text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
identity_vector(l::Integer)
"""
function identity_vector(l::Integer)
IdentityVector(l)
end
struct IdentityVector{T<:Integer} <: AbstractVector{T}
length::T
end
function getindex(c::IdentityVector{T},i::Integer) where T
@assert i > 0
@assert i <= c.length
j::T = i
j
end
size(c::IdentityVector) = ... | {"hexsha": "10163ca889e05b169b78eb00cac2e90ad9158f15", "size": 877, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Arrays/IdentityVectors.jl", "max_stars_repo_name": "barche/Gridap.jl", "max_stars_repo_head_hexsha": "70325414653cf0b9822770b150fb082d1a0a5f78", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
Show differences between WT and STFT
"""
from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
import pywt
waveletname = 'morl'
scales = range(1,200)
t = np.linspace(-1, 1, 200, endpoint=False)
sig = np.cos(2 * np.pi * 7 * t) + signal.gausspulse(t - 0.4, fc=2)
t = np.linspace(-1, 1, 50, en... | {"hexsha": "c2acd711320048cf4096b260d6e85cd5e108c428", "size": 1015, "ext": "py", "lang": "Python", "max_stars_repo_path": "cwt_vs_STFT.py", "max_stars_repo_name": "mn270/Human-Activity-Recognize-HARdataset-", "max_stars_repo_head_hexsha": "861391174f0780c8fe3d69663491076e4c4b737c", "max_stars_repo_licenses": ["MIT"], ... |
import numpy as np
import math
import time
import pcl
from sklearn.neighbors import NearestNeighbors
def nearest_neighbor(src, dst):
'''
Find the nearest (Euclidean) neighbor in dst for each point in src
Input:
src: Nxm array of points
dst: Nxm array of points
Output:
distances:... | {"hexsha": "8f73f35418c53b2a72624b1ed61e11162a7d8af6", "size": 6825, "ext": "py", "lang": "Python", "max_stars_repo_path": "ICP/ICPv1/ICP.py", "max_stars_repo_name": "Yihua-Ni/Tools", "max_stars_repo_head_hexsha": "b40c24b0b2a7025f13182fc5ed5bfcf63b389585", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
[STATEMENT]
lemma abs_majorities_intersect:
assumes crd: "card E < card S + card T"
and s: "S \<subseteq> E" and t: "T \<subseteq> E" and e: "finite E"
shows "S \<inter> T \<noteq> {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. S \<inter> T \<noteq> {}
[PROOF STEP]
proof (clarify)
[PROOF STATE]
p... | {"llama_tokens": 1040, "file": "Heard_Of_Majorities", "length": 16} |
#!/usr/bin/env python
"""
Copyright (c) 2019 Microsoft Corporation. All rights reserved.
MIT License
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limita... | {"hexsha": "c9f5608afc9f0e36116ad4efc2c99be8d698cc38", "size": 9541, "ext": "py", "lang": "Python", "max_stars_repo_path": "reader/preprocess.py", "max_stars_repo_name": "gaochangfeng/pykaldi2", "max_stars_repo_head_hexsha": "5e988e5968aa9a5867f8179e6c53ea715ac46bdc", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import os, time
import matplotlib.pyplot as plt
import numpy as np
import random
import tensorflow as tf
import genart.tf.charts.data as mdata
import genart.tf.charts.model as mmodel
import genart.gen_charts as gc
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_... | {"hexsha": "7012d8b485662be7ac9a31b4cd047c443412ce09", "size": 7513, "ext": "py", "lang": "Python", "max_stars_repo_path": "genart/tf/charts/train_cgan.py", "max_stars_repo_name": "dyf/genart", "max_stars_repo_head_hexsha": "98c8fd31fba4d0e6675809ff4fbc7ea22688bd29", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# ABIF Reader
# ===========
export get_tags, tagelements
struct AbifDirEntry
name::String
number::Int32
element_type::Int32
element_size::Int32
num_elements::Int32
data_size::Int32
data_offset::Int32
end
"""
ABIF.Reader(input::IO)
Create a data reader of the ABIF file format.
# Argum... | {"hexsha": "6cc53e361d85b51a79cd600e5f169700f70e0b4a", "size": 6372, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/abif/reader.jl", "max_stars_repo_name": "ivirshup/BioSequences.jl", "max_stars_repo_head_hexsha": "3c12124952d26f85df9de88307e24ee5a93b4dab", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from PIL import Image
import numpy as np
import cv2 as cv
import os, sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from estimate_calories_from_image import compute_volume, compute_calories, compute_volume_with_grabcut, get_foreground_pixels
def test():
# helper functions for test
... | {"hexsha": "43cf25207674e104ce1fd69dfd5b85e89c244162", "size": 2297, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/testing/unittests/estimate_calories_from_image_test.py", "max_stars_repo_name": "kallentu/chowdr", "max_stars_repo_head_hexsha": "47efd86025836e04c251c06f86c32d5519b2e0a7", "max_stars_repo... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.core.bbox.demodata import random_boxes
def test_imrenormalize():
from mmtrack.core import imrenormalize
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=Tr... | {"hexsha": "c8bfd53e4f1cd2d0055db1af6c8044acaa5bc045", "size": 5229, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_core/test_track/test_transforms.py", "max_stars_repo_name": "BigBen0519/mmtracking", "max_stars_repo_head_hexsha": "61509b301ccbc2ab14f82a682b94c56f82ce09de", "max_stars_repo_licenses":... |
import torch
import loader
import numpy as np
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.nn.functional as F
import network
device = loader.device
noise_dim = 100
initial_img_size = 2
final_img_size = 32
img_channels = 3
num_classes = 10
batch_size = loader.batch_size
epochs = l... | {"hexsha": "5820efa0960cbdf4ef58a06b4bc30450cec5567b", "size": 1645, "ext": "py", "lang": "Python", "max_stars_repo_path": "gans/CGAN/train.py", "max_stars_repo_name": "IvLabs/Variational-DL", "max_stars_repo_head_hexsha": "cd431564ae77ba42a485db17416a6033b32c48fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/**
* \file CachedCosinusGeneratorFilter.cpp
*/
#include "CachedCosinusGeneratorFilter.h"
#include <boost/math/constants/constants.hpp>
#include <cmath>
#include <cstdint>
#include <cstring>
namespace ATK
{
template<typename DataType_>
CachedCosinusGeneratorFilter<DataType_>::CachedCosinusGeneratorFilter(int ... | {"hexsha": "e5018c49dcdb91408c962290282cd29b3b164e0e", "size": 2674, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ATK/Tools/CachedCosinusGeneratorFilter.cpp", "max_stars_repo_name": "AudioTK/AudioTK", "max_stars_repo_head_hexsha": "dba42eea68534501efe74692b74edf4792cca231", "max_stars_repo_licenses": ["BSD-3-Cl... |
\documentclass[11pt,twoside,fleqn,openright,titlepage]{cslreport}
\input{moretext}
\raggedbottom
\usepackage{cite,relative,url,alltt,times}
\usepackage{amsfonts,latexsym,amssymb}
\pagenumbering{roman}
\setcounter{page}{0}
\usepackage[bookmarks=true,hyperindex=true,colorlinks=true,linkcolor=black,citecolor=blue]{hyperr... | {"hexsha": "717e3a4d372fd9e18873833a44167c9590dc99b8", "size": 148112, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ext/yices/doc/manual/manual.tex", "max_stars_repo_name": "maelvls/ocamlyices2", "max_stars_repo_head_hexsha": "554893d467a4bf3e9b0b630833b417348b15e771", "max_stars_repo_licenses": ["0BSD"], "max_... |
"""
The Dual Thrust trading algorithm is a famous strategy developed by Michael Chalek.
It is a breakout system, commonly used in futures, forex and equity markets.
The limits are based on today’s opening price plus or minus a certain percentage of recent trading range.
