text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh import mpl
from bokeh.plotting import show
# We generated random data
data = 1 + np.random.randn(20, 6)
# And then just call the violinplot from Seaborn
sns.violinplot(data, color="Set3")
plt.title("Seaborn violin plot in bokeh.")
s... | {"hexsha": "bc138ffd8c9a65c021e4aea619087e4c5e498a5f", "size": 353, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/compat/seaborn/violin.py", "max_stars_repo_name": "timelyportfolio/bokeh", "max_stars_repo_head_hexsha": "a976a85535cf137c6238ce9e90b41ab14ae8ce22", "max_stars_repo_licenses": ["BSD-3-Clau... |
// The template and inlines for the -*- C++ -*- rational number classes.
// Initially implemented by Wai-Shing Luk <luk036@gmail.com>
//
/** @file include/rational.hpp
* This is a C++ Library header.
*/
#ifndef FUN_RATIONAL_HPP
#define FUN_RATIONAL_HPP 1
#include <cassert>
#include <type_traits> // is_integral<T>... | {"hexsha": "55642e2519886ad0fd5489ec8e67271b5414f54c", "size": 8699, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/include/fun/rational.hpp", "max_stars_repo_name": "luk036/fun", "max_stars_repo_head_hexsha": "ac3896eb8741767324d6b400d38573a66f0917b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import os
import numpy as np
try:
import numba
HAS_NUMBA = True
except ImportError:
HAS_NUMBA = False
List = list
Dict = dict
def set_list_type_for_jit():
global List, Dict
List = numba.typed.List
Dict = numba.typed.Dict
return
def create_nb_List(py_list):
nb_List = List()
if... | {"hexsha": "3d690df8a865a976e74688ab5355cb97930b98b6", "size": 4477, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyequion/utils_for_numba.py", "max_stars_repo_name": "caiofcm/pyequion", "max_stars_repo_head_hexsha": "762ce1fb68cbbf35e52f7d4db2c34bd29f1dd18c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
library(tidyverse)
library(doParallel)
library(randomForest)
library(ggthemes)
library(data.table)
setwd('your_working_directory')
# Load dataset
df <- fread("file_name.csv") %>%
dplyr::select(-axiv_index_b, -axiv_index_t)
df$transition_noTransition <- as.factor(df$transition_noTransition)
# Se... | {"hexsha": "e55ecbbb7311d424a103026774e43d43218f8707", "size": 2180, "ext": "r", "lang": "R", "max_stars_repo_path": "2_classifier_creation.r", "max_stars_repo_name": "neebul/activityShiftDetector", "max_stars_repo_head_hexsha": "5a9ffa03e8dcbd17e207f0dc657179f338665154", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# python imports
import os, shutil
from string import Template
from math import log10
import subprocess
import time
# global library imports
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# local imports
from krg_utils import *
from utils import plot_2d_image
from tinti import tinti
from srf i... | {"hexsha": "3151c83f417f75f1f8f5991374b08b7b871064e1", "size": 22516, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_scale_truncated_resample.py", "max_stars_repo_name": "longyearxuk/sokrg", "max_stars_repo_head_hexsha": "001fcf8275eb158765de4e99e0d442b1712aa061", "max_stars_repo_licenses": ["BSD-3-Cla... |
-- Andreas, 2018-05-09, issue 2636, reported by nad
-- {-# OPTIONS -v tc.pos:10 #-}
id : (A : Set₁) → A → A
id A x = x
A : Set₁
A = Set
where
F : Set₁ → Set₁
F X = X
data D : Set₁ where
c : F D → D
lemma : F (D → Set) → D → Set
lemma fp d = id (F (D → Set)) fp d
-- Problem was:
-- Positivity check... | {"hexsha": "43d754f32d616b014889d1742f3524a0e80f325a", "size": 364, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Bugs/Issue2636.agda", "max_stars_repo_name": "cruhland/agda", "max_stars_repo_head_hexsha": "7f58030124fa99dfbf8db376659416f3ad8384de", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import csv
from iacorpus import load_dataset
from gensim import corpora, models
import numpy as np
import pandas as pd
from sqlalchemy import Table, Column, Integer, sql
from sklearn.model_selection import train_test_split
# read data
dataset = load_dataset('fourforums', host='localhost', port='3306', username='root'... | {"hexsha": "298d91a4f677ae2844cfc2018402f29caffaa743", "size": 4984, "ext": "py", "lang": "Python", "max_stars_repo_path": "psldatagen.py", "max_stars_repo_name": "eosW/CMPS290C", "max_stars_repo_head_hexsha": "c47fcf4469445256975c43e99a15bcde61153da7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
import numpy as np
import torch
from mlp import MLP
from torch import optim
from utils import cosine_distance_torch
# https://github.com/kimiandj/gsw
class GSW_NN:
def __init__(self, din=2, nofprojections=10, model_depth=3, num_filters=32, use_cuda=True):
self.nofprojections = nofprojections
if ... | {"hexsha": "144e0c6166675870a2184ca8da611e16859dc4be", "size": 3304, "ext": "py", "lang": "Python", "max_stars_repo_path": "gswnn.py", "max_stars_repo_name": "ttaa9/DSW", "max_stars_repo_head_hexsha": "ca29d425d4a535f53e70a7a45ebd13a7b196f8c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 32, "max_stars_repo_... |
#!/usr/bin/env python
import rospy
import rospkg
from generation import Generation
import random
import matplotlib.pyplot as plt
import datetime
import time
import numpy as np
import os
import copy
from annealing import annealing
def get_or_error(string):
if rospy.has_param(string):
return rospy.get_param(... | {"hexsha": "fd8fef2c5de1f92f757bc0cf5f715e81523eea28", "size": 11735, "ext": "py", "lang": "Python", "max_stars_repo_path": "position_optimizer/script/genetic_alg.py", "max_stars_repo_name": "CNR-STIIMA-IRAS/position_optimizer", "max_stars_repo_head_hexsha": "b246cd02de6e3f3d1098eb4f7171f5a29b6b4f36", "max_stars_repo_l... |
#include <boost/mpl/aux_/common_name_wknd.hpp>
| {"hexsha": "c1c608eb40904a2fcf70ab3b0c9e805ba3125bff", "size": 47, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_mpl_aux__common_name_wknd.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL... |
from functools import reduce
import logging
import numpy as np
def prune_sum_eq_len(domain):
""" Prune if sum(val) or sum(pos * val) can't equal length """
min_sum, max_sum = domain.estimate("sum")
min_mult, max_mult = domain.estimate("mult")
constraints = [
min_sum > domain.length,
ma... | {"hexsha": "618b95091af2636414fab559e5d29123e1c26eb9", "size": 3207, "ext": "py", "lang": "Python", "max_stars_repo_path": "matrices/prune.py", "max_stars_repo_name": "Kopytok/magic_series", "max_stars_repo_head_hexsha": "679ee7d52c93871cbdf3c499656892a18c36ad2d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from __future__ import print_function
import os
import sys
import random
from time import strftime, gmtime, time
from report_result import ReportResult
from configuration import Conf
from archive_results import ArchiveResults
import argparse
import shutil
import pickle
import json
from keras.preprocessing.text imp... | {"hexsha": "fee715223af227da11653dfca410100964d206df", "size": 12345, "ext": "py", "lang": "Python", "max_stars_repo_path": "stack_over_flow_qa_eval.py", "max_stars_repo_name": "mrezende/keras-language-modeling", "max_stars_repo_head_hexsha": "4cbfc161d55c5b903e667eedb476f9c8f1473894", "max_stars_repo_licenses": ["MIT"... |
extract.controls <- function(rg, probes, verbose=F) {
stopifnot(is.rg(rg))
x.mean <- function(x, na.rm=T) {
if (length(x) <= 1)
stop("It seems that the IDAT files do not match the supplied chip annotation.")
