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# Aula 5 - Regressão e Machine Learning
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plot
import math
import time
start = time.time()
# Formatação geral para apresentar os dados com 2 casas decimais
pd.options.display.float_format = '{:,.2f}'.format
sns.set_style("whitegrid")
# Biblioteca... | {"hexsha": "e75196acd4901dad75c51406a7d73661dc1b0fb7", "size": 10207, "ext": "py", "lang": "Python", "max_stars_repo_path": "Aula 05.py", "max_stars_repo_name": "brunoamaia94/Quarentena_Dados", "max_stars_repo_head_hexsha": "9402ec6223dc15146906f659db05eb1c0b41de97", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
section \<open>General purpose definitions and lemmas\<close>
theory Misc imports
Main
begin
text \<open>A handy abbreviation when working with maps\<close>
abbreviation make_map :: "'a set \<Rightarrow> 'b \<Rightarrow> ('a \<rightharpoonup> 'b)" ("[ _ |=> _ ]")
where
"[ks |=> v] \<equiv> \<lambda>k. if k \<in>... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/GPU_Kernel_PL/Misc.thy"} |
#redirect Big Blue
| {"hexsha": "347676e280f3b8525c9f8d8fb5fd5ea690ca60d5", "size": 19, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/The_Yellow_House.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma ereal_mult_less_0_iff:
fixes a b :: ereal
shows "a * b < 0 \<longleftrightarrow> (0 < a \<and> b < 0) \<or> (a < 0 \<and> 0 < b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (a * b < 0) = (0 < a \<and> b < 0 \<or> a < 0 \<and> 0 < b)
[PROOF STEP]
by (cases rule: ereal2_cases[of a b]) (simp_a... | {"llama_tokens": 165, "file": null, "length": 1} |
#!/usr/bin/env python
"""
P2sGrad:
Zhang, X. et al. P2sgrad: Refined gradients for optimizing deep face models.
in Proc. CVPR 9906-9914, 2019
I think the grad defined in Eq.(11) is equivalent to define a MSE loss with 0
or 1 as target:
\mathcal{L}_i = \sum_{j=1}^{K} (\cos\theta_{i,j} - \delta(j == y_i))^2
The dif... | {"hexsha": "ce1ffd24a20223a913ba89c499de21f68e2a5cdc", "size": 3964, "ext": "py", "lang": "Python", "max_stars_repo_path": "core_modules/p2sgrad.py", "max_stars_repo_name": "Nijta/project-NN-Pytorch-scripts", "max_stars_repo_head_hexsha": "06a50ab072613fb60b8b8e1cea85c4aa8e75549d", "max_stars_repo_licenses": ["BSD-3-Cl... |
"""
Copyright (c) 2020 Intel Corporation
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 wri... | {"hexsha": "af7ad42a85803467a0ebed57db5118e2f68d224e", "size": 12033, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch_toolkit/text_spotting/text_spotting/models/text_recognition_heads/attention_based.py", "max_stars_repo_name": "morkovka1337/openvino_training_extensions", "max_stars_repo_head_hexsha": "8... |
[STATEMENT]
lemma image_eq_to_f:
assumes "f1 ` S1 = f2 ` S2"
obtains f where "\<And> x. x \<in> S2 \<Longrightarrow> f x \<in> S1 \<and> f1 (f x) = f2 x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>f. (\<And>x. x \<in> S2 \<Longrightarrow> f x \<in> S1 \<and> f1 (f x) = f2 x) \<Longrightarrow> thesis)... | {"llama_tokens": 754, "file": "Incredible_Proof_Machine_Build_Incredible_Tree", "length": 7} |
# Monopoly odds
import numpy as np
from numba import njit
from collections import Counter
SIDES = 4
def solve():
iterations = 1_000_000
distribution = np.array([i + j + 2 for i, j in np.ndindex(SIDES, SIDES)])
positions = np.zeros(iterations, dtype=np.uint8)
rng = np.random.default_rng()
rints ... | {"hexsha": "7efd09813b1513bffa845038cdffb5c966734dad", "size": 1637, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/p084.py", "max_stars_repo_name": "wephy/project-euler", "max_stars_repo_head_hexsha": "cc4824478282d3e1514a1bf7a1821b938db5bfcb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
[STATEMENT]
lemma fun_upds_append_drop [simp]:
"size xs = size ys \<Longrightarrow> m(xs@zs[\<mapsto>]ys) = m(xs[\<mapsto>]ys)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length xs = length ys \<Longrightarrow> m(xs @ zs [\<mapsto>] ys) = m(xs [\<mapsto>] ys)
[PROOF STEP]
proof (induct xs arbitrary: ys)
[PROOF... | {"llama_tokens": 869, "file": null, "length": 6} |
# Augmenting Face Image Database via Transformations (Flip, Rotate, Crop & Scale)
# Jay Narhan
# UserID: JN721
#
# This class is designed to apply a series of image transformations to a set of images. A transformation is simply a
# function. It is a function that maps the image to another version of the image.
... | {"hexsha": "073764b7600dba5d7c658a14f837a080dba7bb5a", "size": 5512, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/JN721.py", "max_stars_repo_name": "shubham07kb/facial_expressions", "max_stars_repo_head_hexsha": "f620279b6729bd8697d9d98b4208bff074bec180", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
import numpy as np
x = np.array([-1, 1, 2.569858113, 6]) # x coordinates in space
y = np.array([1, 3, 1, -2]) # f(x)
print("Column 0 ")
print(y)
n = len(y)
table = np.zeros([n, n]) # Create a square matrix to hold table
table[::, 0] = y # first column is y
results = {"table": [], "coefficient": [... | {"hexsha": "65fde16b02f0a099129549c54caf5e10177f03ab", "size": 1255, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/newtonInterpolation.py", "max_stars_repo_name": "eechava6/NumericalAnalysisMethods", "max_stars_repo_head_hexsha": "3eeb06bdb20d97f13a09fd0ed71bce045173ffef", "max_stars_repo_licenses": ["M... |
using StatsPlots, Distributions
@testset "fa" begin
Random.seed!(1234)
a = rand(Uniform(0.5, 2.0), 30)
b = [sort(rand(Uniform(-3, 3), 4); rev = false) for i in 1:30]
θ = rand(Normal(0, 1), 3000)
resp = generate_response(θ, a, b)
fit1 = fa(resp; method = :em)
@test all(loadings(fit1) .≤... | {"hexsha": "003fb3388d03a2e292e60fda3a2eb3cc91f352fe", "size": 1637, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Test2_parallel.jl", "max_stars_repo_name": "takuizum/ParallelAnalysis.jl", "max_stars_repo_head_hexsha": "56609f6f0822dba101b8720f16b20ea3bfc3ad72", "max_stars_repo_licenses": ["MIT"], "max_st... |
import cv2
import numpy as np
img = np.zeros((512, 512, 3), np.uint8)
# print(img.shape)
# img[200:300, 100:300] = 255, 0, 0
# draw a line
cv2.line(img, (0, 0), (300, 300), (0, 255, 0), 3)
# draw a rewctangle
cv2.rectangle(img, (0, 0), (250, 350), (0, 0, 255), 3)