When the price breaks through the upper level, it... | {"hexsha": "c603dd87346c9a2a4ff07b5bada13e265550397f", "size": 8751, "ext": "py", "lang": "Python", "max_stars_repo_path": "backtest/dual_thrust.py", "max_stars_repo_name": "jingmouren/QuantResearch", "max_stars_repo_head_hexsha": "7a17e567b0e95481894ed37524c041b30155b6cb", "max_stars_repo_licenses": ["MIT"], "max_star... |
import h5py
import numpy as np
import os
def run(eventid,file, end):
# parent_dir = "/Users/parthshah/Documents/Northeastern/Spring2022/BigDataAnalytics/Assignment3/API/Intermediate_Files/"
path = "Dummy Variable plz delete"
data_path = "/Users/parthshah/Documents/Northeastern/Spring2022/BigDataAnalytics/A... | {"hexsha": "0dcc2e16bc3710083d379c08ff76ed972afecdc6", "size": 1369, "ext": "py", "lang": "Python", "max_stars_repo_path": "API/Functions/datapipeline.py", "max_stars_repo_name": "BigDataArchitecture/Assignment-3-4", "max_stars_repo_head_hexsha": "f4c87dadf443273ed532a8f9ea3c364b9fda75eb", "max_stars_repo_licenses": ["... |
import logging
import numpy as np
import paddle
from paddle import fluid
from paddle.fluid import dygraph
from modules.util import Hourglass, AntiAliasInterpolation2d, kp2gaussian, \
make_coordinate_grid_cpu
# ====================
TEST_MODE = False
if TEST_MODE:
logging.warning('TEST MODE: dense_motion.py')
#... | {"hexsha": "96fccf7fd56d4689cdfea019f8cd26da26d3a093", "size": 9656, "ext": "py", "lang": "Python", "max_stars_repo_path": "first_order/src/modules/dense_motion.py", "max_stars_repo_name": "GuoQuanhao/Contrib", "max_stars_repo_head_hexsha": "9069366559d0353c96075ed573222f3fbdfabafe", "max_stars_repo_licenses": ["Apache... |
import tensorflow as tf
import numpy as np
import os
from scipy.io import loadmat
from epi.util import dbg_check
import matplotlib.pyplot as plt
# import tensorflow_probability as tfp
FANO_EPS = 1e-6
neuron_inds = {"E": 0, "P": 1, "S": 2, "V": 3}
def load_SSSN_variable(v, ind=0):
# npzfile = np.load("data/V1_Zs... | {"hexsha": "3f24a09d23451ff8f884ff410d0e50d8a368e438", "size": 7867, "ext": "py", "lang": "Python", "max_stars_repo_path": "neural_circuits/SSSN.py", "max_stars_repo_name": "cunningham-lab/epi", "max_stars_repo_head_hexsha": "38febae7035ca921334a616b0f396b3767bf18d4", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import numpy
import matplotlib
import matplotlib.pyplot as plt
import svm_funcs
matplotlib.rcParams.update({'font.size': 8})
def plot_data(x_array, y_array, model=None, reg_C=0, linear_boundary=False, nonlinear_boundary=False):
# Find indices of positive and negative examples
positives = numpy.where(y_arra... | {"hexsha": "ef258d3cd854baa750ed13f0ce7cf65ed2d3f68e", "size": 1523, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ex6_support_vector_machines/svm_funcs/plot_data.py", "max_stars_repo_name": "ashu-vyas-github/AndrewNg_MachineLearning_Coursera", "max_stars_repo_head_hexsha": "1be5124b07df61f7295dd1c5151b... |
[STATEMENT]
lemma set_takeWhile_less_sorted:
"\<lbrakk> sorted I; x \<in> set I; x < n \<rbrakk> \<Longrightarrow> x \<in> set (takeWhile (\<lambda>x. x < n) I)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>sorted I; x \<in> set I; x < n\<rbrakk> \<Longrightarrow> x \<in> set (takeWhile (\<lambda>x. x <... | {"llama_tokens": 1387, "file": "UTP_toolkit_List_Extra", "length": 9} |
"""
PyTorch-emulation of ray tracing
"""
import numpy as np
import torch
import common.utils as utils
import pyrenderer
class Raytracing:
def __init__(self,
settings,# : pyrenderer.RendererInputs,
fov_y_radians: float,
network_output : str,
ste... | {"hexsha": "593a6a04416d172b2a7689cfdf1f72e79107acfd", "size": 13456, "ext": "py", "lang": "Python", "max_stars_repo_path": "applications/common/raytracing.py", "max_stars_repo_name": "shamanDevel/fV-SRN", "max_stars_repo_head_hexsha": "966926ee678a0db0f1c67661537c4bb7eec0c56f", "max_stars_repo_licenses": ["MIT"], "max... |
function panel = BstPanel( varargin )
% Constructor for object BstPanel.
% A BstPanel object holds mainly a java Swing container to be displayed in the
% Brainstorm main window.
%
% Constructor call :
% BstPanel(name, jHandle, sControls)
% BstPanel() : just to have a data template
%
% Data structure
% - jHan... | {"author": "brainstorm-tools", "repo": "brainstorm3", "sha": "a892cfaabde1eaa2f9a3ac015c05b73f3739433a", "save_path": "github-repos/MATLAB/brainstorm-tools-brainstorm3", "path": "github-repos/MATLAB/brainstorm-tools-brainstorm3/brainstorm3-a892cfaabde1eaa2f9a3ac015c05b73f3739433a/toolbox/gui/@BstPanel/BstPanel.m"} |
# -*- coding: utf-8 -*-
# Copyright © 2021 Patrick Levin
# SPDX-Identifier: MIT
from dataclasses import dataclass
from enum import IntEnum
import numpy as np
import os
import tensorflow as tf
from PIL.Image import Image
from typing import List, Optional, Sequence, Tuple, Union
from fdlite import ArgumentError... | {"hexsha": "f6828edaa83222473fcbe447db46dd8a69861fe6", "size": 21163, "ext": "py", "lang": "Python", "max_stars_repo_path": "fdlite/iris_landmark.py", "max_stars_repo_name": "joshtrivedi/face-detection-tflite", "max_stars_repo_head_hexsha": "6ae3bc770dd029af0c1c716d46ace6c8ced05fef", "max_stars_repo_licenses": ["MIT"],... |
function DAt = getDAtm(A,Ablkjc,dense,DAtdenq,d,K)
% DAt = getDAtm(A,Ablkjc,dense,DAtdenq,d,K)
%
% GETDATM Computes d[k]'*Aj[k] for each lorentz block k and constraint j.
%
% ******************** INTERNAL FUNCTION OF SEDUMI ********************
%
% See also sedumi, getada2.