mean(x,na.rm=na.rm)
}
x.which <- function(x) {
i <- which(x... | {"hexsha": "234f8de084db7a0811c171d754d2be3bb980441a", "size": 4512, "ext": "r", "lang": "R", "max_stars_repo_path": "R/extract-controls.r", "max_stars_repo_name": "RichardJActon/meffil", "max_stars_repo_head_hexsha": "8cb1d18fb1f5e350a6774116c5b9571fed1c5067", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_co... |
import glob
import matplotlib.pyplot as plt
plt.rc("font", family="serif")
plt.rc("text", usetex=True)
import numpy as np
from astropy.table import Table
from astropy.cosmology import Planck15
def plot_lc(f, name=None):
dt = []
lum = []
with open(f, "r") as inputf:
for line in inputf.readlines():
... | {"hexsha": "65e1ea93cc1010b22d6b6fbb8ec8525dcb2ba477", "size": 4587, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/extra_plots/xray_lc.py", "max_stars_repo_name": "annayqho/SN2018gep", "max_stars_repo_head_hexsha": "93cd64a1aab326771199f9093339df5bc4eb8002", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#include <boost/serialization/ephemeral.hpp>
| {"hexsha": "89d0a3de9a783cd7a96fdf523f9950fbe5f45ffd", "size": 45, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_serialization_ephemeral.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1... |
\documentclass[preprint]{sigplanconf}
% The following \documentclass options may be useful:
% preprint Remove this option only once the paper is in final form.
% 10pt To set in 10-point type instead of 9-point.
% 11pt To set in 11-point type instead of 9-point.
% numbers To obtain numeric... | {"hexsha": "5558e33c1f3c87fc79316fe203eb175451495245", "size": 16532, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "writeup/thesis.tex", "max_stars_repo_name": "benjaminy/TenaciousCalf", "max_stars_repo_head_hexsha": "b90c002ce16fee579b04f5c9b92d1cc4b4f96b94", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
"""Test the percentage column difference transformer."""
import numpy as np
import numpy.testing as nt
import pandas as pd
import pandas.testing as pt
import pytest
import src.preprocessing as pp
@pytest.fixture
def data():
data = {
'f1': np.array([100, 110, 98, 1500, 30]),
'f2': 100 * np.ones((... | {"hexsha": "bcf40bba9f0a8476c7a6a0fc593404f84de2b4c6", "size": 1477, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_two_col_percent_diff_transformer.py", "max_stars_repo_name": "PieCampi/dl-toolkit", "max_stars_repo_head_hexsha": "6d212f22ed97af9b9e59b6c2e77198e472c3f628", "max_stars_repo_licenses": ... |
# -*- coding: utf-8 -*-
"""
cloud_att_intermediate_values.py
Created on Tue Jun 30 8:53:09 2020
Determined the error between the published L_red value in the ITU-R validation data,
sheet P840-8 Lred, and the calculated L_red value using iturpy
@author: MAW32652
"""
import itur
from itur.models.itu840 import columnar... | {"hexsha": "91c6b5f042e1cfdf8f5dc0f63a692da867bebea2", "size": 6769, "ext": "py", "lang": "Python", "max_stars_repo_path": "itur/validation/validation_scripts/cloud_att_intermediate_values.py", "max_stars_repo_name": "the-aerospace-corporation/ITU-Rpy", "max_stars_repo_head_hexsha": "4456da2db9f28453d5a08339c84fe5bf25b... |
# """
# idris *.idr -o out.qb --codegen qb --cg-opt "--javaName" --cg-opt "--symemu"
#
# Requirements:
# - (v1.3) pkg> add ArgParse
# - (v1.3) pkg> add MLStyle
using MLStyle
using ArgParse
literal_map(kind, x) =
@match String(kind) begin
"float" => parse(Float64, x)
"int" => parse(Int64... | {"hexsha": "f5526d04df765d4483988a4444b3f7ca04a4c2ac", "size": 3963, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "backend.jl", "max_stars_repo_name": "thautwarm/PPL2020-quick-and-reusable-code-generation-for-idris", "max_stars_repo_head_hexsha": "a532c6c47f4f0faaf7588bf74776a0d9835d2ba5", "max_stars_repo_licen... |
import argparse
import os
from typing import Generator
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from .grammar import q_learner
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def main(... | {"hexsha": "28d6cc87784b5b3cc1a8791cc873030c99efa826", "size": 5930, "ext": "py", "lang": "Python", "max_stars_repo_path": "countermeasures/display_results.py", "max_stars_repo_name": "AISyLab/RL-based-countermeasure-design-for-SCA", "max_stars_repo_head_hexsha": "f03895b4b13b0397f0cc7014d9e7d2738ff2a6a2", "max_stars_r... |
from base import StructuredModel
import numpy as np
import sys
import heapq
import time
import random
import math
import multiprocessing
import copy
class Utils(object):
def greeting(name):
print("Hello, " + name)
def getData(self,path, Num):
file1 = open(path, 'r')
lineNum = 1
... | {"hexsha": "6d1aebda74ebfcd2a54d17bbc3f5756c528ca372", "size": 47096, "ext": "py", "lang": "Python", "max_stars_repo_path": "StratLearner/stratLearner.py", "max_stars_repo_name": "cdslabamotong/stratLearner", "max_stars_repo_head_hexsha": "58f278d438eed92683a7daac2605ec39abd18c94", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 23 07:02:58 2020
@author: Sourabh Bhat ( https://spbhat.in/ )
"""
import numpy as np
import scipy.special
# Neural network class definition
class NeuralNetwork:
# initialize the neural network
def __init__(self, numInputNodes, numHidd... | {"hexsha": "1f4fb29a577be900f7e9bd1529fec28472ef3ec9", "size": 3467, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/python/nn/NeuralNetwork.py", "max_stars_repo_name": "heySourabh/NeuralNetwork", "max_stars_repo_head_hexsha": "aafc7081ce9db9a5422b418e9a1586d3b3df041f", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
import Ray
array = np.array([
[
[ 0, 1, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[-2, 0,-2, 0],
],
])
oldarray = np.array([
[
[ 0, 0, 0, 0],
[ 0, 1, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
],
[
[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0, 0, 0, 0],
[ 0,... | {"hexsha": "09fe78eb24be0c093591ecb4b9e115c513c5af5d", "size": 941, "ext": "py", "lang": "Python", "max_stars_repo_path": "c++ test.py", "max_stars_repo_name": "guille0/space-chess", "max_stars_repo_head_hexsha": "3e8a3c8c8b91fbcbc00fbb4b35596a3b2ad1a37c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max... |
\documentclass{scarv-report}
\usepackage{scarvsoc}
\title{SCARV-SoC\\Technical Report and User Guide}
\date{Version $0.0.1$ (\today)}
\author{Ben Marshall}
\affil{
Department of Computer Science, University of Bristol,\\
Merchant Venturers Building, Woodland Road,\\
Bristol, BS8 1UB, United Kingdom.\\
\url{{ben.marsh... | {"hexsha": "35e804cd5eaccbf804ca41e9ceb1302f9de2029e", "size": 2680, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/scarvsoc.tex", "max_stars_repo_name": "scottwedge/scarv-soc", "max_stars_repo_head_hexsha": "6e29e7a103a0b2ac67deb2701044332917230c27", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#include <boost/multiprecision/cpp_dec_float.hpp>
#include <boost/multiprecision/cpp_int.hpp>
#include <boost/numeric/conversion/cast.hpp>
//typedef boost::multiprecision::cpp_dec_float_50 xmc_float;
typedef boost::multiprecision::number<boost::multiprecision::cpp_dec_float<64> > xmc_float;
typedef boost::multiprecisi... | {"hexsha": "29987c6ad2100a67b3e57a8eeb29b885fbc7ff8b", "size": 1608, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/libwalletqt/xmc_int_to_double.hpp", "max_stars_repo_name": "toints/monero-GUI", "max_stars_repo_head_hexsha": "ea29f0ae0e1bc9e00b8a9b69679e302513c3dfcd", "max_stars_repo_licenses": ["BSD-3-Claus... |
'''
Functions for preprocessing/transforming data between extraction from the database and input to the model.