# draw a circle
cv2.circle(img, (250, 250), 30, (255,... | {"hexsha": "0138baf49094f7172471cf4c98a93ff73615e21a", "size": 470, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/opencv/chapter_03.py", "max_stars_repo_name": "snandasena/udacity-dl", "max_stars_repo_head_hexsha": "7ea13ec7ebd992f1199f43bd5300782436ed71e5", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
[STATEMENT]
lemma almost_full_on_pp_iff:
"almost_full_on (adds) A \<longleftrightarrow> almost_full_on (adds) (mapping_of ` A)" (is "?l \<longleftrightarrow> ?r")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. almost_full_on (adds) A = almost_full_on (adds) (pp.mapping_of ` A)
[PROOF STEP]
proof
[PROOF STATE]
proo... | {"llama_tokens": 1583, "file": "Polynomials_PP_Type", "length": 18} |
[STATEMENT]
lemma partitionI: \<^marker>\<open>contributor \<open>Paulo Em?lio de Vilhena\<close>\<close>
fixes A :: "'a set" and B :: "('a set) set"
assumes "\<Union>B = A"
and "\<And>b1 b2. \<lbrakk> b1 \<in> B; b2 \<in> B \<rbrakk> \<Longrightarrow> b1 \<inter> b2 \<noteq> {} \<Longrightarrow> b1 = b2"
sho... | {"llama_tokens": 1753, "file": null, "length": 18} |
#include <boost/numeric/odeint/external/mpi/mpi_nested_algebra.hpp>
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import os
import streamlit as st
import streamlit.components.v1 as components
import time
from video import getvideo
from modelDownloader import downloader
import cv2
import numpy as np
# import matplotlib.pyplot as plt
from PIL import Image
import tempfile
import requests
from pathlib import Path
def detect_objects(... | {"hexsha": "841d164d52dbbc07a38c19c6bff9eb6b8a1f0546", "size": 16108, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/app.py", "max_stars_repo_name": "Kaushal000/Streamlit-coronavirus-detection-app", "max_stars_repo_head_hexsha": "3f6c646c4e7a268f27f2cb6b7d8d02d002e8679a", "max_stars_repo_licenses": ["MIT"],... |
from torch.utils import data
import json
from PIL import Image
import os
import cv2
import numpy as np
class DataLoader(data.Dataset):
def __init__(self, data_list, transform=None):
with open(os.path.abspath(data_list)) as json_file:
data = json.load(json_file)
self.data = data
... | {"hexsha": "c9580a1d58328dc4d998d23f3431adb89dfe65cc", "size": 792, "ext": "py", "lang": "Python", "max_stars_repo_path": "classification/data_loader.py", "max_stars_repo_name": "hrlblab/Glo-In-One", "max_stars_repo_head_hexsha": "7daef49c557bccd6f5c956b88603357346dc78a2", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Basic ROS
import rospy
# ROS messages
from visualization_msgs.msg import Marker
from sensor_msgs.msg import LaserScan
# Maths
import numpy as np
# Custom libraries
from splitandmerge import splitandmerge
from probabilistic_lib.functions import publish_lines
#============... | {"hexsha": "322264a06588b4d24fff91a396d9315ce1b2c7f7", "size": 2237, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/split_and_merge/src/splitandmergenode.py", "max_stars_repo_name": "karanchawla/ROS", "max_stars_repo_head_hexsha": "e005b8b488bcf493a6dd283d3179db597bfcb09d", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python
# coding: utf-8
import sys
import os
import time
import json
import numpy as np
import pandas as pd
import dill
import random
from os.path import join, dirname, abspath, pardir, basename
from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.ensemble import RandomForestClassifier
from... | {"hexsha": "2547f520d972a5e26b0883d47d8f358df054b76e", "size": 8380, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/doh_data_classifier/classifier_rfc.py", "max_stars_repo_name": "yshao321/doh_website_fingerprinting", "max_stars_repo_head_hexsha": "2c44940f80ba6503075735302a1d8cd421571ada", "max_stars_repo... |
[STATEMENT]
lemma arctan_bounds:
assumes "0 \<le> x" "x < 1"
shows arctan_lower_bound:
"(\<Sum>k<2 * n. (- 1) ^ k * (1 / real (k * 2 + 1) * x ^ (k * 2 + 1))) \<le> arctan x"
(is "(\<Sum>k<_. (- 1)^ k * ?a k) \<le> _")
and arctan_upper_bound:
"arctan x \<le> (\<Sum>k<2 * n + 1. (- 1) ^ k * (1 / real ... | {"llama_tokens": 2635, "file": null, "length": 15} |
# This file is part of IntegerTriangles.
# Copyright Peter Luschny. License is MIT.
# Version of: UTC 2021-05-22 22:01:34
# 8228b27e-bb38-11eb-3e8e-73d8fe660356
# Do not edit this file, it is generated from the modules and will be overwritten!
# Edit the modules in the src directory and build this file with BuildTria... | {"hexsha": "a1c06463d72a8c54f421c2b4d2586a979d88337b", "size": 2372, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/perftests.jl", "max_stars_repo_name": "OpenLibMathSeq/IntegerTriangles.jl", "max_stars_repo_head_hexsha": "15cc716086e8d59309b30869f6a4ed59be2dc068", "max_stars_repo_licenses": ["MIT"], "max_s... |
from scipy.spatial.distance import pdist, squareform
from numpy.random import np
from numpy import concatenate
import heapq
# Solves a facility location problem using the algorithm from Jain et al. "Greedy Facility Location Algorithms Analyzed
# using Dual Fitting with Factor-Revealing LP". A facility location algori... | {"hexsha": "189672695fc9b1e5493b5bbb1adb82f628b378a1", "size": 19977, "ext": "py", "lang": "Python", "max_stars_repo_path": "crowdsourcing/util/facility_location.py", "max_stars_repo_name": "sbranson/online_crowdsourcing", "max_stars_repo_head_hexsha": "d1f7c814bb60aae9cf5e76e0b299713246f98ce3", "max_stars_repo_license... |
%---------------------------Shape-----------------------------
\section{Edge Ratio\label{s:hex-edge-ratio}}
The edge ratio quality metric is the ratio of the longest to
shortest edge of a hexahedron:
\[
q = \frac{L_{\max}}{L_{\min}}.
\]
\hexmetrictable{edge ratio}%
{$1$}% Dimens... | {"hexsha": "481350b32b9755dc940aa579a9a5af0786d9e925", "size": 677, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Utilities/verdict/docs/VerdictUserManual2007/HexEdgeRatio.tex", "max_stars_repo_name": "Lin1225/vtk_v5.10.0", "max_stars_repo_head_hexsha": "b54ac74f4716572862365fbff28cd0ecb8d08c3d", "max_stars_repo... |
! ------------------------------------------------------------------------------
!
! Main Program mtclim
!
! author: Johannes Brenner
!
! created: 03.08.2016
! last update: 30.05.2017
!
! ------------------------------------------------------------------------------
!
program main
!