% This file is part of SeDuMi 1.1 by Imre P... | {"author": "zarathustr", "repo": "LibQPEP", "sha": "99e5c23e746ace0bac4a86742c31db6fcf7297ba", "save_path": "github-repos/MATLAB/zarathustr-LibQPEP", "path": "github-repos/MATLAB/zarathustr-LibQPEP/LibQPEP-99e5c23e746ace0bac4a86742c31db6fcf7297ba/MATLAB/sedumi/getDAtm.m"} |
[STATEMENT]
lemma "ipv4_cidr_toString (ipv4addr_of_dotdecimal (192,168,0,1), 22) = ''192.168.0.1/22''"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ipv4_cidr_toString (ipv4addr_of_dotdecimal (192, 168, 0, 1), 22) = ''192.168.0.1/22''
[PROOF STEP]
by eval | {"llama_tokens": 152, "file": "Simple_Firewall_Primitives_Primitives_toString", "length": 1} |
""" from https://www.manishkurse.com/PythonProjects/Analyze_Fitbit_Data.html
# Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
% matplotlib inline
import seaborn as sns
import fitbit
from datetime import timedelta
import csv
import sys, os
from col... | {"hexsha": "11ccbb5015e4333c663e06053c665aa40f2df10e", "size": 25408, "ext": "py", "lang": "Python", "max_stars_repo_path": "example.py", "max_stars_repo_name": "hunterdp/fitbit-tracker", "max_stars_repo_head_hexsha": "9109e6a1e553bd8007a80961d4d4707f3f60ae55", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
function y2 = discrim_plot(discf,y,varargin)
% y2 = discrim_plot(discf,y,[new figure],[column to do partial corr plot of])
%
% Plots correlation or partial correlation
% Color codes by median split of y
%
% see also:
% cluster_discrim
% cluster_discrim_montage
dofig = 1; dopr = 0;
if length(varargin) > 0, dofig = vara... | {"author": "canlab", "repo": "CanlabCore", "sha": "af242e120f0480c4feaeea90471c015a14f1f60e", "save_path": "github-repos/MATLAB/canlab-CanlabCore", "path": "github-repos/MATLAB/canlab-CanlabCore/CanlabCore-af242e120f0480c4feaeea90471c015a14f1f60e/CanlabCore/Cluster_contig_region_tools/Cluster-based_multivar_tools/discr... |
#include <boost/graph/adjacency_list.hpp>
#include <boost/graph/breadth_first_search.hpp>
#include <boost/graph/named_function_params.hpp>
#include <boost/graph/visitors.hpp>
#include <boost/array.hpp>
#include <array>
#include <utility>
#include <algorithm>
#include <iostream>
int main()
{
enum { topLeft, topRight,... | {"hexsha": "240a26a45bd4108719e3b50a9678a2e276900d0b", "size": 1107, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Example/graph_09/main.cpp", "max_stars_repo_name": "KwangjoJeong/Boost", "max_stars_repo_head_hexsha": "29c4e2422feded66a689e3aef73086c5cf95b6fe", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
% !TeX spellcheck = en_US
% !TeX TS-program = xelatex
\documentclass[11pt]{ltxdoc}
\usepackage{color}
\usepackage{xspace,fancyvrb,longtable,booktabs}
\usepackage[neverdecrease]{paralist}
\usepackage[format=hang,labelfont=bf,labelsep=period]{caption}
\definecolor{xpgblue}{rgb}{0.02,0.04,0.48}
\definecolor{lightblue}{rgb... | {"hexsha": "262b64f999de7b9441e5ef111e1b1ae274c3740d", "size": 127718, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/polyglossia.tex", "max_stars_repo_name": "jspitz/polyglossia", "max_stars_repo_head_hexsha": "83332f10cee52ed34d1fb6601e23f29ce0659a27", "max_stars_repo_licenses": ["LPPL-1.3c"], "max_stars_co... |
import numpy as np
#fermion number
N=500
dk=2*np.pi/N
kIndHalf=range(0,int(N))
#parameters before quench
mu0=0
t0=1.0
d0=-1.0
#parameters after the quench
mu1=1
t1=t0
d1=d0
lmd=3
lmdAll=range(0,20)
threadNum=12
#occupation ratio
rho=1
tol=1e-15
#dict of spectrum
deltaKAll=dict()
EkAll=dict()
linEAll=[]
h0Val = lmd... | {"hexsha": "7289dd2a5e9edbe020dca667022ededea579110d", "size": 452, "ext": "py", "lang": "Python", "max_stars_repo_path": "consts.py", "max_stars_repo_name": "saschapojot/kerrKitaevSpectrum", "max_stars_repo_head_hexsha": "e5eb6307e4e6993f376d86eae824201ed0419fe7", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#ifndef MULTI_NODE_WORKER_THREAD_H_
#define MULTI_NODE_WORKER_THREAD_H_
#include <boost/atomic.hpp>
#include <boost/thread.hpp>
#include <boost/unordered_map.hpp>
#include <string>
#include <vector>
#include "caffe/caffe.hpp"
#include "caffe/multi_node/msg.hpp"
#include "caffe/multi_node/sk_server.hpp"
#include "c... | {"hexsha": "26f8e81ad1a4290376ebe7537f89891612640fc2", "size": 6712, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/caffe/multi_node/worker_thread.hpp", "max_stars_repo_name": "AIROBOTAI/caffe-mnode", "max_stars_repo_head_hexsha": "e8b03bfb04f09dce21c9b5bbf66dacecb095d3e1", "max_stars_repo_licenses": ["BS... |
import argparse
import logging
import sys
from aoc import __version__
import numpy as np
__author__ = "Miguel Á. Lobato"
__copyright__ = "Miguel Á. Lobato"
__license__ = "MIT"
_logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG)
def is_card_winner(numbers, card):
t_mark_success = np.f... | {"hexsha": "39f0f563a355b50237f032a504875fef1c47dee0", "size": 1746, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/aoc/day4.py", "max_stars_repo_name": "miguellobato84/aoc2021", "max_stars_repo_head_hexsha": "c7651c7b273b513ed9399b6bcdc212655e481e98", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python
""" Larch Tests Version 1 """
import unittest
import time
import ast
import numpy as np
from sys import version_info
from ut_base import TestCase
from larch import Interpreter
class TestEval(TestCase):
'''testing of asteval'''
def test_function1(self):
"test function definition ... | {"hexsha": "4117e3c6dbfbbeb4052fcd5cccaac1bbed7b6f20", "size": 5950, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unittest_funccalls.py", "max_stars_repo_name": "xraypy/_xraylarch_attic", "max_stars_repo_head_hexsha": "a78a2d257bccb081ad15c43c831dee51d0b4845a", "max_stars_repo_licenses": ["BSD-3-Clause"... |
[STATEMENT]
lemma correctCompositionIn_prop1:
assumes "subcomponents PQ = {P,Q}"
and "correctCompositionIn PQ"
and "x \<in> (ins PQ)"
shows "(x \<in> (ins P)) \<or> (x \<in> (ins Q))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> ins P \<or> x \<in> ins Q
[PROOF STEP]
using assms
[PROOF STAT... | {"llama_tokens": 220, "file": "CryptoBasedCompositionalProperties_Secrecy", "length": 2} |
% Options for packages loaded elsewhere
\PassOptionsToPackage{unicode}{hyperref}
\PassOptionsToPackage{hyphens}{url}
\PassOptionsToPackage{dvipsnames,svgnames*,x11names*}{xcolor}
%
\documentclass[
]{krantz}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\ifnum 0\ifxetex 1\fi\ifluatex 1\... | {"hexsha": "ea09bbf300bd4523443d21f4f4a2bd95e694c427", "size": 113277, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "_book/bookdown.tex", "max_stars_repo_name": "dstanley4/psyc3250bookdown", "max_stars_repo_head_hexsha": "1623b074092bd36d4a31bda05fd5525d9c91dd22", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
export capacity, sample, SumTree
import StatsBase: sample
"""
SumTree(capacity::Int)
Efficiently sample and update weights.
For more detals, see the post at [here](https://jaromiru.com/2016/11/07/lets-make-a-dqn-double-learning-and-prioritized-experience-replay/).
Here we use a vector to represent the binary tree... | {"hexsha": "d0b669fc321890bc641f8e8e6bfa35efa8c2a6a3", "size": 3819, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Utils/sum_tree.jl", "max_stars_repo_name": "UnofficialJuliaMirror/ReinforcementLearning.jl-158674fc-8238-5cab-b5ba-03dfc80d1318", "max_stars_repo_head_hexsha": "5c10dba7fd85b15c8e10e826425c5be6... |
import mindboggle.guts
import numpy
import os.path
import scipy.io
import glob
import functools
from . import MultiprocPipeline as Pipeline
def read_vtk(filename) :
points, indices, lines, faces, depths, scalar_names, npoints, input_vtk = mindboggle.mio.vtks.read_vtk(filename)
return numpy.array(points), num... | {"hexsha": "5a68f3f6454c134e760f103f08cfc14763aa11ba", "size": 3339, "ext": "py", "lang": "Python", "max_stars_repo_path": "mindboggle/x/test_zernike/multiproc/test.py", "max_stars_repo_name": "cemlyn007/mindboggle", "max_stars_repo_head_hexsha": "947d4b3f41fb7a24c079550c7255c4d16939d740", "max_stars_repo_licenses": ["... |
module type_model_wrapper
use type_model, only: model_t
implicit none
!> A wrapper enabling arrays of abstract model_t classes.
!!
!! @author Nathan A. Wukie
!! @date 11/29/2016
!!
!!