'''
import numpy as np
import pandas as pd
import dataset
def encode_labels_str2int(data, y_col='family'):
'''
Create 'label' column in data_df that features integer values corresponding to text la... | {"hexsha": "2c9efd9c725bd0cb695340aa4906107920057cdd", "size": 3752, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyleaves/data_pipeline/.ipynb_checkpoints/preprocessing-checkpoint.py", "max_stars_repo_name": "JacobARose/pyleaves", "max_stars_repo_head_hexsha": "27b4016c850148981f3d021028c9272f18df121d", "max... |
import numpy as np
import matplotlib.pyplot as pl
import h5py
import platform
import os
import pickle
import seaborn as sns
import json
from ipdb import set_trace as stop
class plotDNN(object):
def __init__(self, root, noise):
self.root = root
self.noise = noise
self.dataFile = "... | {"hexsha": "72fd856b84384845e21adabc22688e3860c0c5e0", "size": 3422, "ext": "py", "lang": "Python", "max_stars_repo_path": "DNMilne/training/doPlot.py", "max_stars_repo_name": "aasensio/DeepLearning", "max_stars_repo_head_hexsha": "71838115ce93e0ca96c8314cff3f07de1d64c235", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 9 10:52:36 2015
Description:
@author: sacha gobeyn (sacha.gobeyn@ugent.be or sachagobeyn@gmail.com)
"""
import pandas as pd
import numpy as np
def load_and_preproces_data(inputdata,taxon,filter_parameters,variables,res,nan_value):
""" Load data and variables list
... | {"hexsha": "598ac050aa153d001fea7ecc3b3d4aab5d20fee0", "size": 5008, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/data_processing.py", "max_stars_repo_name": "Sachagobeyn/SDMIT", "max_stars_repo_head_hexsha": "86a5ca5f9e920528b967ac7976b6068f77c614bf", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma \<psi>_im : "\<psi> ` GRepHomSet (\<star>) W \<subseteq> HRepHomSet (\<star>) W"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<psi> ` GRepHomSet (\<star>) W \<subseteq> HRepHomSet (\<star>) W
[PROOF STEP]
using \<psi>T_W \<psi>T_hom FGModuleHomSetI
[PROOF STATE]
proof (prove)
using this:
?T \<in... | {"llama_tokens": 302, "file": "Rep_Fin_Groups_Rep_Fin_Groups", "length": 2} |
/*
* (C) Copyright 2015 ETH Zurich Systems Group (http://www.systems.ethz.ch/) and others.
*
* 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/LICENS... | {"hexsha": "acc62e20e610d117206e3d4066ee603c5d465987", "size": 18050, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "main.cpp", "max_stars_repo_name": "tellproject/ycsb-server", "max_stars_repo_head_hexsha": "8584d2b661221dbb172d267b664d1f7504d18c5c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
\documentclass{article}
\usepackage{graphicx}
\usepackage{titletoc}
\usepackage{titlesec}
\usepackage{geometry}
\usepackage{fontspec, xunicode, xltxtra}
\usepackage{float}
\usepackage{cite}
\usepackage{amsmath}
\usepackage{listings}
\usepackage{titletoc}
\usepackage{booktabs}
\geometry{left=3cm,right=3cm,top=3cm,bott... | {"hexsha": "7b6e6082fc5daab6bca2250cf4cbf3aa736e0c8c", "size": 5157, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "HW7/Homework7.tex", "max_stars_repo_name": "goldsail/BayesianHomework", "max_stars_repo_head_hexsha": "d5506faccbf4d0b7b696c7c2bcb42d020bb0d357", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import sys
from mnist import load_mnist
import numpy as np
(x_train, t_train), (x_test, t_test) = load_mnist(
normalize=True, one_hot_label=True)
print(x_train.shape)
# (60000, 784)
print(t_train.shape)
# (60000, 10)
train_size = x_train.shape[0]
batch_size = 10
batch_mask = np.random.choice(train_size... | {"hexsha": "9c732c3b61d69049458d772f5b74c82a0d12cca8", "size": 493, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/Chapter3_demo/4.py", "max_stars_repo_name": "Gedanke/Getting_started_with_deep_learning", "max_stars_repo_head_hexsha": "18636fb8aaca83f2157c08815e9c1f0b0f55d91a", "max_stars_repo_licenses": [... |
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivat... | {"hexsha": "3f66800198892ea277dc870f00921ad7896990cc", "size": 2923, "ext": "py", "lang": "Python", "max_stars_repo_path": "qiskit/optimization/applications/ising/stable_set.py", "max_stars_repo_name": "johannes-weidenfeller/qiskit-aqua", "max_stars_repo_head_hexsha": "7775410f4dabf09ec9f933bf411ead434550accf", "max_st... |
macro linklibrary_modes()
path = normpath(Pkg.dir("FastSigmoid"),"c-src","libfastposit.so")
esc(quote
set_nanmode = () -> ccall( (:set_nanmode, $path), Void, (Bool,), true )
set_infmode = () -> ccall( (:set_nanmode, $path), Void, (Bool,), false )
set_roundstozero = () -> ccall( (:set_underflow, $path... | {"hexsha": "e88fa8ceb9cd0b41779aacb006851d46144a64e9", "size": 7327, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/c-lib-mode-test.jl", "max_stars_repo_name": "Etaphase/FastSigmoids.jl", "max_stars_repo_head_hexsha": "ebea3f97be21a36fd628cc6a38dddc5b8eee713f", "max_stars_repo_licenses": ["MIT"], "max_stars... |
\section{Section 0}\label{sec:zero}
This is a reference \cite{tur38}. This is an acronym: \ac{MI}. Fun fact: when using it again, it will only be displayed like such: \ac{MI}.
Note, that the gray boxes on the cover page can be replaced. Simply replace the \code{logo.png} file in the \code{images} folder.
cref Demons... | {"hexsha": "1873735baad5ee3253424fcbe33c6a4d1937f533", "size": 929, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/section-0.tex", "max_stars_repo_name": "christian-steinmeyer/theses-template", "max_stars_repo_head_hexsha": "d53d59f1d05025f4a3d2b3ee8f3ff22c69ce124f", "max_stars_repo_licenses": ["CC0-1.0"... |
function [M_est,U_est,V_est,L1_error] = RobustApproximation_M_UV_TraceNormReg(M,W,r,lambda,rho,maxIterIN,signM)
%% Robust low-rank matrix approximation with missing data and outliers
% min |W.*(M-E)|_1 + lambda*|V|_*
% s.t., E = UV, U'*U = I
%
%Input:
% M: m*n data matrix
% W: m*n indicator matrix, with '1' means ... | {"author": "andrewssobral", "repo": "lrslibrary", "sha": "06d457349cb5f1fc56a583cd61af9f1d5150e3a1", "save_path": "github-repos/MATLAB/andrewssobral-lrslibrary", "path": "github-repos/MATLAB/andrewssobral-lrslibrary/lrslibrary-06d457349cb5f1fc56a583cd61af9f1d5150e3a1/algorithms/rpca/RegL1-ALM/RobustApproximation_M_UV_T... |
# Replicate of calcpath subroutine
calcpath <- function(nohrs,slope,aspect,path){
for (i in 1:nohrs){
if (zenang[i] < pid2*0.998){
print(paste('zenang of i = '),zenang[i])
return()
}
path[i] = cos(zenang[i])*cos(slope)+sin(zenang[i])*sin(slope)*cos(aspect-sunazm[i])
if (path[i] == 0){
... | {"hexsha": "2da4f842ddfc8a41055d55185535b14890a6e014", "size": 550, "ext": "r", "lang": "R", "max_stars_repo_path": "src/calcpath.r", "max_stars_repo_name": "hieulel/CUPID", "max_stars_repo_head_hexsha": "4b35e5f5cf338c6061763085b32359bc68866ae1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_rep... |
#The files/folders that need to be in the executing folder are:
# yocto_api.py
# yocto_temperature.py
# folder: cdll
#Available from the Yoctopuce website:
#http://www.yoctopuce.com/EN/libraries.php Python libraries
#TODO:
#Make plotting function
#Buffer creation is commented out.