use mo_kind, only: i4, dp
use m... | {"hexsha": "8d488e4b065f4d1f1602834ec3f7cb1eb149644f", "size": 8504, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/test_mo_mtclim/main.f90", "max_stars_repo_name": "mcuntz/jams_fortran", "max_stars_repo_head_hexsha": "a1dcab78eda21f930aa6c8c3633598a522f01659", "max_stars_repo_licenses": ["MIT"], "max_st... |
using OpenQuantumBase, Test
import LinearAlgebra: Diagonal
funcs = [(x) -> x, (x) -> 1 - x]
test_diag_operator = OpenQuantumBase.DiagonalOperator(funcs)
@test test_diag_operator(0.5) == OpenQuantumBase.Diagonal([0.5, 0.5])
test_geometric_operator = OpenQuantumBase.GeometricOperator(((x) -> -1.0im * x))
@test test_geo... | {"hexsha": "5b55dc303877f855568925ada599c1cb16293862", "size": 1517, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/hamiltonian/adiabatic_frame_hamiltonian.jl", "max_stars_repo_name": "neversakura/QTBase.jl", "max_stars_repo_head_hexsha": "937a3236f1b9578bc223b21817dec2e7a8512ee2", "max_stars_repo_licenses"... |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct IoK8sApimachineryPkgApisMetaV1ObjectMeta <: SwaggerModel
annotations::Any # spec type: Union{ Nothing, Dict{String, String} } # spec name: annotations
clu... | {"hexsha": "282c8d9f00f8759a67032368511a74b14883fa1b", "size": 7639, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/api/model_IoK8sApimachineryPkgApisMetaV1ObjectMeta.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Kuber.jl-87e52247-8a1b-5e01-9430-8fbcac83a23a", "max_stars_repo_head_hexsha": "dd6... |
import numpy as np
from classification_model.config import core
from classification_model.processing import preprocessors as pp
def test_FeatureKeeper(pipeline_inputs):
"""Testing FeatureKeeper function"""
# Given
feature_keeper = pp.FeatureKeeper(
variables_to_keep=core.config.model_config.VARIA... | {"hexsha": "517aa659f0b5f70e91210f192f7281a3944e0616", "size": 2822, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_processing/test_preprocessors.py", "max_stars_repo_name": "Windact/classification_model", "max_stars_repo_head_hexsha": "e42e8099069467eeb21f94f6777eb34e68906500", "max_stars_repo_licen... |
import pygame, sys
import numpy as np
import subprocess
import time
import json
WIDTH = 600
HEIGHT = 600+100
LINE_WIDTH = 15
WIN_LINE_WIDTH = 15
BOARD_ROWS = 3
BOARD_COLS = 3
SQUARE_SIZE = 200
CIRCLE_RADIUS = 60
CIRCLE_WIDTH = 15
CROSS_WIDTH = 25
SPACE = 55
RED = (255, 0, 0)
BG_COLOR = (20, 200, 160)
LINE_COLOR = (23... | {"hexsha": "e42cd4be592a6c62e4d5fb6fd0bfe7a32692c2af", "size": 7661, "ext": "py", "lang": "Python", "max_stars_repo_path": "gui/game.py", "max_stars_repo_name": "guiltygyoza/tictactoe-on-starknet", "max_stars_repo_head_hexsha": "9ff31e7813e0686fc509cb8471803f70b539d6f2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from sklearn import linear_model
from stable_baselines.common.vec_env.vec_video_recorder import VecVideoRecorder
from stable_baselines.low_dim_analysis.eval_util import get_full_param_traj_file_path, get_aug_plot_dir, get_full_params_dir, get_save_dir
import minepy
from matplotlib import pyplot as plt
import numpy ... | {"hexsha": "77fd6ade5dad5cfaf49bd297cbad2fa04022169f", "size": 19412, "ext": "py", "lang": "Python", "max_stars_repo_path": "new_neuron_analysis/visualize_augment_policy.py", "max_stars_repo_name": "hugerepo-tianhang/low_dim_update_stable", "max_stars_repo_head_hexsha": "565f6cbf886d266d0633bc112ccae28f1d116ee1", "max_... |
import torch
import torch.nn as nn
import torch.nn.parallel
from torch.autograd import Variable
import torch.nn.functional as F
from torchvision import models
import torch.utils.model_zoo as model_zoo
from torch.nn import init
import os
import numpy as np
def weights_init_normal(m):
classname = m.__class__.__na... | {"hexsha": "8a1dae863d7924b8a818052fe998ef9263921fbd", "size": 26686, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tencent/Video_Generation/MakeItTalk/src/models/model_image_translation.py", "max_stars_repo_name": "orange-eng/internship", "max_stars_repo_head_hexsha": "c8c566df453d3a4bdf692338f74916ae15792fa1... |
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import time
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import magnum as mn
import numpy a... | {"hexsha": "e9ac508a47940cfc90069782327d8453e0335f2b", "size": 12500, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/fairmotion_interface_utils.py", "max_stars_repo_name": "narekvslife/habitat-sim", "max_stars_repo_head_hexsha": "69ae4848503d5dcc74d6b5920957c1a641ef0a9b", "max_stars_repo_licenses": ["M... |
program crystal2d nk
!c use ieee_arithmetic
! implicit double precision (a-h,o-z) dp
!***********************************************************************
! ... | {"hexsha": "18620ff7fea2f7fa5e94ed46ef990c2d7e141d2a", "size": 9640, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/crystal2d.f", "max_stars_repo_name": "imohame/LabCode", "max_stars_repo_head_hexsha": "b7fca6e58f6c26917ff4a8862ab473da282d027d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
"""Cross-validated training and prediction."""
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, ClassifierMixin, clone
class CVModel(BaseEstimator, ClassifierMixin):
def __init__(self, base_estimator=None):
self.base_estimator = base_estimator
def fit(self, X_train, y_tr... | {"hexsha": "b4c3207a5e0c3591a483e6408b4aed5f8ab28c39", "size": 7080, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml_insights/CVModel.py", "max_stars_repo_name": "JustinKurland/introspective", "max_stars_repo_head_hexsha": "3626c5a176c70fb6d09071307949032b5ff4f0e5", "max_stars_repo_licenses": ["MIT"], "max_st... |
"""
Script for performing a fit to a histogramm of recorded
time differences for the use with QNet
"""
import scipy.optimize as optimize
import numpy
import pylab
import sys
#import optimalbins
def main(bincontent=None,binning = (0,10,21), fitrange = None):
def decay(p,x):
return p[0]*numpy.exp(-x/p[1]... | {"hexsha": "5767384faba457308d0537d25d0cef4345e3b74e", "size": 8464, "ext": "py", "lang": "Python", "max_stars_repo_path": "muonic/analysis/fit.py", "max_stars_repo_name": "LambdaDigamma/muonic", "max_stars_repo_head_hexsha": "cc242582168101f1ab444ffdc915f8a007078bc4", "max_stars_repo_licenses": ["Naumen", "Condor-1.1"... |
import kbmod
import numpy
import re
import pdb
# layered image functions
def science_to_numpy(self, copy_data=False):
if copy_data == None:
copy_data = False
return numpy.array( self.get_science(), copy=copy_data )
def mask_to_numpy(self, copy_data=False):
if copy_data == None:
copy_data = False... | {"hexsha": "c313b1632d20734ccc854cd626d695878629d1f7", "size": 5629, "ext": "py", "lang": "Python", "max_stars_repo_path": "search/pybinds/kbmodpy.py", "max_stars_repo_name": "fraserw/kbmod", "max_stars_repo_head_hexsha": "65d69746d1dd8de867f8da147d73c09439d28b41", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
import itertools
import numpy as np
from arqtic.program import Program, Gate