!--------------------------------------------------------------------
type, public :: mo... | {"hexsha": "16d5054675f393c29acb8650e0a57e1239319e01", "size": 516, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/equations/type_model_wrapper.f90", "max_stars_repo_name": "wanglican/ChiDG", "max_stars_repo_head_hexsha": "d3177b87cc2f611e66e26bb51616f9385168f338", "max_stars_repo_licenses": ["BSD-3-Claus... |
\section{Dataset sources}\label{sec:dataset-sources}
This dataset is composed of 10 English instruction manuals with 453 pages detailing assembly operations of alternators, engines and gearboxes (more details shown in \cref{tab:dataset-sources_dataset-overview}). These object categories were selected because they have... | {"hexsha": "9ee87ecc9c39b466c14055e5108424d593a0049b", "size": 5117, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "article/tex/sections/dataset-sources.tex", "max_stars_repo_name": "carlosmccosta/Assembly-Named-Entity-Recognition-Dataset", "max_stars_repo_head_hexsha": "9dfc09f15ede9cfe9f819c7ef6b652a52f298100",... |
# This is modified from Upenn MEAM 620 course:
# https://alliance.seas.upenn.edu/~meam620/wiki/index.php
import numpy as np
import torch
import matplotlib.pyplot as plt
class qd_object:
"""
Struct to hold qd information
"""
def __init__(self):
self.pos = 0
self.vel = 0
self.eule... | {"hexsha": "fd8089dc9ca153cb33a12e45cf1f6813f72c6617", "size": 11440, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/quadrotor/controller_utils.py", "max_stars_repo_name": "thaipduong/SE3HamiltonianDynsLearning", "max_stars_repo_head_hexsha": "caf385cf810055e88314e6e4b39b566f9a0be419", "max_stars_repo_... |
struct Gtilde{Y<:AbstractVector{<:Real}, A<:Dirichlet,
B<:Union{Normal, <:OrderedNormalMeanPrior}, C<:Real,
D<:LogNormal, E<:Normal, F<:Gamma} <: MCMC.Model
yC::Y # finite log expressions from control group
yT::Y # finite log expressions from treatment group
K::Int # number of mixt... | {"hexsha": "e9f953264d29ab7d088171c962250ad06e0ba44e", "size": 4268, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/models/Gtilde.jl", "max_stars_repo_name": "luiarthur/CytofDiffDensity.jl", "max_stars_repo_head_hexsha": "11370917a13a8291e096fe2dcb81644ba01d399b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import os
from sklearn.model_selection import train_test_split
import numpy as np
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, Input, Add, GlobalAveragePooling2D, DepthwiseConv2D... | {"hexsha": "b1043c734834d1a40af4682e03d47229a1c8307a", "size": 8370, "ext": "py", "lang": "Python", "max_stars_repo_path": "asl.py", "max_stars_repo_name": "dexhunter/ObfGAN", "max_stars_repo_head_hexsha": "5b20c4e79eb52923a9d69e3d0584165829f611c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_star... |
# -*- coding: utf-8 -*-
"""Evaluating NoTram models from the National Library of Norway: NER and POS
Copyright 2020 © National Library of Norway
Evaluating NoTram models from the National Library of Norway: NER and POS
"""
# Dependencies
# !pip -q install https://github.com/huggingface/transformers/archive/0ecbb... | {"hexsha": "a1a69d4202e840d450d57f491459aca93ee80ce2", "size": 29742, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/notram_eval.py", "max_stars_repo_name": "NbAiLab/notram", "max_stars_repo_head_hexsha": "0c90d6b28008df514c4ac847e4c9d68f4709a181", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import pickle
from pathlib import Path
import cv2
import numpy as np
__all__ = ["Camera", "CALIBRATION_DATA_PATH"]
CALIBRATION_DATA_PATH = "camera_cal/calibration.dat"
class Camera:
@staticmethod
def undistort(distorted_img, calibration_data, **kwargs):
(_, mtx, dist, _, _) = calibration_data
... | {"hexsha": "d9f442331818a0ec483e964d991690839ff5a926", "size": 1840, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/camera.py", "max_stars_repo_name": "yashgorana/lane-detection-advanced", "max_stars_repo_head_hexsha": "83201bc275e7a767220fb478dd902e3b96b39e68", "max_stars_repo_licenses": ["MIT"], "max_star... |
%!TEX root = TDT4265-Summary.tex
\section{Representation and description}
The results of image segmentation (Section \ref{sec:segmentation}) can be represented and described in certain ways to be useful for computers. For instance, a region can be represented by its boundary, and the boundary described by its lenght.... | {"hexsha": "6bb53422a9ac707c6a0b996d5ff2a35d48bddf85", "size": 13908, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "TDT4265 Computer vision/11-representation-and-description.tex", "max_stars_repo_name": "jakoblover/ntnu-course-summaries", "max_stars_repo_head_hexsha": "8ba859de2349b93c5079ca10a4cf2ec49c1f5dc0", ... |
module geos5_io_module
use hdf5
implicit none
contains
subroutine find_geos5_bounds(istart, iend, jstart, jend, lat, lon)
integer, intent(inout) :: istart, iend, jstart, jend
real, dimension(:,:), intent(in) :: lat, lon
real :: minLat, maxLat, minLon, maxLon
real, parameter :: dlon = 5./16.
real, ... | {"hexsha": "3d90379f3e09dadb980a47ff13db30d2cf0f64f5", "size": 3799, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/Components/MCARS/SFC_APP/geos5_io_module.f90", "max_stars_repo_name": "GEOS-ESM/AeroApps", "max_stars_repo_head_hexsha": "874dad6f34420c014d98eccbe81a061bdc0110cf", "max_stars_repo_licenses"... |
# Copyright 2019 Google LLC
#
# 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": "480b139569bfbfc4148fd6d7f172b96a83dedaa5", "size": 36395, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow_data_validation/statistics/generators/basic_stats_generator.py", "max_stars_repo_name": "brills/data-validation", "max_stars_repo_head_hexsha": "4f8a5d12b3d5db7383ae53d5fe184af1d781449... |
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing... | {"hexsha": "d1ba5dca5b027c3924f9ffd187c16c576673c6d5", "size": 10208, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/visualizations.py", "max_stars_repo_name": "jeffreyhwatson/terry_stops_project", "max_stars_repo_head_hexsha": "9aa82ee4c2148e7f675d6eea5ab24409d0f2b129", "max_stars_repo_licenses": ["CC-BY-2... |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import os
import csv
import datetime
import pickle
from PIL import Image
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
class Conv4Net_1Channel_Narrow(nn.Module):
def __init__(self, d... | {"hexsha": "5f8ccb16757c4eb69a95fcbc6b250afc656d0985", "size": 18882, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/CNNArchitectures.py", "max_stars_repo_name": "ianscottknight/leaf-count", "max_stars_repo_head_hexsha": "bfefadfdb6aaab141d526ad3bbdfc5f89138c60c", "max_stars_repo_licenses": ["FTL"], ... |
(** 以下代码会预先导入关于整数的定义、证明以及自动证明指令。*)
Require Import Coq.ZArith.ZArith.
Require Import Coq.micromega.Psatz.
Local Open Scope Z.
(** * 归纳类型的又一个例子：二叉树 *)
Inductive tree: Type :=
| Leaf: tree
| Node (l: tree) (v: Z) (r: tree): tree.
(** 这个定义说的是，一棵二叉树要么是一棵空树_[Leaf]_，要么有一棵左子树、有一棵右
子树外加有一个根节点整数标号。Coq中，我们往往可以使用递归函数定义归纳... | {"author": "gzqaq", "repo": "CS2612-PLaC", "sha": "fb7be0651785905b60d3e705324175daaadcc96b", "save_path": "github-repos/coq/gzqaq-CS2612-PLaC", "path": "github-repos/coq/gzqaq-CS2612-PLaC/CS2612-PLaC-fb7be0651785905b60d3e705324175daaadcc96b/assigns/assign0916/CoqInductiveType.v"} |
"""
A simple Python module to obtain energy levels of superconducting qubits by sparse Hamiltonian diagonalization.
"""
import numpy as np
import sympy
from scipy.sparse.linalg import *
from abc import ABCMeta
from abc import abstractmethod
import logging
import scqubits.core.constants as constants
import scqubits.co... | {"hexsha": "fae4b25fa068c67fc699b3261df4c4a13e08971b", "size": 37408, "ext": "py", "lang": "Python", "max_stars_repo_path": "scqubits/core/circuit/circuit.py", "max_stars_repo_name": "IlyaLSMmisis/scqubits-1", "max_stars_repo_head_hexsha": "c4915c998c2d1ef3348db0e4c423e74c7181da40", "max_stars_repo_licenses": ["BSD-3-C... |
@with_kw mutable struct MPCOptions{T}
"Maximum horizon of the MPC"
h::T=3.0
"Maximum number of MPC steps"
M::Int=300
end
mutable struct MPCStatistics{T}
iter::Int
t::T
dt::Vector{T}
traj::Vector{Algames.KnotPoint}
end
function MPCStatistics()
iter = 0
t = 0.0
dt = zeros(0)... | {"hexsha": "909b0e459b3e852ed463b1cb0cfd354da72da318", "size": 1526, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mpc/mpc_struct.jl", "max_stars_repo_name": "simon-lc/AlgamesPlots.jl", "max_stars_repo_head_hexsha": "18851ea53168bbd1ab5c1c7f1116f8194d2c3091", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from nbodykit.lab import cosmology as nbodykit_cos
from power_spectrum import PowerSpec
from rescale import Rescale
from scipy.optimize import minimize
class Cosmo(object):
"""
Cosmology class, used for finding a set of rescaled cosmological parameters
Args:
z: snapshot... | {"hexsha": "0d188b47ad8eb059d03b15285b8ac453ed90fe52", "size": 5358, "ext": "py", "lang": "Python", "max_stars_repo_path": "get_cosmo.py", "max_stars_repo_name": "amjsmith/rescale-cosmology", "max_stars_repo_head_hexsha": "1980fa89a4acc95e2a1effb3b0939af8e8dca275", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
SUBROUTINE PD_CAPE ( zbot, ztop, thbot, thtop, thpbot, thptop,
+ cape, cins )
C************************************************************************
C* PD_CAPE ( ZBOT, ZTOP, THBOT, THTOP, THPBOT, THPTOP, CAPE, CINS ) *
C* *
C* This routine calculates CAPE & CINS for a specifie... | {"hexsha": "baca7426a6f30a5d75295a6a1e75279b2f9042a2", "size": 1977, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/diaglib/pd/pdcape.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
///////////////////////////////////////////////////////////////////////////////
// BSD 3-Clause License
//
// Copyright (C) 2019-2020, LAAS-CNRS, New York University, Max Planck Gesellschaft,
// University of Edinburgh
// Copyright note valid unless otherwise stated in individual files.
