833e9269e5d91330c75d3f6ae0", "max_stars_repo_licenses": ["BSD-3-Clause... |
import numpy as np
from bokeh.document import Document
from bokeh.models import ColumnDataSource, Range1d, Plot, LinearAxis, Grid
from bokeh.models.glyphs import ImageURL
from bokeh.plotting import show
url = "http://bokeh.pydata.org/en/latest/_static/images/logo.png"
N = 5
source = ColumnDataSource(dict(
url = ... | {"hexsha": "136a741c2b79528c4f9addc48108ff5d0c142206", "size": 1381, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/glyphs/ImageURL.py", "max_stars_repo_name": "andreagrant/bokehDev", "max_stars_repo_head_hexsha": "a684afee183496c54d4f187a890707cf6b5ec2a5", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
export saveRecoParams, loadRecoParams, defaultRecoParams, defaultOnlineRecoParams
function defaultRecoParams()
params = Dict{Symbol,Any}()
params[:lambd] = 1e-2
params[:iterations] = 4
params[:SNRThresh] = 2.0
params[:minFreq] = 80e3
params[:maxFreq] = 1.25e6
params[:sortBySNR] = false
params[:nAverage... | {"hexsha": "461410cfba4c2050efdbae382b30d1d8ffdcd41a", "size": 3258, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/RecoParameters.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/MPIReco.jl-e4246700-6248-511e-8146-a1d1f47669d2", "max_stars_repo_head_hexsha": "1c7ef4519acb88fd9ba0089515ca8b2f78b05... |
#pragma once
#include <cstring>
#include <limits>
#include <map>
#include <memory>
#include <stdexcept>
#include <vector>
#include <Eigen/Geometry>
#if VOXELIZED_GEOMETRY_TOOLS__SUPPORTED_ROS_VERSION == 2
#include <sensor_msgs/msg/point_cloud2.hpp>
#elif VOXELIZED_GEOMETRY_TOOLS__SUPPORTED_ROS_VERSION == 1
#include <... | {"hexsha": "7df1d04a9592b63eff9cb7ed039cc621b8d43d4c", "size": 3659, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/voxelized_geometry_tools/pointcloud_voxelization_ros_interface.hpp", "max_stars_repo_name": "calderpg/voxelized_geometry_tools", "max_stars_repo_head_hexsha": "cc36bfd426e984e451e5b844f89be8... |
library(ggplot2)
args <- commandArgs(trailingOnly=TRUE)
if (length(args) <= 13) {
print(args)
stop("Usage: evaluator_bars_per_group_strategies.r common.r input.csv output.pdf granularity groupName evaluator1 unit1 quantity1 indication1 evaluator2 unit2 quantity2 indication2")
}
common <- args[1]
inFile <- args[2]... | {"hexsha": "49ece405c4e4d0f5b44c384054d1e0576e3c56ba", "size": 1468, "ext": "r", "lang": "R", "max_stars_repo_path": "easyspec-evaluate/rscripts/evaluator_bars_per_group_strategies_on_demand.r", "max_stars_repo_name": "NorfairKing/easyspec", "max_stars_repo_head_hexsha": "b038b45a375cc0bed2b00c255b508bc06419c986", "max... |
#!/usr/bin/env python3
import sys
from os.path import expanduser
import pudb
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from mpl_toolkits.mplot3d import Axes3D
# Look for modules in top level of AstroLib
sys.path.insert(0, expanduser("~/AstroLib/python"))
from orbits... | {"hexsha": "c4fa4d7b58ea2bf344b5cc6b16464eb10b3b274a", "size": 1886, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/demos/kepler_uv_plot.py", "max_stars_repo_name": "yookiwooki/AstroLib", "max_stars_repo_head_hexsha": "4598be425e837ea6b216d4f0d09e789aa54d9368", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from numpy.testing import assert_almost_equal
from mmpose.models import build_loss
from mmpose.models.utils.geometry import batch_rodrigues
def test_mesh_loss():
"""test mesh loss."""