from arqtic.exceptions import Error
import scipy
from sklearn.linear_model import Lasso, LinearRegression
import qiskit as qk
from qiskit.aqua.operators.primitive_ops import MatrixOp
from qiskit import Aer, execute
import scipy.linalg as la
#... | {"hexsha": "0a1bcc9a98536623f6039d6bb90ec3c040427cfc", "size": 19547, "ext": "py", "lang": "Python", "max_stars_repo_path": "arqtic/qite.py", "max_stars_repo_name": "lebassman/ArQTiC", "max_stars_repo_head_hexsha": "23d9877dfb0a8c5c10da2865abaf9ca830298199", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], "max_stars_... |
import numpy as np
def calculate_exploration_prob(loss_history, act_explor_prob,threshold):
mean = np.mean(loss_history)
variance = 0
for i in loss_history:
variance += np.square(i-mean)
if threshold >= variance:
act_explor_prob += 0.05
else:
act_explor_prob -= 0.05
i... | {"hexsha": "a654046012f0a0be8b5fe2ef5b6e639fae49832b", "size": 450, "ext": "py", "lang": "Python", "max_stars_repo_path": "Zadanie3/srcV2/Misc.py", "max_stars_repo_name": "letv3/NSIETE", "max_stars_repo_head_hexsha": "3e65c66ddb14cf11b4757bdc8a70d5438785dec2", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_cou... |
[STATEMENT]
theorem cptn_iff_cptn_mod: "(c \<in> cptn) = (c \<in> cptn_mod)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (c \<in> cptn) = (c \<in> cptn_mod)
[PROOF STEP]
apply(rule iffI)
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. c \<in> cptn \<Longrightarrow> c \<in> cptn_mod
2. c \<in> cptn_mod \<Longr... | {"llama_tokens": 293, "file": null, "length": 4} |
import numpy as np
for train_set in ['train_yyn', 'train_ynn']:
all_res = []
for nlayer in [1, 2, 3]:
for eunits in [50, 60, 70, 80, 90]:
average_list = []
for random_seed in [1, 3, 5, 7, 9]:
exp_dir = 'exp/%s_pytorch_train_delta_%d_%d_%d' % (train_set,
... | {"hexsha": "001a14568ca601c274e0a46bb8544a5308d3bce9", "size": 858, "ext": "py", "lang": "Python", "max_stars_repo_path": "egs/modified_yesno/asr_mfcc_char_ns/local/average_random_seed.py", "max_stars_repo_name": "ZhaoZeyu1995/espnet", "max_stars_repo_head_hexsha": "350e97da275e35a9495da8ee2f53d19f93f50241", "max_stars... |
# Importing Required Libraries
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import matplotlib.pyplot as plt
# helper function to find midpoint between two points
def midpoint(ptA, ptB):
return ((... | {"hexsha": "4d47db0f7e9b964e3996e9259c7351a1f78e95af", "size": 3562, "ext": "py", "lang": "Python", "max_stars_repo_path": "Baggage Fitment Index/final.py", "max_stars_repo_name": "sahilrider/Machine-Vision-Challenge", "max_stars_repo_head_hexsha": "d930a1dad0de05643a1245c3c243fb03a268ba4b", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma parCases'[consumes 5, case_names cPar1 cPar2 cComm1 cComm2]:
fixes \<Psi> :: 'b
and P :: "('a, 'b, 'c) psi"
and Q :: "('a, 'b, 'c) psi"
and \<alpha> :: "'a action"
and T :: "('a, 'b, 'c) psi"
and C :: "'d::fs_name"
assumes Trans: "\<Psi> \<rhd> P \<parallel> ... | {"llama_tokens": 6351, "file": "Psi_Calculi_Tau", "length": 27} |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from nn_meter.utils.utils import try_import_onnx
import networkx as nx
from .utils import get_tensor_shape
from .constants import SLICE_TYPE
from itertools import chain
import logging
class OnnxConverter:
def __init__(self, model):
o... | {"hexsha": "d4d214f9065299fae8d0877722c3a467dd181c16", "size": 5134, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn_meter/ir_converters/onnx_converter/converter.py", "max_stars_repo_name": "kaleid-liner/nn-Meter", "max_stars_repo_head_hexsha": "526f8ddeeb33816a0b2b7f97964683e0510cd85d", "max_stars_repo_licen... |
import numpy as np
#import nengolib
from nengo_ssp.spatial_semantic_pointer import SpatialSemanticPointer
from nengo_ssp.hrr_algebra import HrrAlgebra
def PlaneWaveBasis(K):
# Create the bases vectors X,Y as described in the paper with the wavevectors
# (k_i = (u_i,v_i)) given in a matrix K. To get hexganal p... | {"hexsha": "89e2827ee3ea1adee47c56141def5bd19967ad6c", "size": 7364, "ext": "py", "lang": "Python", "max_stars_repo_path": "nengo_ssp/vector_generation.py", "max_stars_repo_name": "nsdumont/nengo_ssp", "max_stars_repo_head_hexsha": "9530a4618e213fb695b52887772c1309d0f07a0b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
// Implementation of all debugging command handlers.
#include <iostream>
#include <map>
#include <unordered_map>
#include <string>
#include <vector>
#include <boost/graph/breadth_first_search.hpp>
#include <boost/tokenizer.hpp>
#include "typedefs.h"
#include "debugger_commands.h"
#include "debugger_graph.h"
#include... | {"hexsha": "9852767131d2cd6e10298404db863bdb8afa2735", "size": 11949, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "common/debugger_commands.cpp", "max_stars_repo_name": "nitish2112/ALADDIN", "max_stars_repo_head_hexsha": "2cef8aa854f25483f9f50a8a07d185b56c53e150", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
/*
* Copyright 2011 Matthias Fuchs
*
* 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... | {"hexsha": "bf98c498d6fa946652d3af7c000e090b785dc24e", "size": 1883, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "python/stromx/runtime/XmlReader.cpp", "max_stars_repo_name": "roteroktober/stromx", "max_stars_repo_head_hexsha": "e081a35114f68a77e99a4761946b8b8c64eb591a", "max_stars_repo_licenses": ["Apache-2.0"... |
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
*/
/*
* File: RpcServerModule.cpp
* Author: ubuntu
*
* Created on January 20, 2018, 2:46 PM
*/
#include <boost/beast.hpp>
#incl... | {"hexsha": "2a9ef07e9cfdefc8041cfa09027a8e4f96644281", "size": 960, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/modules/rpc_server/RpcServerModule.cpp", "max_stars_repo_name": "burntjam/keto", "max_stars_repo_head_hexsha": "dbe32916a3bbc92fa0bbcb97d9de493d7ed63fd8", "max_stars_repo_licenses": ["MIT"], "max... |
using MedicalImagingUtils
using Documenter
DocMeta.setdocmeta!(MedicalImagingUtils, :DocTestSetup, :(using MedicalImagingUtils); recursive=true)
makedocs(;
modules=[MedicalImagingUtils],
authors="Dale <djblack@uci.edu> and contributors",
repo="https://github.com/Dale-Black/MedicalImagingUtils.jl/blob/{com... | {"hexsha": "8d0ad85d9d84c00df3fffee28c73f96a3fc09d40", "size": 691, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "Dale-Black/MedicalImagingUtils.jl", "max_stars_repo_head_hexsha": "3e58d48fd93c7608f8a557139bd902a96137b974", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python
# Copyright 2014-2018 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | {"hexsha": "0ee8634fb2ddf829f231dc38f931da6f63b5af01", "size": 28209, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/lib/gto/test/test_ecp.py", "max_stars_repo_name": "mfkasim1/pyscf", "max_stars_repo_head_hexsha": "7be5e015b2b40181755c71d888449db936604660", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
! { dg-do compile }
!
! PR 42188: [OOP] F03:C612. The leftmost part-name shall be the name of a data object
!