// All ... | {"hexsha": "92f6f00632fc47516818c330a66742b34f011e2d", "size": 2545, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/crocoddyl/core/solvers/kkt.hpp", "max_stars_repo_name": "pFernbach/crocoddyl", "max_stars_repo_head_hexsha": "cbf81a329e3abaf4ce1b4a8fab1431f93cd9a5c8", "max_stars_repo_licenses": ["BSD-3-Cl... |
"""
chain = mcmc(θ, reps, burnin, Prior, lnL, Proposal)
Simple MH MCMC
You must set the needed functions, e.g.,:
Prior = θ -> your_prior(θ, whatever other args)
lnL = θ -> your_log_likelihood(θ, whatever other args)
Proposal = θ -> your_proposal(θ, whatever other args)
(optionally) mcmcPr... | {"hexsha": "04a4043045ee1904c059aab413bb5a89ca0b9e34", "size": 4540, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Bayesian/mcmc.jl", "max_stars_repo_name": "mcreel/Econometrics", "max_stars_repo_head_hexsha": "f87efde84de6121afa9908b64961ca97e53e251c", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
from matplotlib import pyplot as plt
from scipy import stats
import figlatex
sigma = 1
c = 0.99 * sigma ** 2
deltau = sigma / 100
N = 5 * sigma / deltau
u = np.linspace(0, 5 * sigma, 1000)
###########################
def pcross0(u, sigma):
return stats.norm.sf(u, scale=sigma)
def pcross1(u, ... | {"hexsha": "7ce98346f17a47e0eea749cc1a1b9ad98e06f7f3", "size": 1482, "ext": "py", "lang": "Python", "max_stars_repo_path": "figthesis/figcrossingprob.py", "max_stars_repo_name": "Gattocrucco/sipmfilter", "max_stars_repo_head_hexsha": "74215d6c53b998808fc6c677b46030234d996bdf", "max_stars_repo_licenses": ["CC-BY-4.0", "... |
#!/usr/bin/env python3
#
# Copyright (c) Bo Peng and the University of Texas MD Anderson Cancer Center
# Distributed under the terms of the 3-clause BSD License.
import os
import sys
import unittest
from ipykernel.tests.utils import execute, wait_for_idle
from sos_notebook.test_utils import (get_display_data, get_std... | {"hexsha": "d0b38e6212d597d6af5a3aa4f4413eb431766e54", "size": 10640, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_sos_magics.py", "max_stars_repo_name": "aadithpm/sos-notebook", "max_stars_repo_head_hexsha": "62ac5d56a12b4ce2d4aaf4c60311e6a85c197baa", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
from worldBuilderTest import Rectangle
from naiveRules import *
import numpy as np
#OLD but works. Will be replaced soon.
# Cross space is a transformation from icon space. Here instead of keepeing track of where
# icons are, it keeps track of the connections between icons.
# This space will further be used as for ... | {"hexsha": "7b228c49e9a80ceb0ab3d7d6a2d6d7a34fde81b3", "size": 2299, "ext": "py", "lang": "Python", "max_stars_repo_path": "NaivecrossStateSpace.py", "max_stars_repo_name": "Sjokoladepapir/iconbounce", "max_stars_repo_head_hexsha": "74a868aff9e4da49a952b98f3e6738d3aa4eda56", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
def solve(puzzle: np.ndarray) -> np.ndarray:
return puzzle
| {"hexsha": "a22f13c3253ab1b6cff98d414d7699238bdadcc8", "size": 84, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/decrypter/solver.py", "max_stars_repo_name": "headma5ter/decrypter", "max_stars_repo_head_hexsha": "35cce659caa87943cc5586181f0b5df0f2ea43f3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""Contains the n dimensional inverted pendulum environment."""
import warnings
from typing import Optional
import matplotlib.pyplot as plt
import numpy as np
from numpy import ndarray
from polytope import polytope
from scipy.integrate import ode
from scipy.spatial.qhull import ConvexHull
from ..utils import assert_s... | {"hexsha": "b6f376b60a00ab17f19cd60c626a3bd2ccf60f7f", "size": 12589, "ext": "py", "lang": "Python", "max_stars_repo_path": "safe_exploration/environments/ndpendulum.py", "max_stars_repo_name": "oscarkey/safe-exploration", "max_stars_repo_head_hexsha": "32f0582a7b54ab7d4c1d415afbcf5e9554e8bcec", "max_stars_repo_license... |
#pragma once
#include "boost_defs.hpp"
#include "logger.hpp"
#include <boost/functional/hash.hpp>
#include <cstdint>
#include <ostream>
namespace krbn {
class mouse_key final {
public:
mouse_key(void) : x_(0),
y_(0),
vertical_wheel_(0),
horizontal_wheel_(... | {"hexsha": "e2669eb22ef9b1092c5839a47e76fa0037318f0b", "size": 4889, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/share/types/mouse_key.hpp", "max_stars_repo_name": "cyrusccy/Karabiner-Elements", "max_stars_repo_head_hexsha": "90f83e487a0b6c671bc76f48c01e91fb28ae67c2", "max_stars_repo_licenses": ["Unlicense... |
from keras.utils import Sequence
import numpy as np
from dataset import convert_corpus
class BatchGenerator(Sequence):
"Generator for Keras"
def __init__(self):
self.vectors = MagitudeVectors(emdim).load_vectors()
with open(self.input_file, 'r', encoding="utf-8") as f:
... | {"hexsha": "c9d68ad5f0dd54ac7a6d3cebe9db147d3b347118", "size": 1282, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/batch_generator.py", "max_stars_repo_name": "gajanlee/V-net", "max_stars_repo_head_hexsha": "feba60bdb2688041fb4fe2c6970ca1b1505e0a65", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.spatial import distance
from sklearn.cluster import KMeans
class Pattern:
def __init__(self, img, num_colors):
self.img = img
self.num_colors = num_colors
def import_colormap(self):
clr_df = pd.re... | {"hexsha": "c6243afc8a1c9885af27d948bb71d7ff92d452fe", "size": 3744, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pattern/pattern.py", "max_stars_repo_name": "lransohoff/CrossStitchPatternGen", "max_stars_repo_head_hexsha": "5ac17ab60aefaf439c640baea1b9657f8d263880", "max_stars_repo_licenses": ["MIT"], "max_s... |
SUBROUTINE SOLGEN(KCOORD,TSEC50,RSUN,IFLAG)
IMPLICIT REAL*8(A-H,O-Z)
C
C CALCULATE THE POSITION OF THE SUN IN A SPECIFIED COORDINATE SYSTEM
C
C VARIABLE TYPE I/O DESCRIPTION
C -------- ---- --- -----------
C
C KCOORD I*4 I FLAG IDICATING WHICH COORDINATE SYSTEM IS WANTED.