loss_cfg = dict(
type='MeshLoss',
... | {"hexsha": "98907675d26bfe65790edfc2bde7b8179aee4ad8", "size": 5793, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_losses/test_mesh_losses.py", "max_stars_repo_name": "nightfuryyy/mmpose", "max_stars_repo_head_hexsha": "910d9e31dd9d46e3329be1b7567e6309d70ab64c", "max_stars_repo_licenses": ["Apache-2... |
import os
from os.path import join as pjoin
import numpy as np
import pandas as pd
import scipy.stats
import dask
from cesium import featurize
from cesium.tests.fixtures import (sample_values, sample_ts_files,
sample_featureset)
import numpy.testing as npt
import pytest
DATA_PATH ... | {"hexsha": "c90b4a53bfe359f73d49c6c5e81119bccac0fb54", "size": 12579, "ext": "py", "lang": "Python", "max_stars_repo_path": "cesium/tests/test_featurize.py", "max_stars_repo_name": "acrellin/cesium", "max_stars_repo_head_hexsha": "9d33edc0f9b3a79c68070826c0f390896abe294d", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
"""
An [`AbstractConstraintSet`](@ref) that stores the constraint values as well as Lagrange
multiplier and penalty terms for each constraint.
The cost associated with constraint terms in the augmented Lagrangian can be evaluated for
cost!(J::Vector, ::ALConstraintSet)
which adds the cost at each time step to the ... | {"hexsha": "643e71fd928205f5b9cba629865d00afc500d9b1", "size": 8125, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ALconset.jl", "max_stars_repo_name": "serenity4/TrajectoryOptimization.jl", "max_stars_repo_head_hexsha": "5584984cd472d5ceb6634c032b8b572e57754084", "max_stars_repo_licenses": ["MIT"], "max_st... |
import aoc_utils
import itertools
import functools
import operator
import networkx
import math
from collections import *
from copy import deepcopy
import random
import re
lines = aoc_utils.readlines()
def isvalid(string):
stack = []
for x in string:
if x == "{" or x == "[" or x == "(" or x == "<":
... | {"hexsha": "be90ea942b501a20e842e4a783579fe27c5ac204", "size": 2100, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/Python21/10.py", "max_stars_repo_name": "sapieninja/AdventOfCode", "max_stars_repo_head_hexsha": "8190c11e3eb2e4292a0cf66a6ef9261dee880f2e", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from scipy import stats
from skimage import img_as_ubyte
from skimage.feature import local_binary_pattern
from skimage.io import imread
import glob
import keras_NN
import numpy as np
import os
import pandas as pd
import time
# Define the global variables related to the dataset
DATASET_PATH = "./input"
TRAINING_FOLDER_... | {"hexsha": "29b114c4f6cf5afbf01bd345eed3faaad886e6f0", "size": 5031, "ext": "py", "lang": "Python", "max_stars_repo_path": "Copper Analysis/solution.py", "max_stars_repo_name": "nixingyang/Kaggle-Face-Verification", "max_stars_repo_head_hexsha": "b5f9908f4c23dc78b3e6b647c7add8f2b0d84663", "max_stars_repo_licenses": ["M... |
program tarefaa
print *, "Digite o valor inteiro de N:"
read (*,*) N
! Número de "Andarilhos" M
M = 1000
! loop do passo
do i = 1, N
! zera a soma
soma = 0e0
! loop do andarilho
do j = 1, M
! soma os passos de cada andarilho
... | {"hexsha": "2c438594106da63b7e4beac62887861bf9268241", "size": 500, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "projeto-2/tarefa-a/tarefa-a-10407962.f90", "max_stars_repo_name": "ArexPrestes/introducao-fisica-computacional", "max_stars_repo_head_hexsha": "bf6e7a0134c11ddbaf9125c42eb0982250f970d9", "max_sta... |
"""
If you use this code, please cite one of the SynthSeg papers:
https://github.com/BBillot/SynthSeg/blob/master/bibtex.bib
Copyright 2020 Benjamin Billot
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 Lice... | {"hexsha": "bc8adc822e5b8227d6952e674d42df447e4fd832", "size": 28973, "ext": "py", "lang": "Python", "max_stars_repo_path": "SynthSeg/predict.py", "max_stars_repo_name": "a-parida12/SynthSeg", "max_stars_repo_head_hexsha": "fc37820826f13e39603e96e532bdbdd409b51774", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