import os,sys
import time
impor... | {"hexsha": "dd24717fd0d37a8ce528b5d3af09b45be3f8d93f", "size": 9890, "ext": "py", "lang": "Python", "max_stars_repo_path": "YoctoThermistor_FISH.py", "max_stars_repo_name": "linnarsson-lab/ROBOFISH", "max_stars_repo_head_hexsha": "cc54bf6c63565a8e2e1fdfbaa12255576cf0b784", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from typing import *
import pickle
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset
class Affectdataset(Dataset):
def __init__(self, data: Dict, flatten_time_series: bool, aligned: bool = True, task: str = None) -> None:
self.da... | {"hexsha": "d3f02f479cf50ea75e9bfefd0a0346909ec28f18", "size": 3822, "ext": "py", "lang": "Python", "max_stars_repo_path": "deprecated/dataloaders/affect/get_data.py", "max_stars_repo_name": "kapikantzari/MultiBench", "max_stars_repo_head_hexsha": "44ab6ea028682040a0c04de68239ce5cdf15123f", "max_stars_repo_licenses": [... |
program LOOP
integer I,N,A(100),B(100)
do i = 1, 100
A(I) = 0.0
enddo
do i = 1, 100
call PRIV1(A,B,i)
call PRIV2(A,B,i)
enddo
end
subroutine PRIV1(V,W,N)
integer V(N),W,i
integer WORK(100)
save WORK
do i = 1,N
... | {"hexsha": "14fd020adbb128f5dcf31a684cbd78d1e52292b3", "size": 696, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/PIPS/validation/RegionPrivatization/declarations_priv.f", "max_stars_repo_name": "DVSR1966/par4all", "max_stars_repo_head_hexsha": "86b33ca9da736e832b568c5637a2381f360f1996", "max_stars_re... |
import numpy as np;
# from make_ad_nvar import *
from make_ccm import *
import pprint
import pandas as pd
import operator
import pdb
import copy
import re
import collections
from scipy.stats import poisson
from ccm_ad_flex_tests import *
class Node:
def __init__(self,value, name, parent=None):
self.value = value
... | {"hexsha": "63d964a378dfff954aa0b5e5e40bd32e8a76c033", "size": 37565, "ext": "py", "lang": "Python", "max_stars_repo_path": "smc_het_eval/ccm_ad_flexible.py", "max_stars_repo_name": "asalcedo31/SMC-Het_Scoring", "max_stars_repo_head_hexsha": "8b072a22eeefa4cbac37b9d22fe732798b62d40a", "max_stars_repo_licenses": ["Apach... |
#!/usr/bin/env python3
"""
Changelog:
New is v1_0:
- Create script
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Author:
Sleiman Safaoui
Email:
sleiman.safaoui@utdallas.edu
Github:
@The-SS
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... | {"hexsha": "ab34e42aa62042f5e207ffc315081bf2d15dc5d7", "size": 67884, "ext": "py", "lang": "Python", "max_stars_repo_path": "Unicycle Simulation/scripts/destination_distribution_check.py", "max_stars_repo_name": "TSummersLab/Risk_Bounded_Nonlinear_Robot_Motion_Planning", "max_stars_repo_head_hexsha": "717b9f07f4ed625ee... |
"""
Update claims-based hospitalization indicator.
Author: Maria Jahja
Created: 2020-09-27
"""
# standard packages
import logging
from multiprocessing import Pool, cpu_count
# third party
import numpy as np
import pandas as pd
from delphi_utils import GeoMapper
# first party
from delphi_utils import Weekday
from .... | {"hexsha": "b4169370d0d634fa8d8944bdd2c054734cf1b65f", "size": 11378, "ext": "py", "lang": "Python", "max_stars_repo_path": "claims_hosp/delphi_claims_hosp/update_indicator.py", "max_stars_repo_name": "jingjtang/covidcast-indicators", "max_stars_repo_head_hexsha": "34cb8786f78fbea2710b810a9500ee02c2379241", "max_stars_... |
module DataIO
include("filesystem/filesystem.jl")
end # module DataIO
| {"hexsha": "1e3b269151c4aa93cdeee9b773d2ae58ee48eb84", "size": 73, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/dataio/dataio.jl", "max_stars_repo_name": "gitter-badger/NumericalDataManipulation.jl", "max_stars_repo_head_hexsha": "4f1bc43e8c2f94c3700c88619f51a9632f956306", "max_stars_repo_licenses": ["MIT"... |
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
import numpy as np
'''
This package is to be used as a library. Please do not edit.
'''
def fpoly(x: np.float) -> np.float:
""" Simple polynomial of degree 5"""
return 0.009 * (x ** 5) + 0.02 * (x ** 4) - 0.3... | {"hexsha": "8d5ee936f0bcf3c5ccf6493e418f2b4fc99084df", "size": 5033, "ext": "py", "lang": "Python", "max_stars_repo_path": "Project6/lib.py", "max_stars_repo_name": "veronikadim99/Wissenschaftliches-Rechnen", "max_stars_repo_head_hexsha": "3b7c86e9488bf434f3ad1d590f5b9bb9b4cdf218", "max_stars_repo_licenses": ["Apache-2... |
""" Provide a consistent set of constants to use through CLMM """
from enum import Enum
import astropy.constants as astropyconst
import astropy.units as u
class Constants(Enum):
""" A set of constants for consistency throughout the
code and dependencies. """
CLIGHT = 299792458.0
""" Speed of light (m... | {"hexsha": "99600f2404d9ebdd062dc47bee88f4bedef4b1ce", "size": 854, "ext": "py", "lang": "Python", "max_stars_repo_path": "clmm/constants.py", "max_stars_repo_name": "96RadhikaJadhav/CLMM", "max_stars_repo_head_hexsha": "cd0508f82f9a6a4692fe785277ac25c73e89d0d7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
[STATEMENT]
lemma uminus_one_neq_one_double[simp]: "- 1 \<noteq> (1 :: double)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. - 1 \<noteq> 1
[PROOF STEP]
by (transfer, transfer, simp) | {"llama_tokens": 82, "file": "MFODL_Monitor_Optimized_Code_Double", "length": 1} |
import cv2
import numpy as np
import os
Dir = os.getcwd()
path = os.path.join(Dir, 'source')
images = os.listdir(path)
def rescale(frame, scale=0.20):
w = int(frame.shape[1] * scale)
h = int(frame.shape[0] * scale)
dim = (w, h)
return cv2.resize(frame, dim)
for file in images:
filename = file.spl... | {"hexsha": "14f152b70583e2eebcc2f1a7f48d8076edbc038e", "size": 492, "ext": "py", "lang": "Python", "max_stars_repo_path": "opencv_/rescale.py", "max_stars_repo_name": "AKSK16101999/Image_Processing", "max_stars_repo_head_hexsha": "d6b12e445ace1c7afff747f24d5028f506a96825", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import shutil
from os import path
import numpy as np
import logging
import yass
from yass import preprocess, detect, cluster, templates, deconvolute
from yass.batch import RecordingsReader
from yass import read_config
try:
from pathlib2 import Path
except ImportError:
from pathlib import Path
def test_thresh... | {"hexsha": "6a61280ffc25826bda003d7e0e2fed7cbb5e982b", "size": 3680, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/performance/test_threshold_output.py", "max_stars_repo_name": "jaib1/yass", "max_stars_repo_head_hexsha": "9899c7d63c522a26b160ac7a223c794dfd3e23c6", "max_stars_repo_licenses": ["Apache-2.0"... |
import random
import copy
import math
from collections import defaultdict
import numpy as np
import scipy as sp
import h5py
import cyclus
import pickle
from cyclus.agents import Institution, Agent, Facility
from cyclus import lib
import cyclus.typesystem as ts
class ann_lwr(Facility):
fuel_incommod = ts.String(
... | {"hexsha": "600aedb46e9d669d2c159108ac56f71e69934764", "size": 9971, "ext": "py", "lang": "Python", "max_stars_repo_path": "ann_lwr/ann_lwr.py", "max_stars_repo_name": "jbae11/ann_pwr", "max_stars_repo_head_hexsha": "c35ed75184aa7b06c3469a4bb6bed77ff2465c1c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
[STATEMENT]
lemma vars_term_ctxt_apply [simp]:
"vars_term C\<langle>t\<rangle> = vars_ctxt C \<union> vars_term t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. vars_term C\<langle>t\<rangle> = vars_ctxt C \<union> vars_term t
[PROOF STEP]
by (induct C arbitrary: t) auto | {"llama_tokens": 112, "file": "Regular_Tree_Relations_Util_Term_Context", "length": 1} |
"""
Trainer class.