C
C = 1, GEO... | {"hexsha": "9ec4db19c9d9882b393a88e0d0395448f1be8598", "size": 1576, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "gsc-13083/solgen.for", "max_stars_repo_name": "SteveDoyle2/nasa-cosmic", "max_stars_repo_head_hexsha": "c8015a9851a04f0483b978d92c2cbaee31c81fe3", "max_stars_repo_licenses": ["BSD-Source-Code"],... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2020 Patrick Lumban Tobing (Nagoya University)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
from __future__ import division
from distutils.util import strtobool
import argparse
import logging
import math
import os
import sys
import time
import... | {"hexsha": "53b61fb2c004b0aa1d4bbd07dc9c752b4a6d0dda", "size": 13984, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/bin/decode_wavernn_dualgru_compact_lpc.py", "max_stars_repo_name": "ml-applications/cyclevae-vc-neuralvoco", "max_stars_repo_head_hexsha": "a1976c127eaf9d2a3ef7a8a783839743ffb69c5c", "max_sta... |
\documentclass[main.tex]{subfiles}
\begin{document}
\section{Chapter 1}
This is Chapter 1, provided in a sub file.
\end{document}
| {"hexsha": "9b3f38f589fd6c73f26f744838705158b58e80ed", "size": 134, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Tests/files/command/sub-file-chapter-1.tex", "max_stars_repo_name": "zspitz/PandocFilters", "max_stars_repo_head_hexsha": "a73d25afa28e5db69cddcd66822ed93cc637be8b", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from dgl import topological_nodes_generator
from copy import deepcopy
from collections import OrderedDict
#
#
#
class resnet(nn.Module):
def __init__(self, in_dim, hidden_dim, out_dim=None):
super(resnet, self).__... | {"hexsha": "b5761c8a4db70fae1444780db584634dd3ecfaa1", "size": 17501, "ext": "py", "lang": "Python", "max_stars_repo_path": "PFPO/model/bignn.py", "max_stars_repo_name": "phillipcpark/PredictiveFPO", "max_stars_repo_head_hexsha": "1fbbccd8b01056ef124960e5a0d214690a007dc3", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Licensed under a 3-clause BSD style license - see LICENSES
import numpy as np
import pytest
import sncosmo
try:
from matplotlib.figure import Figure
HAS_MATPLOTLIB = True
except:
HAS_MATPLOTLIB = False
@pytest.mark.skipif('not HAS_MATPLOTLIB')
class TestPlotLC:
def setup_class(self):
# Crea... | {"hexsha": "3de8ffac99f1985844cbe4a8eea8e05a93b3c642", "size": 791, "ext": "py", "lang": "Python", "max_stars_repo_path": "sncosmo/tests/test_plotting.py", "max_stars_repo_name": "rbiswas4/sncosmo", "max_stars_repo_head_hexsha": "813b707044fd21e8e35e7a1cdc650b48417f0ebc", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
"""
PyTorch의 LSTM을 사용해보기 위한 연습용 코드
Usage:
python PyTorch_LSTM_Sample.py
"""
import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
import os, sys
import matplotlib.pyplot as plt
class Net(nn.Module):
"""
신경망 구성 클래스
"""
def __init__(self, input_size, hidden_... | {"hexsha": "b9315bec2aea6ba77978ed3005f9756a72c92629", "size": 3611, "ext": "py", "lang": "Python", "max_stars_repo_path": "library_review/PyTorch_LSTM_Sample.py", "max_stars_repo_name": "zzong2006/space-filling-curve-with-RF-learning", "max_stars_repo_head_hexsha": "30823745dae91240c0977185fb1831c9b4771a40", "max_star... |
# ******************************************************************************************
# Notices:
#
# Copyright © 2022 United States Government as represented by the Administrator of the
# National Aeronautics and Space Administration. All Rights Reserved.
#
# Disclaimers
#
# No Warranty: THE SUBJECT SOFTWARE IS... | {"hexsha": "eb4076cef2bb038b79e967a9ced90498b1f1b5d6", "size": 7485, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/solvers/global/SoftActorCritic/src/distributed.jl", "max_stars_repo_name": "NASA-SW-VnV/AdaStress.jl", "max_stars_repo_head_hexsha": "a8802eeb2c7890a100ff87470853b7d1acda03fb", "max_stars_repo_... |
// Copyright 2020 Gareth Cross
#pragma once
#include <gtest/gtest.h>
#include <Eigen/Dense>
// Numerical tolerances for tests.
namespace tol {
static constexpr double kDeci = 1.0e-1;
static constexpr double kCenti = 1.0e-2;
static constexpr double kMilli = 1.0e-3;
static constexpr double kMicro = 1.0e-6;
static conste... | {"hexsha": "3382278225503799d75021496afad7ca0b9426de", "size": 3765, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "test/test_utils.hpp", "max_stars_repo_name": "gareth-cross/geometry_utils", "max_stars_repo_head_hexsha": "cc687d19559c2055b68e7f8708af3595e7f93917", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Test that basic function works and that it doesn change the mean
@test all(findcenteredmean(m, only_positive = true) .>= 0)
@test all(getmean(m) .== means)
# Try different centers and changing the mean
@test isa(findcenteredmean(m, center = :analytic), Vector)
@test all(findcenteredmean(m, center = :analytic, only_p... | {"hexsha": "968b424db99068203b9f06ac12b0cdc37fa68f0d", "size": 576, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/find_center.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/FlexibilityAnalysis.jl-a86d3b49-e43c-5f8d-b693-d32ec726be17", "max_stars_repo_head_hexsha": "25cea23289e03630a293d6b96d9f... |
/**************************************************************************\
|
| Copyright (C) 2009 Marc Stevens
|
| This program is free software: you can redistribute it and/or modify
| it under the terms of the GNU General Public License as published by
| the Free Software Foundation, either version 3 of... | {"hexsha": "3178b009b12517685346f413f1326b9858c81afa", "size": 5637, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/md5backward/backward.cpp", "max_stars_repo_name": "killua4564/hashclash", "max_stars_repo_head_hexsha": "f780f17ef579e4bb246f5c47f31765f665dab74f", "max_stars_repo_licenses": ["MIT"], "max_stars... |
'''
ResourceAllocationTasks.py : basic resource allocation tasks
Cem Karaoguz, 2020
MIT License
'''
import numpy as np
class JacksCarRental:
N_CARS_MAX_REQUESTS = 7
N_CARS_MAX_RETURNS = 7
def __init__(self, nCarsMaxA=20, nCarsMaxB=20, nCarsMaxRelocate=5, lambdaCarRequestA=3,
lambdaCarRequestB=4, lamb... | {"hexsha": "b97ad6578fbb8213036f3d2f0d3c793ed77ba302", "size": 10214, "ext": "py", "lang": "Python", "max_stars_repo_path": "IRL/environments/ResourceAllocationTasks.py", "max_stars_repo_name": "cemkaraoguz/reinforcement-learning-an-introduction-second-edition", "max_stars_repo_head_hexsha": "735bfa6b66ffb52b7cf0396616... |
'''IO functions for various formats used: trace, sinex etc '''
import glob as _glob
import re as _re
import zlib
from io import BytesIO as _BytesIO
import logging
import numpy as _np
import pandas as _pd
from p_tqdm import p_map as _p_map
from p_tqdm.p_tqdm import tqdm as _tqdm
from ..gn_const import PT_CATEGORY, TY... | {"hexsha": "7df67fafb03f2398938ce729a863154edb893aa5", "size": 17221, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/gn_lib/gn_io/sinex.py", "max_stars_repo_name": "umma-zannat/ginan", "max_stars_repo_head_hexsha": "a4d1a3bb8696267f23d26e8c6a2f6080b87bb494", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
[STATEMENT]
lemma proots_within_iff[simp]:
"x\<in>proots_within p s \<longleftrightarrow> poly p x=0 \<and> x\<in>s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (x \<in> proots_within p s) = (poly p x = (0::'a) \<and> x \<in> s)
[PROOF STEP]
unfolding proots_within_def
[PROOF STATE]
proof (prove)
goal (1 subgoa... | {"llama_tokens": 204, "file": "Budan_Fourier_BF_Misc", "length": 2} |
import os
import torch
import random
from data_utils.utils import numpy_seed
from torch.utils import data
import numpy as np
from data_utils import indexed_dataset
class CLMTaskDataset(data.Dataset):
def __init__(self, path, tokenizer, batch_size, max_tokens, world_size=1, max_lens=510, seed=512, no_cache=Fals... | {"hexsha": "b5244d5403fe13778966721fd1f0c7456168afe3", "size": 6142, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_utils/test_gpt_datasets.py", "max_stars_repo_name": "initc/gpt-lm", "max_stars_repo_head_hexsha": "941f2816d7a749ea3a3e0c574b35fc3fc67e94e3", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# -*- coding: utf-8 -*-
"""
Created on Thu May 14 06:55:06 2020
@author: lcovarrubias, eledgarmurillo
"""
# TEST Variance
import pylab as pylab
import numpy as numpy
import minoritymodel as minmod
fig = pylab.figure(1,figsize=(6,4))
for n in range(5):
sim = minmod.System(T=1000,N=101, m=3,s=2)
sim.run()
... | {"hexsha": "0e466f172d3dd85bedd62b4728a8329e2117b352", "size": 1773, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_variance_vs_m.py", "max_stars_repo_name": "LeoCovarrubias/MinorityGame", "max_stars_repo_head_hexsha": "c923db08649804b94c1aae68f822f3b1d770e41a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from __future__ import division, print_function
import os
import numpy as np
import yaml
from visual_dynamics import envs
from visual_dynamics import policies
from visual_dynamics.utils.config import ConfigObject
from visual_dynamics.utils.rl_util import do_rollouts, discount_returns
class Algorithm(ConfigObject):... | {"hexsha": "2d2367e87bc938d85374d4cf539eee31f69aea99", "size": 6445, "ext": "py", "lang": "Python", "max_stars_repo_path": "visual_dynamics/algorithms/base.py", "max_stars_repo_name": "alexlee-gk/visual_dynamics", "max_stars_repo_head_hexsha": "90227bb0d0aebb1989117b5c25ca311655ca7cc7", "max_stars_repo_licenses": ["MIT... |
import re
import math
import bisect
import warnings
import time
from datetime import datetime, timedelta
from inspect import signature, getsourcelines
from collections import namedtuple
import numpy as np
from scipy import integrate
from scipy import linalg
import matplotlib.pyplot as plt
from mpl_toolkit... | {"hexsha": "1f4ea8a97319a38ca4c99298e2caec53769108f4", "size": 4816, "ext": "py", "lang": "Python", "max_stars_repo_path": "Test2.py", "max_stars_repo_name": "fakeAEmajorRosen/RocketPy_Rosen", "max_stars_repo_head_hexsha": "27211e7952891bdffa71a4ecca29070d98056794", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ----------------------------------------------------------------------... | {"hexsha": "f141bfd235754189a3823a985d387b3a8c16c5d6", "size": 7781, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyStationB/libraries/GlobalPenalisation/gp/base/sequential.py", "max_stars_repo_name": "BrunoKM/station-b-libraries", "max_stars_repo_head_hexsha": "ea3591837e4a33f0bef789d905467754c27913b3", "max... |
Require Import Coq.ZArith.ZArith.