\documentclass[11 pt]{scrartcl}
\usepackage[header, margin, koma]{tyler}
\newcommand{\hwtitle}{Discussion 4B Recap}
\pagestyle{fancy}
\fancyhf{}
\fancyhead[l]{\hwtitle{}}
\fancyhead[r]{Tyler Zhu}
\cfoot{\thepage}
\begin{document}
\title{\Large \hwtitle{}}
\author{\large Tyler Zhu}
\date{\large\today}
\maketitle
... | {"hexsha": "21dcab41f768a32c3057ff0a1958bbcbb4a66b61", "size": 1925, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "CS70/recap4b/recap4b.tex", "max_stars_repo_name": "cbugwadia32/course-notes", "max_stars_repo_head_hexsha": "cc269a2606bab22a5c9b8f1af23f360fa291c583", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
from scipy.signal import butter, lfilter
#from control import matlab
def decimate(data,fs_befor,fs_after):
from scipy.signal import decimate
if fs_after<=8:
data_ = decimate(data,int(fs_befor/8),ftype='iir')
data_ = de... | {"hexsha": "a795afb74e37c52933d63e6be1aca47668ca406c", "size": 3101, "ext": "py", "lang": "Python", "max_stars_repo_path": "miyopy/signal/filt.py", "max_stars_repo_name": "MiyoKouseki/miyopy", "max_stars_repo_head_hexsha": "0f2da1a99f656259b556a9aac892483b44d17112", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 13 10:06:46 2019
@author: mikael
"""
import struct
import math as m
import numpy as np
def to_float80(x):
sign = 0
exponent = 0
if (x < 0):
sign = 0x8000
x = -x
mantisse_h = 0
mantisse_l = 0
mantisse = ... | {"hexsha": "e27cfd31e4b793e938f192855bc80c8494f22891", "size": 4017, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/audiofeel/io/aiff/writer.py", "max_stars_repo_name": "mrAgan/audiofeel-player", "max_stars_repo_head_hexsha": "39a1ea63808754a61590a72373b04a53a2331fbb", "max_stars_repo_licenses": ["MIT... |
#!/usr/bin/env python
import rospy
from sensor_msgs.msg import Image
import numpy as np
import sys
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
import cv2
sys.path.append('/opt/ros/kinetic/lib/python2.7/dist-packages')
import rospy
from std_msgs.msg import String
from sensor_msgs.msg import Image
fr... | {"hexsha": "44c7b385d308c607992de0178ce33c2dd21de11a", "size": 1704, "ext": "py", "lang": "Python", "max_stars_repo_path": "darknet_ros/scripts/publishKittiImage.py", "max_stars_repo_name": "mrinalsenapati04/darknet_ros", "max_stars_repo_head_hexsha": "238c5dce25af9c607e73a59aa588c73343b99739", "max_stars_repo_licenses... |
# -*- coding: utf-8 -*-
import os
import time
import numpy as np
import pandas as pd
import scanpy as sc
import scipy.sparse as ssp
from cospar.tmap import _tmap_core as tmap_core
from cospar.tmap import _utils as tmap_util
from .. import help_functions as hf
from .. import logging as logg
from .. import settings
f... | {"hexsha": "7bedaa6d09c058a4cfa7c7c81903434b417d19a2", "size": 51743, "ext": "py", "lang": "Python", "max_stars_repo_path": "cospar/tmap/map_reconstruction.py", "max_stars_repo_name": "AllonKleinLab/cospar", "max_stars_repo_head_hexsha": "6d2028717a048db7ad79b0cdb6f25910b6901eec", "max_stars_repo_licenses": ["MIT"], "m... |
SUBROUTINE HOPEN ( iret )
C************************************************************************
C* HOPEN - TIFF *
C* *
C* This subroutine opens a plot file for the device. *
C* *
C* HOPEN ( IRET ) *
C* *
C* Output parameters: *
C* IRET INTEGER Return code *
C** ... | {"hexsha": "fb3defe0a89c9b05ba301b3302e8835f2fed2320", "size": 563, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/driver/active/tiff/hopen.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clau... |
"""This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Line class which lets you build your Line charts just
passing the arguments to the Chart class and calling the proper functions.