! Contributed by Janus Weil <janus@gcc.gnu.org>
module grid_module
implicit none
type grid
contains
procedure :: new_grid
procedure :: new_int
end type
contains
subroutine new_grid(this)
class(gr... | {"hexsha": "51f5b8634945d3ea36da08db7777f9a9827d0af3", "size": 921, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/derived_result_2.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo... |
from sklearn.svm import LinearSVR
from sklearn.linear_model import SGDRegressor
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_... | {"hexsha": "844a059771536f1bdead9e1a1dcd9d080ba67b15", "size": 916, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn2.py", "max_stars_repo_name": "WayneFerrao/autofocus", "max_stars_repo_head_hexsha": "80a5d2366639177dbd16708a79b88df17528054c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_sta... |
\setchapterpreamble[u]{\margintoc}
\chapter{Energy Systems}
\labch{tut1}
After completing this session successfully, you should be able
\begin{itemize}
\item to understand what an energy system is,
\item to know about issues connected with current energy systems, and
\item to explain the necessity of energy system t... | {"hexsha": "e8fdd6944784a2336e51aa96ad843806c6207127", "size": 7357, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "handbook/chapters/session_1.tex", "max_stars_repo_name": "lhofbauer/outreach_course", "max_stars_repo_head_hexsha": "2c15f6e33103f3a06be2e1228cc43ac2547632ef", "max_stars_repo_licenses": ["CC-BY-4.0... |
# -*- coding: utf-8 -*-
import numpy as np
import pycuda.autoinit # noqa
import pycuda.driver as cuda
class _CalibratorBuffer:
def __init__(self, bindings, batch_size):
self.bindings = bindings
self.allocations = {}
for binding in self.bindings.values():
elem_size = binding.... | {"hexsha": "005f7f255a02dc68f96e22301a5020a1c29e723f", "size": 2803, "ext": "py", "lang": "Python", "max_stars_repo_path": "turret/int8/int8_calibrator.py", "max_stars_repo_name": "hemantranvir/turret", "max_stars_repo_head_hexsha": "bc3df21541ce2f808c749c985db47a210149f22c", "max_stars_repo_licenses": ["MIT"], "max_st... |
import ITlib
import numpy
import matplotlib.pyplot as plt
print "\nExample 2"
print "Entropy example for a binary source\n"
step = 0.001
p1 = numpy.arange(0,1+step,step) # p1 defined in the [0,1] range
p2 = 1 - p1 # p2 = 1 - p1
H = numpy.zeros... | {"hexsha": "b1c68e1078b2f77e8a79653434e0a75d65f76f85", "size": 703, "ext": "py", "lang": "Python", "max_stars_repo_path": "example2.py", "max_stars_repo_name": "tyiannak/inf_teiste_info_theory_lab", "max_stars_repo_head_hexsha": "94ac5469d850472e492afdc36785e3590760660d", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
\documentclass[a4paper,12pt]{article}
% Margins
\usepackage{a4wide}
% Write english
\usepackage[english]{babel}
% Used for images
\usepackage{graphicx}
% Used to show eps on Windows
\usepackage{epstopdf}
\usepackage{float}
% Text encoding
% Needed to use headers
\usepackage{fancyhdr}
\usepackage{hyperref}
% Used for t... | {"hexsha": "acd01daf36e9073d17c7c95bde3fa9b8400931f2", "size": 32904, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "AMMA/ProgressReport.tex", "max_stars_repo_name": "FlashYoshi/UGentProjects", "max_stars_repo_head_hexsha": "5561ce3bb73d5bc5bf31bcda2be7e038514c7072", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import time
import mrcfile
import argparse
import numpy as np
import multiprocessing
from scipy import ndimage as ndi
from scipy.stats import wasserstein_distance
from skimage import transform, measure
from multiprocessing import cpu_count as mul_cpu_count
SHIFT = ['Euclidean', 'L1', 'cosine'] # Metrics requiring rea... | {"hexsha": "772d7cb94fa360435db10493fd8f932528f54028", "size": 11355, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/slicem.py", "max_stars_repo_name": "marcottelab/2D_projection_clustering", "max_stars_repo_head_hexsha": "35431b16af7d1ca9d0647aa5d0bcff62243dbcdc", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import HEngine
def OnUpdateRuntime(transformComponent, ts):
transformComponent.SetTranslation(transformComponent.GetTranslation().x + ts.GetSeconds(), transformComponent.GetTranslation().y, transformComponent.GetTranslation().z)
return transformComponent;
def OnUpdateEditor(transformCompone... | {"hexsha": "541fb899d81bf88f5676ab0790175487d2de51ba", "size": 373, "ext": "py", "lang": "Python", "max_stars_repo_path": "Engine/Source/Scripts/MoveRight.py", "max_stars_repo_name": "hebohang/HEngine", "max_stars_repo_head_hexsha": "82f40797a7cfabaa11aeeb7797fba70551d18017", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Copyright 2020 Forschungszentrum Jülich
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... | {"hexsha": "f31ebfeb75233f679de37ac4c1ea153e2d80efc8", "size": 8866, "ext": "py", "lang": "Python", "max_stars_repo_path": "webjugex/util.py", "max_stars_repo_name": "FZJ-INM1-BDA/PyJuGEx", "max_stars_repo_head_hexsha": "6cb82679a3a25512f7faa388df98445d46e9471a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
[GOAL]
ι : Type u_1
R : Type u_2
S : Type u_3
M : ι → Type u_4
N : Type u_5
dec_ι : DecidableEq ι
inst✝⁷ : Semiring R
inst✝⁶ : (i : ι) → AddCommMonoid (M i)
inst✝⁵ : (i : ι) → Module R (M i)
inst✝⁴ : AddCommMonoid N
inst✝³ : Module R N
inst✝² : Semiring S
inst✝¹ : Module S N
inst✝ : SMulCommClass R S N
F : (i : ι) → M ... | {"mathlib_filename": "Mathlib.LinearAlgebra.DFinsupp", "llama_tokens": 90178} |
import matplotlib.pyplot as plt
from pyexocross.exomol import ExomolDef
from pyexocross import ExocrossRunner
import numpy as np
path_to_exocross = '/Users/ahmed/Documents/repos/exocross/xcross.exe'
path_to_linelist = '/Users/ahmed/Documents/Linelists/H2O/'
path_to_def_file = '/Users/ahmed/Documents/Linelists/H2O/1H2... | {"hexsha": "e496f82d128ebab7193ad055146593959948886d", "size": 1618, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/example_run.py", "max_stars_repo_name": "ucl-exoplanets/pyexocross", "max_stars_repo_head_hexsha": "703341cd0fddafcbb04e935c89ddc9d02dda9f59", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
from __future__ import absolute_import, division, print_function
import numpy as np
from numpy.testing import (assert_allclose,
assert_equal, assert_almost_equal, assert_raises)
from scipy.spatial import procrustes
class TestProcrustes(object):
def setup_method(self):
"""creat... | {"hexsha": "2903a0b68b33773f95d9cd90df2bb75db2224967", "size": 5049, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/spatial/tests/test__procrustes.py", "max_stars_repo_name": "rostyboost/scipy", "max_stars_repo_head_hexsha": "2f5aa264724099c03772ed784e7a947d2bea8398", "max_stars_repo_licenses": ["BSD-3-Cl... |
# -*- coding: utf-8 -*-
# import numpy as np
# from numpy import testing
#
# from sktime.contrib.interval_based._cif import CanonicalIntervalForest
# from sktime.datasets import load_gunpoint, load_italy_power_demand
#
#
# def test_cif_on_gunpoint():
# # load gunpoint data
# X_train, y_train = load_gunpoint(spl... | {"hexsha": "1d25e22b4bebae4595fa73b321318c5c90515f12", "size": 2717, "ext": "py", "lang": "Python", "max_stars_repo_path": "sktime/contrib/interval_based/test_cif.py", "max_stars_repo_name": "bmurdata/sktime", "max_stars_repo_head_hexsha": "fb079e76e3f3dfbb849fb805e53f09adec6cdf79", "max_stars_repo_licenses": ["BSD-3-C... |
<a href="https://colab.research.google.com/github/john-s-butler-dit/Numerical-Analysis-Python/blob/master/Chapter%2009%20-%20Elliptic%20Equations/902_Poisson%20Equation-Zero%20Boundary%20Conditions.ipynb" target="_parent"></a>
# Finite Difference Methods for the Poisson Equation with Zero Boundary
This notebook will f... | {"hexsha": "7024f9f33fe98e64988f7443c1a7eff56a41ce09", "size": 264356, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Chapter 09 - Elliptic Equations/902_Poisson Equation-Zero Boundary Conditions.ipynb", "max_stars_repo_name": "jjcrofts77/Numerical-Analysis-Python", "max_stars_repo_head_hexsha": "9... |
from unittest import TestCase
import numpy as np
import nucleoatac.NucleosomeCalling as Nuc
import pyatac.VMat as V
from pyatac.chunkmat2d import BiasMat2D
from pyatac.chunk import ChunkList
from pyatac.bias import InsertionBiasTrack
class Test_variance(TestCase):
"""class for testing variance calculation on back... | {"hexsha": "e633004fb704ae3c5d9deac20f9dd45f4a61e6ee", "size": 1764, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_var.py", "max_stars_repo_name": "oaxiom/NucleoATAC", "max_stars_repo_head_hexsha": "e1ec144df924ab48f5f6fe65c52e4ec40d5a89f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 101... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 3 11:53:06 2021
@author: ghiggi
"""
import pandas as pd
import numpy as np
def _define_event_id(timesteps, maximum_interval_without_timesteps):
# Check type validity
if not isinstance(timesteps, (list, pd.Series,np.ndarray)):
ra... | {"hexsha": "58c57c6628484b1b963b783e8ae92e7a260d5915", "size": 2537, "ext": "py", "lang": "Python", "max_stars_repo_path": "mascdb/utils_event.py", "max_stars_repo_name": "ltelab/pymascdb", "max_stars_repo_head_hexsha": "d4ff0888aefcfa84a551459c61b0a93ab7d61622", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
from car.Car import Car
# Create a 2D world of 0's
height = 4
width = 6
world = np.zeros((height, width))
# Define the initial car state
initial_position = [0, 0] # [y, x] (top-left corner)
velocity = [0, 1] # [vy, vx] (moving to the right)
cara = Car(initial_position,velocity,world)
cara.move()... | {"hexsha": "38c9b5a06f15e93d47ae182e46b7611a13c4bf38", "size": 503, "ext": "py", "lang": "Python", "max_stars_repo_path": "self-driving/Matrix/move.py", "max_stars_repo_name": "xta0/Python-Playground", "max_stars_repo_head_hexsha": "513ebd2ad7f0a8c69f2f04b4f7524b31e76fa5bc", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
from common.prioritized import PrioritizedExperienceReplay
class ReplayMemory(object):
def __init__(self, n_buffer,len_state,len_action):
# Parameters
self.n_buffer = n_buffer
self.len_state = len_state
self.len_action = len_action
self.n_expe... | {"hexsha": "d72479d0e051c4a22354d598e1cbb9a5f7561db5", "size": 1907, "ext": "py", "lang": "Python", "max_stars_repo_path": "ReinforcementLearning/ExperienceReplay.py", "max_stars_repo_name": "Suryavf/SelfDrivingCar", "max_stars_repo_head_hexsha": "362ac830516366b1c31ef01ea0456eb99f0d9722", "max_stars_repo_licenses": ["... |