Require Import Coq.Lists.List.
Require Import Coq.Strings.String.
Require Import Crypto.Bedrock.Field.Common.Types.
Require Import Crypto.Bedrock.Field.Translation.Expr.
Require Import Crypto.Language.API.
Require Import Crypto.Util.Option.
Require Import Crypto.Util.Notations.
Import ... | {"author": "dip-proto", "repo": "fiat-crypto", "sha": "fc3a9280c51f413943c167cc9292e953b8e42c02", "save_path": "github-repos/coq/dip-proto-fiat-crypto", "path": "github-repos/coq/dip-proto-fiat-crypto/fiat-crypto-fc3a9280c51f413943c167cc9292e953b8e42c02/src/Bedrock/Field/Translation/Cmd.v"} |
import numpy as np
#-----------------------------------------
# VMF model
class Metrics(object):
def __init__(self):
pass
from collections import Counter
class Multinomial(object):
def __init__(self, train, n_states=21):
self.vmf = [dict(Counter(row)) for row in train.T]
self.n_state... | {"hexsha": "7c546fb481deb391a278fe9f8cced7ccba2fefe7", "size": 2801, "ext": "py", "lang": "Python", "max_stars_repo_path": "multinom/mult.py", "max_stars_repo_name": "smoitra87/deepnet", "max_stars_repo_head_hexsha": "c4f89c65f78298d846bd6dc0654b9c8f5e223f2b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
# Autogenerated wrapper script for LAME_jll for powerpc64le-linux-gnu
export lame, libmp3lame
JLLWrappers.@generate_wrapper_header("LAME")
JLLWrappers.@declare_executable_product(lame)
JLLWrappers.@declare_library_product(libmp3lame, "libmp3lame.so.0")
function __init__()
JLLWrappers.@generate_init_header()
JL... | {"hexsha": "1fa8123e8db8f2d39066b9234e787f7ca41dd4ab", "size": 585, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/powerpc64le-linux-gnu.jl", "max_stars_repo_name": "JuliaBinaryWrappers/LAME_jll.jl", "max_stars_repo_head_hexsha": "188394f057ccb647fde57abd4eba52f9fba7faf2", "max_stars_repo_licenses":... |
#!/usr/bin/python3
# -*- coding: UTF-8 -*-
__author__ = 'zd'
import numpy as np
import jieba
from jieba import analyse
import joblib
import global_parameters as config
from data_loader import get_sentences
import data_utils
def sentence_vector(sentence, stop_words, model):
"""
生成句向量方法
根据词向量模型 和 给定句子 生成句... | {"hexsha": "dc1be3b82bc7b48e1e41e1edc8d94efaa5e61288", "size": 3918, "ext": "py", "lang": "Python", "max_stars_repo_path": "\u6587\u672c\u6458\u8981/text_rank/model_utils.py", "max_stars_repo_name": "zhangdddong/beautifulNLP", "max_stars_repo_head_hexsha": "295987cc03c9afb52008917d9d141fdb2eb66ba5", "max_stars_repo_lic... |
"""ODE solver by Euler method"""
from numbers import Real
"""
# notes
$$a_1 \equiv
1 - h_i^{guess}\partial_{x}F|_{\left(t_{i},x_{i}\right)}$$
$$a_2 \equiv
1 - h_i^{guess}\partial_{x}F|_{\left(t_{i}+h_{i}^{guess},x_{i}\right)}$$
$a_1$ and $a_2$ should be positive.
"""
def get_stepsize(ti, xi, Fx, dxFx, hi_max... | {"hexsha": "937625f35a9986b8a258a98ff7cd791c35b757e9", "size": 7113, "ext": "py", "lang": "Python", "max_stars_repo_path": "bohm/ode/euler.py", "max_stars_repo_name": "jam31118/bohm", "max_stars_repo_head_hexsha": "f842aa716f48240ceafacc6f815ae0687a965f5d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import os
import sys
import cv2
import numpy as np
from copy import deepcopy
from scipy.spatial.transform import Rotation as rot
import torch, torchvision
from time import time
import math
import h5py
import json
import random
import argparse
from const import KPTS_15, SMPL_KPTS_15
from data_utils import sample_projec... | {"hexsha": "ef8b70f419321ba24f9ab7e0b7009b36c73c9673", "size": 20881, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/prepare_datasets.py", "max_stars_repo_name": "kristijanbartol/gender-classifier", "max_stars_repo_head_hexsha": "6eccd89722989e0d1f3c6a2fbf59a4e3f40349c7", "max_stars_repo_licenses": ["MIT"],... |
import pandas
import matplotlib.pyplot as plt
import numpy
import sympy
# plt.style.use("grayscale")
plt.rcParams.update({'font.size': 16})
def calculate_reciprocals(n):
return [1/i for i in range(1, n + 1)]
def calculate_harmonic_series(n):
series = [1]
for i in range(1, n):
series.append(series[i - 1] + 1 / (... | {"hexsha": "c08fee31d48b70ae3fd571de074a039a3a90c84a", "size": 2650, "ext": "py", "lang": "Python", "max_stars_repo_path": "harmonic_numbers.py", "max_stars_repo_name": "bernatfogarasi/coupon-collector", "max_stars_repo_head_hexsha": "91fd9bce0b2a98d8f6fa58cf9fc774513d43123e", "max_stars_repo_licenses": ["MIT"], "max_s... |
(*
Author: Mohammad Abdulaziz, Fred Kurz
*)
theory STRIPS_Representation
imports State_Variable_Representation
begin
section "STRIPS Representation"
(*<*)
type_synonym ('variable) strips_state = "('variable, bool) state"
(*>*)
text \<open> We start by declaring a \isakeyword{record} for STRIPS operators.