"""
import json
import logging
import os
import sys
import time
from collections import OrderedDict
import torch
import numpy as np
from tqdm import tqdm
from transformers.optimization import AdamW, get_linear_schedule_with_warmup
from galaxy.args import str2bool
from galaxy.data.data_loader impor... | {"hexsha": "15602c3b1b8f0a8b04acdec5d8f0567474cc6506", "size": 47085, "ext": "py", "lang": "Python", "max_stars_repo_path": "galaxy/trainer.py", "max_stars_repo_name": "siat-nlp/GALAXY", "max_stars_repo_head_hexsha": "b1c23f588a37a588b0de7e16f5bcdbeb8a517fd5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
[STATEMENT]
lemma obs_consistent_med_a0m1a_is [iff]:
"obs_consistent R_a0m1a_is med_a0m1a_is a0i m1a"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. obs_consistent R_a0m1a_is med_a0m1a_is a0i m1a
[PROOF STEP]
by (auto simp add: obs_consistent_def R_a0m1a_is_def med_a0m1a_is_def
a0i_def m1a_def... | {"llama_tokens": 166, "file": "Security_Protocol_Refinement_Key_establish_m1_keydist_iirn", "length": 1} |
import os
import numpy as np
import sys
# sys.path.append(BASE_DIR)
# sys.path.append(os.path.join(ROOT_DIR, 'utils'))
import data_prep_util
import indoor3d_util
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
# Constants
data_dir = os.path.join(ROOT_DIR, 'data')
indoor3d_dat... | {"hexsha": "1807a33fe8d1136f1ffc9e74f2771754ab95747a", "size": 3641, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/s3dis/third_party/gen_indoor3d_h5.py", "max_stars_repo_name": "corochann/chainer-pointnet", "max_stars_repo_head_hexsha": "4b0350122c6a704ebea9bf206896a6f18e1ab4d7", "max_stars_repo_li... |
import pandas as pd
import numpy as np
def main(args):
dates = pd.date_range('20130101', periods=2)
df = pd.DataFrame(np.random.randn(2,2), index=dates, columns=list('AB'))
print(df)
return df.to_dict('split')
| {"hexsha": "2286ff4d2004051539a4cf15970b63321f3a4c5b", "size": 227, "ext": "py", "lang": "Python", "max_stars_repo_path": "python3IBM/pandas.py", "max_stars_repo_name": "csantanapr/openwhisk-demos", "max_stars_repo_head_hexsha": "d5613658678363619f7af345f49ba0bf8727d9ed", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
/* CirKit: A circuit toolkit
* Copyright (C) 2009-2015 University of Bremen
* Copyright (C) 2015-2017 EPFL
*
* Permission is hereby granted, free of charge, to any person
* obtaining a copy of this software and associated documentation
* files (the "Software"), to deal in the Software without
* restriction, in... | {"hexsha": "1d8ffc862a5ecca776a5cbcb689e824ed3682767", "size": 4702, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/classical/sat/operations/logic.hpp", "max_stars_repo_name": "eletesta/cirkit", "max_stars_repo_head_hexsha": "6d0939798ea25cecf92306ce796be154139b94f5", "max_stars_repo_licenses": ["MIT"], "max_... |
\chapter{\label{chapter3} The Abstract Syntax Tree (AST)}
The abstract class \texttt{ASTNode.cs} represents the building block of the data structure that is used as the Intermediate Representation (IR) for the \fwap language. The Abstract Syntax Tree (AST), assembled using the methods provided by the \texttt{ASTGenera... | {"hexsha": "dd1b2edb89f8adf72c085285384309e8f858ac0f", "size": 2204, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/chapters/ast.tex", "max_stars_repo_name": "MCSN-project2014/APproject", "max_stars_repo_head_hexsha": "6bdfbedfa0dc8fec7e25b81665624c6aedc93e3d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
from PIL import Image
import numbers
from collections.abc import Sequence
from typing import Tuple, List, Optional
import random
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
def _check_sequence_input(x, name, req_sizes):
msg = req_size... | {"hexsha": "3a4e30504b9edbb1dcbafa5a599b8663d30fca2d", "size": 8420, "ext": "py", "lang": "Python", "max_stars_repo_path": "semseg/transforms.py", "max_stars_repo_name": "rainarit/segmentation-benchmark", "max_stars_repo_head_hexsha": "bbdadf56ed2ff1049e7dd5925f61f524d0440401", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
Module containing tasks for morphological operations
Credits:
Copyright (c) 2017-2019 Matej Aleksandrov, Matej Batič, Andrej Burja, Eva Erzin (Sinergise)
Copyright (c) 2017-2019 Grega Milčinski, Matic Lubej, Devis Peresutti, Jernej Puc, Tomislav Slijepčević (Sinergise)
Copyright (c) 2017-2019 Blaž Sovdat, Nejc Ves... | {"hexsha": "5e6f993e371bab3494d27cab64088feaa07448e2", "size": 2506, "ext": "py", "lang": "Python", "max_stars_repo_path": "geometry/eolearn/geometry/morphology.py", "max_stars_repo_name": "rpitonak/eo-learn", "max_stars_repo_head_hexsha": "246616903c600f9d85d8d7bfcaef3785356b9fd1", "max_stars_repo_licenses": ["MIT"], ... |
C++---------------------------------------------------------------------
C Set of routines to determine automatic center of galaxy
C and automatic sky level for Fitelli and other programs
C Contains: AUTO_CENTER and AUTO_SKY
C------------------------------------------------------------------------
C Subroutine AUTO_CE... | {"hexsha": "0337536027c91b495f235c14ee028170f5efa76f", "size": 5510, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "jlpsub/auto_sky.for", "max_stars_repo_name": "jlprieur/shell_galaxies", "max_stars_repo_head_hexsha": "1dde87ef33b3c33b3a892e9ad0d642ae02ac6d9e", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/-
Copyright (c) 2021 Yury Kudryashov. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Yury Kudryashov
-/
import analysis.special_functions.integrals
import analysis.calculus.fderiv_measurable
/-!
# Non integrable functions
In this file we prove that the derivative of... | {"author": "saisurbehera", "repo": "mathProof", "sha": "57c6bfe75652e9d3312d8904441a32aff7d6a75e", "save_path": "github-repos/lean/saisurbehera-mathProof", "path": "github-repos/lean/saisurbehera-mathProof/mathProof-57c6bfe75652e9d3312d8904441a32aff7d6a75e/src/tertiary_packages/mathlib/src/analysis/special_functions/no... |
from tensorflow.keras.losses import CategoricalCrossentropy
import tensorflow as tf
import numpy as np
from utils.dataset import get_train_dataset
from utils.utils import UtilityFunction
from utils.config import Config as Cfg
from utils.model import get_model
# Build model
model, input_size = get_model(classes_numbe... | {"hexsha": "5dcdf2af8d5904d096ee5a6cf241f4a36efaf9a1", "size": 1906, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "MrRiahi/Convolutional-Neural-Networks", "max_stars_repo_head_hexsha": "e15b93376da83af89df672982f81475bf541c8cf", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
### A Pluto.jl notebook ###
# v0.19.4
#> [frontmatter]
#> title = "ExoFinder.jl"
#> description = "Let's find some worlds!"