"""
#-----------------------------------------------------------------... | {"hexsha": "92a953032bff298260ec529bd6b4bc5d47b2f360", "size": 7800, "ext": "py", "lang": "Python", "max_stars_repo_path": "bokeh/charts/line.py", "max_stars_repo_name": "brian15co/bokeh", "max_stars_repo_head_hexsha": "6cecb7211277b9d838039d0eb15e50a10f9ac3d1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
[STATEMENT]
lemma [iff]: "P,E,h \<turnstile> e\<^sub>1;;e\<^sub>2 :' T\<^sub>2 = (\<exists>T\<^sub>1. P,E,h \<turnstile> e\<^sub>1:' T\<^sub>1 \<and> P,E,h \<turnstile> e\<^sub>2:' T\<^sub>2)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (P,E,h \<turnstile> e\<^sub>1;; e\<^sub>2 :' T\<^sub>2) = (\<exists>T\<^sub>1... | {"llama_tokens": 489, "file": "CoreC++_Progress", "length": 3} |
import cv2
import imutils
import numpy as np
cam = cv2.VideoCapture(0)
cv2.namedWindow("test")
img_counter = 0
ret, frame = cam.read()
# cv2.imshow("test", frame)
k = cv2.waitKey(1)
# hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# show image
blueLower = (98, 109, 20)
blueUpper = (112, 255, 255)
frame = imutils.re... | {"hexsha": "ea7363e724e238956bd7dd5383b990cce8a7f77a", "size": 1451, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "akilawickey/irobot", "max_stars_repo_head_hexsha": "cec600889a7244ed047b25e7b88d4c846a1681a6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may... | {"hexsha": "ef79140edc8e0fad1c78b2452a8f0cdf0386d872", "size": 6730, "ext": "py", "lang": "Python", "max_stars_repo_path": "Model/predictor-dl-model/tests/trainer/api_test/client_rest_api_test.py", "max_stars_repo_name": "rangaswamymr/incubator-bluemarlin", "max_stars_repo_head_hexsha": "6cb60b2a41edc6509377f9eacb7660d... |
'''
Show all different interpolation methods for imshow
'''
import matplotlib.pyplot as plt
import numpy as np
# from the docs:
# If interpolation is None, default to rc image.interpolation. See also
# the filternorm and filterrad parameters. If interpolation is 'none', then
# no interpolation is performed on the Ag... | {"hexsha": "4977368b49ae3b1f297f48713f9c211432eca868", "size": 1052, "ext": "py", "lang": "Python", "max_stars_repo_path": "matplotlib_examples/examples_src/images_contours_and_fields/interpolation_methods.py", "max_stars_repo_name": "xzlmark/webspider", "max_stars_repo_head_hexsha": "133c620c65aa45abea1718b0dada09618c... |
# coding:utf-8
"""
create Wangmeng Song
July 4,2017
overwrite by Wangmeng Song
July 17,2017
修改固定上车时间
July 20,2017
"""
import shapefile as sf
from shapely.geometry import Polygon, Point, LinearRing
import os
import datetime
import numpy as np
import inspect
import copy
import json
import requests
PICKTIME = 3
DIFDURA... | {"hexsha": "515d928a2d7a60fae4cf461a0c9ed241dbb67af4", "size": 9004, "ext": "py", "lang": "Python", "max_stars_repo_path": "recomTimeOnTheBus/recommendtime.py", "max_stars_repo_name": "hellodu-dev/team_schedule", "max_stars_repo_head_hexsha": "6239a6798f337f2cf5e88277d175143519045d86", "max_stars_repo_licenses": ["Apac... |
import numpy as np
class Hessian():
def __init__(self, f):
self.value = f.Real.Real.Real
fd1 = f.Dual[0].Real.Real[0].Real.Real
fd2 = f.Dual[1].Real.Real[1].Real.Real
self.firstDer = np.array([fd1,fd2])
hxx = f.Dual[0].Dual[0].Real[0].Real
hyy = f.Dual[0].Real.Real[1].Dual[0].Real[1].Real
hxy = f.Du... | {"hexsha": "5fe2965ddd1626c451aeeb254c3cc019f2d7c230", "size": 514, "ext": "py", "lang": "Python", "max_stars_repo_path": "ADPYNE/Hessian.py", "max_stars_repo_name": "PYNE-AD/cs207-FinalProject", "max_stars_repo_head_hexsha": "7b146da3ebb4747ce213bf0537af3c385689ecc1", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import time
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
import argparse
from PIL import Image
from scipy import ndimage
from dnn_utils import load_data, initialize_parameters_deep, L_model_forward, \
compute_cost, L_model_backward, update_parameters, predict, print_mislabeled_images... | {"hexsha": "8b0bea1c556bdf9bccc14874a6f68a2bab6da3de", "size": 6092, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "santamm/DeepNet", "max_stars_repo_head_hexsha": "fd05804200eb1bd62fb3a80a793b22794e4ec7d2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