This wh... | {"author": "zabihullah331", "repo": "barakzai", "sha": "793257c1d71ec75a299fc6b5843af756ead2afb0", "save_path": "github-repos/isabelle/zabihullah331-barakzai", "path": "github-repos/isabelle/zabihullah331-barakzai/barakzai-793257c1d71ec75a299fc6b5843af756ead2afb0/thys/Verified_SAT_Based_AI_Planning/STRIPS_Representatio... |
#!/home/zhuqingjie/env/py3_tf_low/bin/python
'''
@Time : 07.08 0008 下午 07:25
@Author : zhuqingjie
@User : zhu
@FileName: modelbuilder.py
@Software: PyCharm
'''
import json
import numpy as np
import os
import tensorflow as tf
import time
from train import G
os.environ['CUDA_VISIBLE_DEVICES'] = ''
# param
model... | {"hexsha": "0bcbd5ab779e6b1e70021986dfceea6721ad1fda", "size": 1900, "ext": "py", "lang": "Python", "max_stars_repo_path": "modelbuilder.py", "max_stars_repo_name": "azzhu/deeps", "max_stars_repo_head_hexsha": "dda178497be3d62067a2f2a7a0a5aa1d793a89bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
import numpy as np
class ParticulatesEmissionsModel:
"""
Calculate particulates emissions based on the method described in:
https://www.eea.europa.eu/ds_resolveuid/6USNA27I4D
and further disaggregated in:
https://doi.org/10.1016/j.atmosenv.2020.117886
Include emission from:
- brake wear... | {"hexsha": "a789f1e8edf56c257b306cd226ad2e9da5958239", "size": 6621, "ext": "py", "lang": "Python", "max_stars_repo_path": "carculator/particulates_emissions.py", "max_stars_repo_name": "vishalbelsare/carculator", "max_stars_repo_head_hexsha": "44516a5f3e7f7f42f0d0d7a5c2bd5af3d17d0fd4", "max_stars_repo_licenses": ["BSD... |
using Pkg.Artifacts
using Pkg.BinaryPlatforms
using URIs
function tectonic()
pkgname = "tectonic"
origin = "https://github.com/tectonic-typesetting/tectonic/releases/download"
version = v"0.8.0"
build = 1
downloads = Dict(
"$origin/tectonic%40$version/tectonic-$version-x86_64-unknown-linux... | {"hexsha": "6d16cbfed156be167499b11f7da460d181258341", "size": 4269, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "build.jl", "max_stars_repo_name": "MichaelHatherly/Tectonic.jl", "max_stars_repo_head_hexsha": "2e242fcbe63ab9631ed5fc8a8c374d9f5338512f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19,... |
from __future__ import print_function, absolute_import
from numba import unittest_support as unittest
from numba.ocl.ocldrv.driver import driver as cl
from numba.ocl.ocldrv.devices import _runtime as rt
from numba.ocl.ocldrv import spirv
from numba.ocl.ocldrv import spir2
sample_spir = """
; ModuleID = 'kernel.out.bc... | {"hexsha": "464cb13554ef69aa3956873449a2dc4614e35458", "size": 2305, "ext": "py", "lang": "Python", "max_stars_repo_path": "numba/ocl/tests/ocldrv/test_spir_loading.py", "max_stars_repo_name": "SPIRV/NUMBA", "max_stars_repo_head_hexsha": "6b93f44c923e7bf8cd9f95cc5188bba3aea4e75d", "max_stars_repo_licenses": ["BSD-2-Cla... |
from typing import List, Optional, Union
import numpy as np
import sympy
from openfermion import IsingOperator, QubitOperator
from openfermion.utils import count_qubits
from overrides import overrides
from zquantum.core.circuits import Circuit, H, create_layer_of_gates
from zquantum.core.circuits.symbolic import natur... | {"hexsha": "3e2863fec9afe2274174878f30fb753895cf50c2", "size": 5263, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/zquantum/qaoa/ansatzes/farhi_ansatz.py", "max_stars_repo_name": "zapatacomputing/z-quantum-qaoa", "max_stars_repo_head_hexsha": "a13a99939ee41c760fdfb302e5f4944e087a09a7", "max_stars_re... |
'''
This program reads a FAMUS file, (should run shift-idx.py first)
and a list of changes (u,v,w,m).
It finds the (u,v) tower and changes the w-th slice to value m
where (u,v) are indices as defined by mag-map.py contours
and w indexes slices from [1,N].
The m value sets pho, a scalar between... | {"hexsha": "07e42c0467be5d0825cd148a666c63703315851d", "size": 2261, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/3d-zot/3d-zot.py", "max_stars_repo_name": "tmqian/MUSE", "max_stars_repo_head_hexsha": "164e3ad8c7345f55ef4c8f0584155a2d3d7fbe2f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
from .function import (
abs, max, min, log, exp, relu, sigmoid, softmax, tanh,
)
from .random import (
normal, normal_like,
uniform, uniform_like,
)
from .tensor import (
Tensor,
tensor, as_tensor, stack,
zeros, zeros_like, ones, ones_like,
add, sub, neg, mul, truediv, matmul, power, dot,
... | {"hexsha": "de51b289e7b627a9690d5febc1d1dfbf0cd4364c", "size": 502, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dpln/autograd/__init__.py", "max_stars_repo_name": "shizuku/dpln", "max_stars_repo_head_hexsha": "d6f62e97073313a92ba492bbf1b9cd57842a8369", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
export SU2
"""
Represents SU(2).
fields: u and v.
"""
struct SU2
a::Real
v::Complex
end
| {"hexsha": "8ddda0b4132ede6b0c3460f48243e03bac7ef1ae", "size": 103, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/symmetrygroups_tests/su3_tests.jl", "max_stars_repo_name": "SimonDanisch/Porta.jl", "max_stars_repo_head_hexsha": "70a5b6586b74f5d76d3add8c9f305071dea13b6c", "max_stars_repo_licenses": ["MIT"],... |
from cnn import Env
import torch
import numpy as np
import matplotlib.pyplot as plt
def train_model(model, criterion, optimizer, train_loader, valid_loader, model_name='model.pt'):
valid_loss_min = np.inf
for epoch in range(1, Env.epochs + 1):
train_loss = 0.0
valid_loss = 0.0
########... | {"hexsha": "81ede7466a3958ad5a25dc1fbafee1b59849c8c0", "size": 4380, "ext": "py", "lang": "Python", "max_stars_repo_path": "helper.py", "max_stars_repo_name": "omtripathi786/MLP", "max_stars_repo_head_hexsha": "37c766590524b274c30057bc94c28e0c41ba1e14", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
#!/usr/bin/env python
"""ACTIVE LEARNING
This is an open source example to accompany Chapters 3 and 4 from the book:
"Human-in-the-Loop Machine Learning"
This example tries to classify news headlines into one of two categories:
disaster-related
not disaster-related
"""
import torch
import torch.nn as nn
impor... | {"hexsha": "89775ffce2b9b85283daec42d7b8e22a6d961833", "size": 28320, "ext": "py", "lang": "Python", "max_stars_repo_path": "active_learning.py", "max_stars_repo_name": "sthagen/pytorch_active_learning", "max_stars_repo_head_hexsha": "4d97ad42d95f73f564d05357389b97b45d4972a2", "max_stars_repo_licenses": ["MIT"], "max_s... |
from fem import DofHandler, Basis, QuadFE
from gmrf import GaussianField, Covariance
from assembler import Form, Assembler
from mesh import Mesh1D, QuadMesh
from plot import Plot
from function import Nodal
import numpy as np
from scipy import sparse as sp
import matplotlib.pyplot as plt
"""
Goal:
Investigate opt... | {"hexsha": "7b11fadd70f36e6c34db6989a9ecbfa73999ff98", "size": 13614, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/multiscale_gmrf/ex04/ex04.py", "max_stars_repo_name": "hvanwyk/drifter", "max_stars_repo_head_hexsha": "a08df0cef81bc6ca76084ae8cac089644e2bd56b", "max_stars_repo_licenses": ["MIT"], ... |
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
import numpy as np
import scipy
import os
import scipy.io
import sys
try:
import cPickle
except:
import _pickle as cPickle
# Syspath for the folder with the utils files
#sys.path.insert(0, "/media/data/srebuffi")
import ut... | {"hexsha": "b53a4241ccb4f57c1cca4481d5a8cf7777d9756f", "size": 12339, "ext": "py", "lang": "Python", "max_stars_repo_path": "iCaRL-Tensorflow/main_resnet_tf.py", "max_stars_repo_name": "augustoolucas/iCaRL", "max_stars_repo_head_hexsha": "dcad835c10f726e68cf83298fd96a32fe2949a5d", "max_stars_repo_licenses": ["MIT"], "m... |
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