# """
# Runs libEnsemble on the 6-hump camel problem. Documented here:
# https://www.sfu.ca/~ssurjano/camel6.html
#
# Execute via the following command:
# mpiexec -np 4 python3 test_6-hump_camel_uniform_sampling.py
# The number of concurrent evaluations of the objective function will be 4-1=3.
# """
from __futur... | {"hexsha": "c4d164d61d1e68fbf3646ee21d28e4a71c382f5d", "size": 2711, "ext": "py", "lang": "Python", "max_stars_repo_path": "libensemble/tests/regression_tests/test_6-hump_camel_uniform_sampling.py", "max_stars_repo_name": "Kardyne/libensemble", "max_stars_repo_head_hexsha": "566c8f5daafe2ad4deebc13198a1e131e4ce6542", "... |
#ifndef __COMMON_H__
#define __COMMON_H__
#include <array>
#include <assert.h>
#include "otype.hpp"
// define random seed
#define SEED 2333333
// define stencil type
#define STENCIL_STAR 0
#define STENCIL_BOX 1
// define boundary type
#define BOUND_OPEN 0
#define BOUNDARY_OPEN 0
#define BOUND_PERIODIC 1
#define BO... | {"hexsha": "f4fc322f99650dd6b33b76920352a1d5e1efb60e", "size": 2494, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "common.hpp", "max_stars_repo_name": "hxmhuang/OpenArray_Dev", "max_stars_repo_head_hexsha": "863866a6b7accf21fa253567b0e66143c7506cdf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "m... |
import numpy as np
import matplotlib.pyplot as plt
from pprint import pprint
def to_pt(l):
# return tuple(map(float, l.split(" - ")[0].replace("(", "").split(",")))
return l.split(" - ")[0].replace("(", "").split(",")
with open("/home/slam_data/data_sets/pcl_plane_pts.txt", "r") as conn:
pts1 = conn.read... | {"hexsha": "6c1ead886d2fe69bbfc3956c17c72469d136bd92", "size": 1855, "ext": "py", "lang": "Python", "max_stars_repo_path": "temp3.py", "max_stars_repo_name": "BOpermanis/pyORBSLAM2", "max_stars_repo_head_hexsha": "ff7c303bc6d2023fc3c22090e6af048072cce90b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
[STATEMENT]
lemma Qp_pow_ConsI:
assumes "t \<in> carrier Q\<^sub>p"
assumes "x \<in> carrier (Q\<^sub>p\<^bsup>m\<^esup>)"
shows "t#x \<in> carrier (Q\<^sub>p\<^bsup>Suc m\<^esup>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. t # x \<in> carrier (Q\<^sub>p\<^bsup>Suc m\<^esup>)
[PROOF STEP]
using assms cart... | {"llama_tokens": 355, "file": "Padic_Field_Padic_Semialgebraic_Function_Ring", "length": 2} |
import operator
import os
import re
from collections import OrderedDict
from typing import List
import numpy as np
from sortedcollections import OrderedSet
from EXIFnaming.helpers import constants as c, settings
from EXIFnaming.helpers.decode import read_exiftag
from EXIFnaming.helpers.program_dir import log
from EXI... | {"hexsha": "3b07a7f67ad452d41b2d743c2c4d264e2e08cc54", "size": 19501, "ext": "py", "lang": "Python", "max_stars_repo_path": "EXIFnaming/helpers/tag_conversion.py", "max_stars_repo_name": "mvolkert/EXIFnaming", "max_stars_repo_head_hexsha": "2cd7aa03cec8e05e046ccc84c523d560ccc3b8c0", "max_stars_repo_licenses": ["MIT"], ... |
#ifndef LIB_INCLUDE_TICK_ARRAY_VECTOR_OPERATIONS_H_
#define LIB_INCLUDE_TICK_ARRAY_VECTOR_OPERATIONS_H_
// License: BSD 3 clause
#include <atomic>
#include <vector>
#include <numeric>
#include <algorithm>
#include <type_traits>
#include "promote.h"
#include "tick/base/defs.h"
#if defined(TICK_USE_MKL)
#include "m... | {"hexsha": "bb288e98d09b991dacd20587d2b805bfeb4df702", "size": 1085, "ext": "h", "lang": "C", "max_stars_repo_path": "lib/include/tick/array/vector_operations.h", "max_stars_repo_name": "sumau/tick", "max_stars_repo_head_hexsha": "1b56924a35463e12f7775bc0aec182364f26f2c6", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
import warnings
import numpy as np
import numpy.linalg as la
import numpy.random as rnd
from pymanopt.manifolds.manifold import EuclideanEmbeddedSubmanifold
class _Sphere(EuclideanEmbeddedSubmanifold):
"""Base class for tensors with unit Frobenius norm."""
def __init__(self, *shape, name, dimension):
... | {"hexsha": "73f3a1c9dc76ca62b023f8a43eeda5ba10b97d38", "size": 5816, "ext": "py", "lang": "Python", "max_stars_repo_path": "manifolds/sphere.py", "max_stars_repo_name": "cjyaras/pymanopt", "max_stars_repo_head_hexsha": "447545fd9a6f33f3060a083fde1a2ac643ed340e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
subroutine runP_i(k,i1,f,Gr,Shat2,N0)
implicit none
include 'pvDnames.f'
include 'pvDv.f'
include 'Darraydef.f'
include 'Darrays.f'
integer ep,N0,k,i1,np
parameter(np=3)
double precision f(np),Gr(np,np)
double complex Shat2(np,np,-2:0)
do ep=-2,0... | {"hexsha": "13e8b640cd39501d0c24d0d6f403cde9061eaf4e", "size": 577, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/TensorReduction/recur/smallP/runP_i.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma composition_of_substs_eq :
shows "(subst_equation (subst_equation e \<sigma>) \<eta>)
= (subst_equation e (comp \<sigma> \<eta>))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subst_equation (subst_equation e \<sigma>) \<eta> = subst_equation e (\<sigma> \<lozenge> \<eta>)
[PROOF STEP]
by ... | {"llama_tokens": 140, "file": "SuperCalc_equational_clausal_logic", "length": 1} |
[STATEMENT]
lemma wfT_fun_return_t:
fixes \<tau>a'::\<tau> and \<tau>'::\<tau>
assumes "\<Theta>; \<B>; (xa, b, ca) #\<^sub>\<Gamma> GNil \<turnstile>\<^sub>w\<^sub>f \<tau>a'" and "(AF_fun_typ x b c \<tau>' s') = (AF_fun_typ xa b ca \<tau>a' sa')"
shows "\<Theta>; \<B>; (x, b, c) #\<^sub>\<Gamma> GNil \<tur... | {"llama_tokens": 6078, "file": "MiniSail_WellformedL", "length": 29} |
import pandas as pd
import numpy as np
from pycaret.classification import predict_model, load_model
def load_data(filepath):
"""
Loads churn data into a DataFrame from a string filepath.