% -*- root: ../gvoysey-thesis.tex -*-
\chapter{Introduction}
\label{chapter:Introduction}
\thispagestyle{myheadings}
\section{Motivation}
The variability of overall performance between putatively normal hearing listeners, particularly in supra-threshold tasks performed in complex acoustic environments such as the cockt... | {"hexsha": "55c3c0e1554ce8aee1ad6b442900eb37c6d178a8", "size": 5748, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "text/1_Intro/intro.tex", "max_stars_repo_name": "gvoysey/thesis", "max_stars_repo_head_hexsha": "766ed365f55ada08c3b6f548a6f857f9d3e49b91", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count... |
[STATEMENT]
lemma ack_3: "ack (Suc (Suc (Suc 0))) j = 2 ^ (j+3) - 3"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ack (Suc (Suc (Suc 0))) j = 2 ^ (j + 3) - 3
[PROOF STEP]
proof (induct j)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. ack (Suc (Suc (Suc 0))) 0 = 2 ^ (0 + 3) - 3
2. \<And>j. ack (Suc (Suc (Suc ... | {"llama_tokens": 1170, "file": "Ackermanns_not_PR_Primrec", "length": 11} |
using AstroUtils
using Test, SafeTestsets
@time begin
@time @safetestset "cartToKep tests..." begin include("cartToKepTests.jl") end
end | {"hexsha": "ec18fce86bdd2198ad485268b4f86f4def58db58", "size": 137, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "GrantHecht/AstroUtils.jl", "max_stars_repo_head_hexsha": "40227897cc6030fa6ab505b805c7f72d1322f2b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 9 10:38:44 2020
@author: jsalm
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
# from sklearn import svm, datasets
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy.ndimage import convolve,d... | {"hexsha": "edb814adb76721168a69f55d93523cff2d35e530", "size": 17654, "ext": "py", "lang": "Python", "max_stars_repo_path": "SVM.py", "max_stars_repo_name": "eduluca/Generalized-Sklearn-ML-Pipeline", "max_stars_repo_head_hexsha": "75a3be16ca229ffe7712266cb9c1c50469ccd25d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
// version 02: first impl to sort large files.
// sort and merge
// 30% faster than sort(1) for all 1GB 5GB 10GB files
#include <boost/noncopyable.hpp>
#include <boost/ptr_container/ptr_vector.hpp>
#include <datetime/Timestamp.h>
#include <algorithm>
#include <string>
#include <ext/vstring.h>
#include <vector>
#inc... | {"hexsha": "48c00ec8332121a433df0ce80873d6e174729f13", "size": 5956, "ext": "cc", "lang": "C++", "max_stars_repo_path": "esort/sort02.cc", "max_stars_repo_name": "ririripley/recipes", "max_stars_repo_head_hexsha": "04267c68a7424326b4aa8dd14b1a879b59ab887c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
theory Ex2_1
imports Main
begin
datatype 'a tree = Leaf 'a | Branch 'a "'a tree" "'a tree"
primrec preOrder :: "'a tree \<Rightarrow> 'a list" where
"preOrder (Leaf val) = [val]"|
"preOrder (Branch val lft rgt) = val # preOrder lft @ preOrder rgt"
primrec postOrder :: "'a tree \<Rightarrow> 'a... | {"author": "SvenWille", "repo": "ExerciseSolutions", "sha": "1a71e30f3369d34c4691a4d010257b8c8afc566c", "save_path": "github-repos/isabelle/SvenWille-ExerciseSolutions", "path": "github-repos/isabelle/SvenWille-ExerciseSolutions/ExerciseSolutions-1a71e30f3369d34c4691a4d010257b8c8afc566c/src/isabelle/Trees and other ind... |
from sklearn.cluster import KMeans
import numpy as np
from classes import *
from treelib import *
from math import *
def hi_kmeans(_first_node, _des_database_list, _b, _depth, _n_documents):
descriptors = [] # putting in a list the descriptor 128 vectors
for i in range(len(_des_database_list)):
desc... | {"hexsha": "a4fd3fb7cd26ea108ded992ea91fd6d22ae01c2c", "size": 2697, "ext": "py", "lang": "Python", "max_stars_repo_path": "hi_k_means.py", "max_stars_repo_name": "favia96/Visual-Search-System", "max_stars_repo_head_hexsha": "06b3188062aabb4602ca4f2546897a19fc987a4a", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
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