using Markdown
using InteractiveUtils
# ╔═╡ f19b358c-8506-11ec-252c-c39dcd644d06
begin
import Pkg
Pkg.activate(Base.current_project())
using AstroImages, PlutoUI, Plots
using MarkdownLite... | {"hexsha": "e5e5b53405f8d2e9f669628797bbaf3f3a7be44c", "size": 8496, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "astroimages.jl", "max_stars_repo_name": "icweaver/Pluto_sample_notebooks", "max_stars_repo_head_hexsha": "37d5869eecd748ddcf2e7e5600cc730a689af721", "max_stars_repo_licenses": ["Unlicense"], "max_s... |
import os
import os.path as osp
import json
import torch
import numpy as np
from torch_sparse import coalesce
from torch_geometric.data import (InMemoryDataset, Data, download_url,
extract_zip)
class PPI(InMemoryDataset):
r"""Protein-protein interaction networks from the `"Predi... | {"hexsha": "e8d7bf89e92bd949bba68e690d305e388829e8ba", "size": 3466, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_geometric/datasets/ppi.py", "max_stars_repo_name": "n-kats/pytorch_geometric", "max_stars_repo_head_hexsha": "9ef6ad5501d4f2439ae608ad0d197500a8acc2d8", "max_stars_repo_licenses": ["MIT"], "... |
"""
Implements the ArraysInterface object and supporting functionality.
"""
#***************************************************************************************************
# Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS).
# Under the terms of Contract DE-NA0003525 with NTES... | {"hexsha": "8c97d0b1f6b4218becbef090736450e4bd3dce9e", "size": 43114, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygsti/optimize/arraysinterface.py", "max_stars_repo_name": "pyGSTi-Developers/pyGSTi", "max_stars_repo_head_hexsha": "bfedc1de4d604f14b0f958615776fb80ddb59e33", "max_stars_repo_licenses": ["Apac... |
#!/bin/python3
# Copyright (©) 2015-2016 Lucas Maugère, Thomas Mijieux
# 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": "61905875057adfe961f056cfebeb257f3a2cd515", "size": 1738, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/taikorank/test/levenshtein.py", "max_stars_repo_name": "tomtix/osux", "max_stars_repo_head_hexsha": "cf87171ffca9513c3a05e2156618b20cea4aef98", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import random
import argparse
import os
import numpy as np
import timm
import torch
from torch.optim import Adam, AdamW, RMSprop, SGD
from torch.utils.data import DataLoader
from torchvision.datasets import *
import torchvision.transforms as transforms
import torchdata as td
from adamp import AdamP
from radam import... | {"hexsha": "6ae6e3aa9722fb3830b4e97d6947f48b9f0f43e9", "size": 3441, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "prismleong/ICTH", "max_stars_repo_head_hexsha": "297dfa829e878151d4c057438ff5dce9cde97d27", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
# RUN: %PYTHON %s | npcomp-opt -split-input-file | FileCheck %s --dump-input=fail
import numpy as np
from npcomp.compiler import test_config
import_global = test_config.create_import_dump_decorator()
global_data = (np.zeros((2, 3)) + [1.0, 2.0, 3.0] * np.reshape([1.0, 2.0],
... | {"hexsha": "1a6ad850a6dde72fca64f52a68f1beb786486daf", "size": 741, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/Python/NumpyCompiler/array_basics.py", "max_stars_repo_name": "marbre/mlir-npcomp", "max_stars_repo_head_hexsha": "30adf9e6b0c1e94db38050a9e143f20a5a461d17", "max_stars_repo_licenses": ["Apach... |
#!/usr/bin/env python
''' differences of Gaussians .
Usage : python dog.py [<video source>]
'''
import numpy as np
import cv2
import video
from common import nothing, getsize
n=0;
if __name__ == '__main__':
import sys
print __doc__
try:
fn = sys.argv[1]
except:
fn = 0
cap = vi... | {"hexsha": "773bcf2b011afff1ed7d321c39130b723c39ab8a", "size": 2039, "ext": "py", "lang": "Python", "max_stars_repo_path": "dog.py", "max_stars_repo_name": "elliots41/python-vision", "max_stars_repo_head_hexsha": "5c29f1196f9ce8ce81a1e09f08ad42cb1364bb7a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": n... |
'''
This module fits a parameterized function for a SNIa light-curve with one
or two peaks. Taken from M. Stritzinger's PhD thesis, which was adapted
from Contardo, G., Leibundgut, B., & Vacca, W. D. 2000, A&A, 359, 876.
'''
from scipy.optimize import leastsq,brentq
from numpy import *
def Ialcn(t, par, n):
m0,g... | {"hexsha": "d9c80b140ee1cec29fc364de4e3caea7f0322e77", "size": 2451, "ext": "py", "lang": "Python", "max_stars_repo_path": "snpy/utils/fit_lc.py", "max_stars_repo_name": "emirkmo/snpy", "max_stars_repo_head_hexsha": "2a0153c84477ba8a30310d7dbca3d5a8f24de3c6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "m... |
using Revise
using CSV
using Geodesy
using Dates
using Plots
using StatsPlots
using MinimalRides
using MinimalRides: Pos, load_animal_data
filename = "/media/win/Data/Arctic fox Bylot - GPS tracking.csv"
tracks = load_animal_data(filename);
@assert length(tracks) == 20
length(tracks[1].route)
x = tracks[1].route[3].t... | {"hexsha": "bf7150bb65bf6f217ca0c9b8dff694edb7d02f8a", "size": 2001, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/AnimalExperiments.jl", "max_stars_repo_name": "Arkoniak/MinimalRides.jl", "max_stars_repo_head_hexsha": "e842094242dffd8f9a97ad2b5a835f37105116bc", "max_stars_repo_licenses": ["MIT"], "ma... |
import tensorflow as tf
import numpy as np
import re
from baselines.acktr.kfac_utils import *
from functools import reduce
KFAC_OPS = ['MatMul', 'Conv2D', 'BiasAdd']
KFAC_DEBUG = False
class KfacOptimizer():
def __init__(self, learning_rate=0.01, momentum=0.9, clip_kl=0.01, kfac_update=2, stats_accum_iter=60,
... | {"hexsha": "0aec823d6162c78e0852e4a6ff214864daaf6de9", "size": 46263, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/acktr/kfac.py", "max_stars_repo_name": "speedcell4/baselines", "max_stars_repo_head_hexsha": "c4be964fad7d015d1aa2f76a946c7c8c1025ce61", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
Created on Wednesday 7 March 2018
Last update: Wednesday 25 April 2018
@author: Michiel Stock
michielfmstock@gmail.com
Make a city for the project of discrete optimization
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import BallTree
import json
blue = '#264653'
green = '#2a9d8f'... | {"hexsha": "23f70565cf3de7f84bdbe85220f1fcb37221f151", "size": 3039, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapters/08.ProjectDiscrete/make_city.py", "max_stars_repo_name": "ntienvu/SelectedTopicsOptimization", "max_stars_repo_head_hexsha": "069659ca9754cc7fd884b654a06157cc7da6f963", "max_stars_repo_li... |
import random
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from cogment_verse_torch_agents.third_party.hive.agent import Agent
from cogment_verse_torch_agents.third_party.td3.td3_mlp import ActorMLP, CriticMLP
class DDPGAgent(Agent):
def __init__(
self,
... | {"hexsha": "89d1ed11f01878ecf6f4d75ddba228dd5990b137", "size": 8327, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_agents/cogment_verse_torch_agents/third_party/hive/ddpg.py", "max_stars_repo_name": "kharyal/cogment-verse", "max_stars_repo_head_hexsha": "12bcb855bc742e3ec4ed11c40a1b475e95a32515", "max_st... |
/-
Copyright (c) 2020 Bhavik Mehta. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Bhavik Mehta, Andrew Yang
-/
import category_theory.limits.shapes.terminal
import category_theory.limits.shapes.pullbacks
import category_theory.limits.shapes.binary_products
/-!