"""
df = pd.read_csv(filepath, index_col='customerID')
return df
def make_predictions(df):
"""
Uses th... | {"hexsha": "be37f8db7b78c231762b5cfa59214910a6dd6ae8", "size": 1679, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict_churn.py", "max_stars_repo_name": "kodama3d/msds_600_week5", "max_stars_repo_head_hexsha": "746fbc1bb92811e4ef568a8220a1c93301d14909", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import GitLab
using Base.Test
myauth = GitLab.authenticate(ENV["GITLAB_AUTH"]) # don't hardcode your access tokens!
println("Authentication successful")
options = Dict("private_token" => myauth.token)
myrepo = GitLab.repo_by_name("TestProject1"; headers=options)
file = GitLab.file(myrepo, "src/file1", "master"; heade... | {"hexsha": "af1305dd4b0fb9abe489e06f6c0fe8436c771229", "size": 410, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/contents.jl", "max_stars_repo_name": "UnofficialJuliaMirror/GitLab.jl-ec55e9df-579d-5e55-a10d-b795213e2edd", "max_stars_repo_head_hexsha": "28fc1fc4f561b027ebf117f1bc7a497252f47123", "max_stars... |
"""
Propagation module
"""
# struct for handling information of the problem
struct ShootingSettings
n::Int
nr::Int
n_sv::Int
prob_stm::ODEProblem
tofs::Array
r0::Array # fixed vector
rf::Array # fixed vector
v0::Array
vf::Array
method
reltol::Float64
abstol::Float64
tolDC::Float64
... | {"hexsha": "c5f66a16623bd8bcd3b3d585551bf7cacefd132f", "size": 5257, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/twostage_shooting.jl", "max_stars_repo_name": "Yuricst/ShootingStar.jl", "max_stars_repo_head_hexsha": "89243d55bb53ea28c25719767d9addef20dcdb4f", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import matplotlib.pyplot as plt
values_mV = np.loadtxt("ljpValues.txt")
values_uV = values_mV * 1000
valuesMean_uV = np.mean(values_uV)
valuesMean_mV = valuesMean_uV / 1000.0
baselineValues_uV = values_uV - valuesMean_uV
n, bins, patches = plt.hist(baselineValues_uV, 100)
details = "n: %d, mean: %f ... | {"hexsha": "abf1265de170651ca42b53859d960dd72db98762", "size": 568, "ext": "py", "lang": "Python", "max_stars_repo_path": "dev/variance/calculateVariance.py", "max_stars_repo_name": "swharden/JLJP", "max_stars_repo_head_hexsha": "9644ceb2d7a6ed5c61f94e535e556a0887ef0753", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import pandas as pd
from feature_selection import read_data
from keras.models import Sequential
from keras.layers import Dense
from sklearn import svm
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import (accuracy_score, f1_score, precision_score... | {"hexsha": "9110695003914a46f145bf493da85108a8f998ed", "size": 5964, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/classification.py", "max_stars_repo_name": "federico-code/PedMS-Classification", "max_stars_repo_head_hexsha": "04215b78dd299463cf5ae6ca33c1deb3d49acd41", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma (in comm_group) subgroup_iso_DirProds_IDirProds:
assumes "subgroup J G" "is_idirprod J S I" "finite I"
shows "(\<lambda>x. \<Otimes>\<^bsub>G\<^esub>i\<in>I. x i) \<in> iso (DirProds (\<lambda>i. G\<lparr>carrier := (S i)\<rparr>) I) (G\<lparr>carrier := J\<rparr>)"
(is "?fp \<in> iso ?DP ?J")
[PR... | {"llama_tokens": 15144, "file": "Finitely_Generated_Abelian_Groups_DirProds", "length": 98} |
import numpy as np
class Tree(object):
def __init__(self, dim, rank):
self.dim = dim
self.rank = rank
self.nodes = []
self.edges = []
def check_structure(matrix):
"""Check if a given matrix is a vine array and should respect the conditions a regular vine.
Para... | {"hexsha": "9a93edf82d4cc8b3a90d408921481971c887981e", "size": 2620, "ext": "py", "lang": "Python", "max_stars_repo_path": "depimpact/model.py", "max_stars_repo_name": "NazBen/dep-impact", "max_stars_repo_head_hexsha": "284e72bccfb6309110df5191dfae3c0a93ce813b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#----------------------------------------------------------------
# When run this script, plwase consider whether there is a need to comment line
# 741-746 in Env2DCyliner (just to make sure baseline runs longer time than
# single run session)
#
# The default running time steps are used for Re=200, and it may run a
... | {"hexsha": "f1a2a9dc712c49937233cb8120a4cbea355171d8", "size": 3733, "ext": "py", "lang": "Python", "max_stars_repo_path": "RobustDRLCylinder2DControl/simulation_base/baseline_single_run.py", "max_stars_repo_name": "thw1021/Cylinder2DFlowControlGeneral", "max_stars_repo_head_hexsha": "ac5ea7fd304dedefa4489790e45c42ca94... |
"""Class that schedules motion on can bus."""
import asyncio
from collections import defaultdict
import logging
from typing import List, Set, Tuple, Iterator, Union
import numpy as np
from opentrons_hardware.firmware_bindings import ArbitrationId
from opentrons_hardware.firmware_bindings.constants import NodeId
from o... | {"hexsha": "f3b765c7643dbde437e1a2d4765e0d9891e6d6e7", "size": 16281, "ext": "py", "lang": "Python", "max_stars_repo_path": "hardware/opentrons_hardware/hardware_control/move_group_runner.py", "max_stars_repo_name": "Opentrons/protocol_framework", "max_stars_repo_head_hexsha": "ebbd6b2fe984edd6ecfcbf1dbe040db7f7356b9f"... |
#!/usr/bin/env python3
import sys
try:
#TODO this is a hack, at minimum should be done s.t. it'll work for aaaany ros distribution
sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
sys.path.append('/opt/ros/kinetic/lib/python2.7/dist-packages')
except Exception as e:
print(e)
print("no... | {"hexsha": "7ce65a01169f459ed9442d59105a5e88cfee4f16", "size": 4156, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/environments/simple_tracker.py", "max_stars_repo_name": "gamerDecathlete/neural_jacobian_estimation", "max_stars_repo_head_hexsha": "44deed91f0650830dd2da1796e67d084f0918995", "max_stars_repo_... |
/**
* Copyright (c) 2015 Eugene Lazin <4lazin@gmail.com>
*
* 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 app... | {"hexsha": "18fa38c904d89c7f9f2e60e079e581a4c9ab6fe3", "size": 6202, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libakumuli/seriesparser.cpp", "max_stars_repo_name": "vladon/Akumuli", "max_stars_repo_head_hexsha": "c45672a23b929ccb3a5743cc5e9aae980c160eb0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
#!/usr/bin/env python
"""
Local Processing Unit (LPU) with plugin support for various neuron/synapse models.
"""
import time
import collections
import numbers
import copy
import itertools
from future.utils import iteritems
from past.builtins import long
from builtins import zip
import pycuda.gpuarray as garray
from... | {"hexsha": "ffe97310fe40db1979f26e7e8035250b02684660", "size": 60005, "ext": "py", "lang": "Python", "max_stars_repo_path": "neurokernel/LPU/LPU.py", "max_stars_repo_name": "yiyin/neurodriver", "max_stars_repo_head_hexsha": "34e6874a1cf35633cda1191920cbaeac5d25dc9b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
\subsection{Burn}
\label{sec:721Burn}
We continue with the notation and indices from the prior sections.\\
\\
Suppose Bob is the owner of the token commitment $Z_B$ which represents the ERC-721 asset with tokenId $\alpha$ (as discussed in the prior section).
The asset $\alpha$ can continue to be transferred unde... | {"hexsha": "db6d2ec8d49bc9b65b8c629317536b2194675556", "size": 21278, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/whitepaper/protocols/ERC721/burn721.tex", "max_stars_repo_name": "roggerJose/nightfall", "max_stars_repo_head_hexsha": "59d7d83bcaf920bbf7c7427d14b5fcdb7e53843c", "max_stars_repo_licenses": ["C... |
#ifndef SIMPLEBOT_DRIVER_HPP
#define SIMPLEBOT_DRIVER_HPP
#include <JetsonGPIO.h>
#include <string>
#include <cmath>
#include <boost/asio.hpp>
#include "nlohmann/json.hpp"
#include <ros/ros.h>
typedef struct {
int pinPWM;
int pinDirA;
int pinDirB;
} pinInfo;
typedef struct{
int left;
int right;
} encoderD... | {"hexsha": "a2ae4cd0154c2a1fa398b19d292dc255b584625c", "size": 902, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "simplebot_hardware/include/simplebot_hardware/simplebot_driver.hpp", "max_stars_repo_name": "yukimakura/simplebot", "max_stars_repo_head_hexsha": "21625af59bbfd70b63e6881a02e41dc0ed67c3fa", "max_star... |
from typing_extensions import SupportsIndex
from django.shortcuts import render
# Create your views here.
from django.http import HttpResponse
from .forms import InputForm
import pandas as pd
import numpy as np
import pickle
from pymongo import MongoClient
client = MongoClient('localhost', 27017)
db = cli... | {"hexsha": "f71650e79d12d7042562e07291e570fa83922710", "size": 2081, "ext": "py", "lang": "Python", "max_stars_repo_path": "heart_app/views.py", "max_stars_repo_name": "kylepgr/heart-disease-pred", "max_stars_repo_head_hexsha": "d128cc815dde4839ba18e887113bb47387499ce1", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#=
Similar to Batch Normalization, except online and without the rescaling/skew
y = (a .- μ) ./ σ
TODO: This is currently broken because OnlineStats.Variances no longer
exists.
=#
type InputNorm{T,W<:Weight} <: Transformation
n::Int # maps n --> n
vars::Variances{W}
input::SumNode{T,1} # ... | {"hexsha": "f1fba208cc66623f99cac30d8ade1ef4de191ae0", "size": 1306, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/inputnorm.jl", "max_stars_repo_name": "JuliaTagBot/Transformations.jl", "max_stars_repo_head_hexsha": "96041d4b3ef913c3f2f2e7cd65f9090cf40bb851", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
!+ gaseous absorption after Rosenkranz 98 model
subroutine rosen98_gasabs &
(errorstatus,& ! out
freq, & ! in
tempK, & ! in
rhoWv, & ! in
pres, & ! in
absAir,& ! out
absWv ) ! out
! Description:
! Based on frequency, temperature, water vapor density, and pressure, thi... | {"hexsha": "373a6051c49f566f28fea02c60afac0984cf2b59", "size": 4830, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "rosen98_gasabs.f90", "max_stars_repo_name": "DaveOri/pyGasAbs", "max_stars_repo_head_hexsha": "966eae26d0d356f33bb8964c912d63e2fffe2345", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# test on generic type
using Pseudospectra, Test, GenericSVD
@testset "Generic(Big)" begin
@info("This test is expected to comment about lack of methods for BigFloat eigvals")
A = Matrix{BigFloat}(Pseudospectra.grcar(8))
# ax is needed until ∃ eigvals(BigFloat)
opts = Dict{Symbol,Any}(:ax => [-1,3,-3... | {"hexsha": "4a26464f11fba634c95938324db727ba288bb2f0", "size": 867, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/big.jl", "max_stars_repo_name": "ranocha/Pseudospectra.jl", "max_stars_repo_head_hexsha": "63b3c62dc34707289bb73959bbeaee0529ecee36", "max_stars_repo_licenses": ["BSD-3-Clause", "MIT"], "max_st... |
! { dg-do compile { target { ! *-*-* } } }
!
program bug
use H5GLOBAL
implicit none
integer :: i
i=H5P_DEFAULT_F
end program bug
| {"hexsha": "5eee0986b809806b127477ae63996d8926cb70c5", "size": 137, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/pr77420_4.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_licens... |
using Test
using PyCall
using BenchmarkTools
using Fermi
using Fermi.Wavefunction
using Fermi.CoupledCluster: RCCSD,RCCD,DFRCCD
psi4.core.be_quiet() #turn off output
psi4.set_num_threads(6)
using LinearAlgebra
BLAS.set_num_threads(6)
# > setup
tol = 1E-14
psi4.set_options(Dict("D_CONVERGENCE" => 14,
... | {"hexsha": "7bfa0bc84a856aa2a79cd95158ff535b61c3aabe", "size": 1052, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/BenchCoupledCluster.jl", "max_stars_repo_name": "jturney/Fermi.jl", "max_stars_repo_head_hexsha": "46a22ee67b0de7496c6767cb73c71d21dce397cf", "max_stars_repo_licenses": ["MIT"], "max_star... |
# importing modules and packages
# system tools
import os
import sys
import argparse
sys.path.append(os.path.join("..", ".."))
from contextlib import redirect_stdout
# pandas, numpy, gensim
import pandas as pd
import numpy as np
import gensim.downloader
# import my classifier utility functions - see the Github repo!
... | {"hexsha": "ade713c52195e4d636a5f8f37736575316cae117", "size": 5648, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/4/lr_got.py", "max_stars_repo_name": "marmor97/cds-language-exam", "max_stars_repo_head_hexsha": "d6f1aa543ba3f78d1a9f34c67a687e4f0944a665", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
Require Import List.
Require Import ZArith.
Require Import String.
Open Scope string_scope.
Ltac inv H := inversion H; subst.
Ltac break_match :=
match goal with
| _ : context [ if ?cond then _ else _ ] |- _ =>
destruct cond as [] eqn:?
| |- context [ if ?cond then _ else _ ] =>
destruct cond as ... | {"author": "palmskog", "repo": "street-fighting-proof-assistants", "sha": "f89660fab17a8c1a6c9cd9c14484ed8d72fb0088", "save_path": "github-repos/coq/palmskog-street-fighting-proof-assistants", "path": "github-repos/coq/palmskog-street-fighting-proof-assistants/street-fighting-proof-assistants-f89660fab17a8c1a6c9cd9c144... |
import argparse
import math
import numpy as np
import torch
from torch import nn
from basicsr.archs.stylegan2_arch import StyleGAN2Generator
from basicsr.metrics.fid import (calculate_fid, extract_inception_features,
load_patched_inception_v3)
def calculate_stylegan2_fid():
devic... | {"hexsha": "8a3d8a978fa0f5971f6d03aaa3d6add2fff87013", "size": 2851, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/modules/BasicSR/scripts/metrics/calculate_stylegan2_fid.py", "max_stars_repo_name": "harryprabowo/multipurpose-iris-recognition", "max_stars_repo_head_hexsha": "549cb95499abb3ea7900aa29ced55a... |
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