# Cons... | {"author": "nick-kuhn", "repo": "leantools", "sha": "567a98c031fffe3f270b7b8dea48389bc70d7abb", "save_path": "github-repos/lean/nick-kuhn-leantools", "path": "github-repos/lean/nick-kuhn-leantools/leantools-567a98c031fffe3f270b7b8dea48389bc70d7abb/src/category_theory/limits/constructions/binary_products.lean"} |
# coding: utf-8
# In[1]:
import numpy as np
import matplotlib.pyplot as plt
get_ipython().magic('matplotlib inline')
from PIL import Image
# In[2]:
def stitch(stack, numpix_threshold=0):
'''
Combine multiple instance segmentations based on overlapping patches into a single
segmentation
Args
... | {"hexsha": "c78574613d35aa229128b47bea3937c527ae3543", "size": 8154, "ext": "py", "lang": "Python", "max_stars_repo_path": "postprocess/stitching.py", "max_stars_repo_name": "patrickfletcher/Biological-structure-segmentation-in-microscopy-images-using-deep-learning", "max_stars_repo_head_hexsha": "6ce3b008dbe0374cd4c50... |
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import PPO1
from scipy.special import softmax
from gym.spaces import Box, Discrete
from typing import NamedTuple, Callable, List, Union
from stable_baselines3 import PPO
from ... | {"hexsha": "582e57d834ed27e48de92bd1fee8bb4d5a5939a1", "size": 6358, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/reference_implementations/rl_algorithms.py", "max_stars_repo_name": "lite-david/polymath", "max_stars_repo_head_hexsha": "cf1addc75e203fa606ebc6d32bc552fb3975ea99", "max_stars_repo_licenses"... |
subroutine sortst ( c10a , c10b , val , nr )
c Subroutine to sort (part of) tables R6-R7-R8
integer nr, ir, jndex
character*10 c10a(nr), c10b(nr), evea, eveb, sortar(nr)
real val(nr), eveval
logical flag
integer sortnr(nr), evenr, nrarr
c Set index
... | {"hexsha": "0a6f9c73c9eac6c8226e313d0c87176107aa9c20", "size": 5438, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/tools_gpl/waqpb/packages/waqpb_lib/src/sortst.f", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac66f5bd0d9a1... |
/*
Copyright (C) 2017 Sascha Meiers
Distributed under the MIT software license, see the accompanying
file LICENSE.md or http://www.opensource.org/licenses/mit-license.php.
*/
#include <iostream>
#include <fstream>
#include <vector>
#include <unordered_map>
#include <tuple>
#include <boost/program_options/cmdline.... | {"hexsha": "1bbb1268f5eb559749c276cb7b474e883f2b55fd", "size": 17428, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/count.hpp", "max_stars_repo_name": "tobiasmarschall/mosaicatcher", "max_stars_repo_head_hexsha": "42b078ec0964f3711f0f4871065be5157e63eb37", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
[STATEMENT]
lemma ipp_cond2_minus:"\<lbrakk>ipp_cond1 {a} i; ipp_cond2 z {a} i f\<rbrakk> \<Longrightarrow>
ipp_cond2 z {\<^sub>i- a} i f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>ipp_cond1 {a} i; ipp_cond2 z {a} i f\<rbrakk> \<Longrightarrow> ipp_cond2 z ... | {"llama_tokens": 178, "file": "Group-Ring-Module_Algebra9", "length": 1} |
clear all; close all; clc
n=200; L=8;
x=linspace(0,L,n);
x1=x(1:100); % train
x2=x(101:200); % test
n1=length(x1);
n2=length(x2);
ftrain=(x1.^2).'; % train parabola x=[0,4]
ftest=(x2.^2).'; % test parbola x=[4,5]
figure(1), subplot(3,1,1),
plot(x1,ftrain,'r',x2,ftest,'b','Linewidth',[2])
legend('','','Location','... | {"author": "dynamicslab", "repo": "databook_matlab", "sha": "d390d39d18489a4804ee87a143ae8db8a1f3010b", "save_path": "github-repos/MATLAB/dynamicslab-databook_matlab", "path": "github-repos/MATLAB/dynamicslab-databook_matlab/databook_matlab-d390d39d18489a4804ee87a143ae8db8a1f3010b/CH04/CH04_SEC05_1_CrossValidate.m"} |
"""Inputs for MNIST dataset"""
import math
import numpy as np
import glob
import CSGM.dcgan.dcgan_utils as dcgan_utils
import CSGM.mnist.mnist_model_def as mnist_model_def
import tensorflow.compat.v1 as tf
from tensorflow.examples.tutorials.mnist import input_data
NUM_TEST_IMAGES = 10000
def get_random_test_subset(... | {"hexsha": "e06d17bedcdd132e45464dc75257584e375ac846", "size": 2408, "ext": "py", "lang": "Python", "max_stars_repo_path": "CSGM/mnist/mnist_input.py", "max_stars_repo_name": "PSCLab-ASU/OpenICS", "max_stars_repo_head_hexsha": "e8f639f9278ce88c98f14daf026a56395cb64ca9", "max_stars_repo_licenses": ["CC0-1.0"], "max_star... |
# Plots product 10
# By Jose Ignacio Hernandez
# Load packages
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# Load data
inputfile = "../output/producto10/FallecidosEtario_T.csv"
dat = pd.read_csv(inputfile)
# Create variables
date = dat["Grupo de edad"]
cases = dat[["<=39","40-49","50-59","... | {"hexsha": "56895188ff3ded0abf8679975a0380b89e89b4bc", "size": 1521, "ext": "py", "lang": "Python", "max_stars_repo_path": "plots/plots_product_10.py", "max_stars_repo_name": "ighdez/Datos-COVID19", "max_stars_repo_head_hexsha": "9b11b59f3a63d743681916b55c33440f5f18e541", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import re
from typing import Any
from typing import Dict
from typing import List
from typing import Tuple
from typing import Union
import attr
import numpy as np
import pandas as pd
import talib
from sklearn.preprocessing import FunctionTransformer
@attr.s
class TAFactory:
"""
Factory that creates sklearn tr... | {"hexsha": "6082354cdd5f033faaefc2b0c6ef90e3cb46dbed", "size": 10875, "ext": "py", "lang": "Python", "max_stars_repo_path": "mizarlabs/transformers/technical/factory.py", "max_stars_repo_name": "MizarAI/mizar-labs", "max_stars_repo_head_hexsha": "c6ec17bc3d9a91ec3f6ee2e7b20017499115fc37", "max_stars_repo_licenses": ["M... |
# Open3D: www.open3d.org
# The MIT License (MIT)
# See license file or visit www.open3d.org for details
import numpy as np
import argparse
import math
import sys
sys.path.append("../..")
sys.path.append("../Utility")
from py3d import *
from common import *
def scalable_integrate_rgb_frames(path_dataset, intrinsic):
... | {"hexsha": "60f3c600c1174c0147c1eec7d6ff8f8d60d47376", "size": 2274, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pipeline/py3d/Tutorial/ReconstructionSystem/integrate_scene.py", "max_stars_repo_name": "riccardomarin/FARM-ZOSR", "max_stars_repo_head_hexsha": "7d29469d7e1c08b4a1e5d13084435001f509bec3", "max_st... |
##############################################################################
import sys
import numpy as np
from IOModule import IOProcessor
from HandlerModule import Handler
from EncoderModule import Encoder
from ExperimentationModule import Experimentation
from VariablesModule import N_FOLDS, MODEL_DICT, HEADERS_... | {"hexsha": "4973787724d8588d1545388cec585ffcc7e5f2db", "size": 3161, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment.py", "max_stars_repo_name": "jpedrocm/DSChallenge", "max_stars_repo_head_hexsha": "cb865d3e8bab66b718c3a2a6943827b0285f534b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
# encoding: utf-8
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def list_all_files(rootdir, key):
import os
_files = []
list = os.listdir(rootdir) # 列出文件夹下所有的目录与文件
... | {"hexsha": "2b8e54d9ba07085ebbc7eda5e53fdf3cf8fed6cb", "size": 5869, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch/exercises/wgan/main_improved_wgan.py", "max_stars_repo_name": "wangyendt/deeplearning_models", "max_stars_repo_head_hexsha": "47883b6c65b8d05a0d1c5737f1552df6476ded34", "max_stars_repo_lic... |
Fuzio is an Italianfusion Restaurants restaurant Universal pasta. They have mostly Italian pastas, with a few Asian noodle bowls and steak/tuna/salmon entrees. (If youre looking for something closer to true Italian rather than fusion, then try Caffe Italia, Strings Italian Cafe Strings, Pasta, or Osteria Fasulo.) I... | {"hexsha": "06c0336320dfd54421d9ec84bdf2b6acf0e404ed", "size": 30079, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Fuzio.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
"""
Created on 22 Apr 2015
@author: Anna
"""
from .Globals import G
from .Allocation_3 import Allocation2
from copy import deepcopy
from numpy import mean, array, absolute, std
from operator import itemgetter
from .UtilisationCalculation import utilisationCalc2, utilisationCalc1, utilisationCalc3
def AllocationRouti... | {"hexsha": "5611f12e70a534ec575b90d2e3b5d4ecb180deeb", "size": 16498, "ext": "py", "lang": "Python", "max_stars_repo_path": "manpy/simulation/applications/DemandPlanning/AllocationRoutine_Final2.py", "max_stars_repo_name": "datarevenue-berlin/manpy", "max_stars_repo_head_hexsha": "0056eb6e93cba3bf2a1061f9170aa2a1edf248... |
function table2 = i4mat_border_cut ( m, n, table )
%*****************************************************************************80
%
%% I4MAT_BORDER_CUT cuts the "border" of an I4MAT.
%
% Discussion:
%
% We suppose the input data gives values of a quantity on nodes
% on a 2D grid, and we wish to create a new t... | {"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/i4lib/i4mat_border_cut.m"} |
# coding=utf-8
# Copyright (C) 2020 NumS Development Team.
#
# 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... | {"hexsha": "e091aef6a1906865860162a8e79aedc8f6fd86c7", "size": 3716, "ext": "py", "lang": "Python", "max_stars_repo_path": "nums/experimental/nums_modin.py", "max_stars_repo_name": "gohar94/nums", "max_stars_repo_head_hexsha": "2d8b0d7dd7b48c5b56641d4f03279b5ce2185db5", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# coding:utf-8
import re
import numpy as np
class AddManualFeature(object):
def __init__(self, *, train_feature, test_feature):
self.__train_feature = train_feature.copy()
self.__test_feature = test_feature.copy()
self.__income_by_occupation = None
def add_manual_feature(self... | {"hexsha": "0b4b32779027aeaa24ffedbb64a7b358e0ca1ed3", "size": 4649, "ext": "py", "lang": "Python", "max_stars_repo_path": "20180617/JamesShepherd/AddManualFeature.py", "max_stars_repo_name": "fengjiaxin/Home_Credit_Default_Risk", "max_stars_repo_head_hexsha": "3407e76b4e5cfb8dd6056d24675b80fe0e82c123", "max_stars_repo... |
using RungeKutta.Tableaus: get_radau_1_nodes, get_radau_1_weights, get_radau_1_coefficients,
get_radau_2_nodes, get_radau_2_weights, get_radau_2_coefficients
@testset "$(rpad("Radau Tableaus",80))" begin
@test_throws ErrorException get_radau_1_nodes(1)
@test_throws ErrorException ge... | {"hexsha": "b15675674f7cf65ceedca2b8760c3d65c772d0b9", "size": 3677, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_radau.jl", "max_stars_repo_name": "JuliaGNI/RungeKutta.jl", "max_stars_repo_head_hexsha": "b6933446c0f76525a2e36f4d94bf7ff9694c7f5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import matplotlib.pyplot as plt
from random import random
from numba import njit
import random as rand
class Toric_code():
nbr_eq_classes = 16
def __init__(self, size):
self.system_size = size
self.qubit_matrix = np.zeros((2, self.system_size, self.system_size), dtype=np.ui... | {"hexsha": "106d3fd79468689f6c75514a3f749efb0414ccd3", "size": 15417, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/toric_model.py", "max_stars_repo_name": "mats-granath/EWD-QEC", "max_stars_repo_head_hexsha": "6ce9ac0940c18e2a63ec244cdd7b80e40b0c7073", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import logging
import warnings
from typing import List, Tuple, Dict
import numpy as np
import pandas as pd
from models import BetaBernoulli, ClasswiseEce
logger = logging.getLogger(__name__)
np.random.seed(0)
############################################################################
"""
Update DATA_DIR, RESULTS_... | {"hexsha": "bcdd5873eff3a2544a8b91eaf68e69cbbcd54b44", "size": 16936, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data_utils.py", "max_stars_repo_name": "rloganiv/bayesian-blackbox", "max_stars_repo_head_hexsha": "6a111553200b6aa755149e08174abe1a61d37198", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/-
Copyright (c) 2021 Justus Springer. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Justus Springer
-/
import algebra.category.Group.filtered_colimits
import algebra.category.Module.basic
/-!
# The forgetful functor from `R`-modules preserves filtered colimits.
For... | {"author": "jjaassoonn", "repo": "projective_space", "sha": "11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce", "save_path": "github-repos/lean/jjaassoonn-projective_space", "path": "github-repos/lean/jjaassoonn-projective_space/projective_space-11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce/src/algebra/category/Module/filtered_coli... |
import os
import argparse
import numpy as np
from itertools import cycle
import torch
import random
import pickle
from torchvision import datasets
from torch.autograd import Variable
from torch.distributions import Normal
import math
from alternate_data_loader import MNIST_Paired
from alternate_data_loader import Doub... | {"hexsha": "aa73a6a2290ed25a39fba8bd5bd82ef97e54ec2d", "size": 12930, "ext": "py", "lang": "Python", "max_stars_repo_path": "inference.py", "max_stars_repo_name": "vicissitude1999/multi-level-vae", "max_stars_repo_head_hexsha": "83bc98fbe5046c61941298d4fd49b08fd868ee89", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import matplotlib.pyplot as plt
import rosbag
import argparse
import numpy as np
def make_llc_plot(bagfile):
b = rosbag.Bag(bagfile)
state_est_topic_name = '/vehicle/state_est'
mpc_path_topic_name = '/vehicle/mpc_path'
if '/vehicle/state_est_dyn' in b.get_type_and_topic_info()[1].keys():
state_est_topic_name =... | {"hexsha": "bc9f0e330eb97d93f3e0bf9657dd22944938bb64", "size": 3282, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/analysis/plot_low_level_control.py", "max_stars_repo_name": "yluthu/genesis_path_follower", "max_stars_repo_head_hexsha": "55d6e88a5e928cc214f33b07dc2624e6d13e51df", "max_stars_repo_licens... |
Require Export Fiat.Common.Coq__8_4__8_5__Compat.
(** * Definition of a parse-tree-returning CFG parser-recognizer *)
Require Import Coq.Lists.List.
Require Import Coq.Arith.EqNat.
Require Import Coq.Arith.Compare_dec Coq.Arith.Wf_nat.
Require Import Coq.ZArith.ZArith.
Require Import Fiat.Common.List.Operations.
Requir... | {"author": "mit-plv", "repo": "fiat", "sha": "4c78284c3a88db32051bdba79202f40c645ffb7f", "save_path": "github-repos/coq/mit-plv-fiat", "path": "github-repos/coq/mit-plv-fiat/fiat-4c78284c3a88db32051bdba79202f40c645ffb7f/src/Parsers/GenericRecognizerMin.v"} |
# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# Maximilian Christ (maximilianchrist.com), Blue Yonder Gmbh, 2016
from unittest import TestCase
import pandas as pd
import numpy as np
from tsfresh.transformers.feature_selector import F... | {"hexsha": "f748e51dcd1ea370eb197cbae823f26353bb34ee", "size": 3152, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/transformers/test_feature_selector.py", "max_stars_repo_name": "awesome-archive/tsfresh", "max_stars_repo_head_hexsha": "9419aa15bb26a3725291f39636354e67c9b04caa", "max_stars_repo_licenses":... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.