text stringlengths 0 27.1M | meta dict |
|---|---|
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.optim as optim
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision.datasets as datasets
import torchvision.utils as utils
import numpy as np
import time
import torch.nn.functional as F
from tor... | {
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import os
import glob
import subprocess
from numpy import testing
import numpy as np
from numpy.core.numeric import NaN
from numpy.lib.function_base import average
from numpy.lib.shape_base import split
import pandas as pd
import json
import io
import matplotlib.pyplot as plt
# Genetic algorithm in the future?
"""
j... | {
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[STATEMENT]
lemma source_all_outarcs_T:
"\<lbrakk>undirected_tree G; tail G e = root; e \<in> arcs G\<rbrakk> \<Longrightarrow> e \<in> arcs T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>undirected_tree G; tail G e = root; e \<in> arcs G\<rbrakk> \<Longrightarrow> e \<in> arcs T
[PROOF STEP]
using sou... | {
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\documentclass[11pt, compress]{beamer}
\usepackage{preamb}
\usepackage{tikz,tkz-tab}
\usepackage{tkz-euclide}
\usepackage{movie15}
\usepackage{hyperref}
\setbeamertemplate{navigation symbols}{}
\usetheme{Warsaw}
\setbeamertemplate{theorem begin}{{
\inserttheoremheadfont
\inserttheoremname
\inserttheorempunctuation
}... | {
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# -*- coding: utf-8 -*-
"""This module is mean to be used to get the main training data for train the model to be used on ml_rivets.mll node
This code is to be used on maya with numpy library
MIT License
Copyright (c) 2020 Mauro Lopez
Permission is hereby granted, free of charge, to any person obtaining a copy
of th... | {
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"""
Cython wrapper to provide python interfaces to
PROJ.4 (http://trac.osgeo.org/proj/) functions.
Performs cartographic transformations and geodetic computations.
The Proj class can convert from geographic (longitude,latitude)
to native map projection (x,y) coordinates and vice versa, or
from one map projection coor... | {
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"""
Unified place for determining if external dependencies are installed or not.
You should import all external modules using the import_module() function.
For example
>>> from sympy.external import import_module
>>> numpy = import_module('numpy')
If the resulting library is not installed, or if the installed versi... | {
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import glob
from pathlib import Path
import cv2
import matplotlib.pyplot as plt
import numpy as np
from keras import models
from matplotlib import image as mpimg
if __name__ == '__main__':
# Visualizing intermediate activation in Convolutional Neural Networks with Keras
# https://github.com/gabrielpierobon/cn... | {
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import tensorflow as tf
import numpy as np
import logging.config
import functions
import json
from datetime import datetime
np.set_printoptions(suppress=True)
# 1 - logging
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = "tf_logs"
logdir = "{}/run-{}/".format(root_logdir, now)
with open('./logging_con... | {
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"""
Profile groupfitter
See where the bulk of computation occurs.
Examples on how to profile with python
https://docs.python.org/2/library/profile.html
"""
import cProfile
import logging
import numpy as np
import pstats
import sys
sys.path.insert(0, '..')
from chronostar.synthdata import SynthData
from chronostar i... | {
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from Attribute import Attribute
from Utility import Utility
from Observation import Observation
from HyperParameters import HyperParameters
from Memory import Memory
import numpy
import random
import math
class CapsuleMemory(Memory):
def __init__(self):
self._observations : list = list() # O... | {
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import io
import numpy as np
import sys
import tensorflow as tf
import matplotlib
backend = 'Agg' if sys.platform == 'linux' else 'TkAgg'
matplotlib.use(backend)
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
def _build_network(
name,
inputs,
hidden_layer_dims,
... | {
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import numpy as np
def make_sphere(ball_shape, radius, position):
"""
Assumes shape and position are both a 3-tuple of int or float
the units are pixels / voxels (px for short)
radius is a int or float in px
:param tuple(int) ball_shape:
:param float radius:
:param tuple(int) position:
... | {
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#!/bin/python
import numpy as np
import torch
import matplotlib.pyplot as plt
from spiking import SpikingLGN
from torchvision.datasets import MNIST
from torchvision import transforms
if __name__ == "__main__":
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {devic... | {
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# -*- coding: utf-8 -*-
"""Console script for star_pairs."""
import click
import readline
import numpy as np
import matplotlib.pyplot as plt
import time
import datetime
from time import localtime
from time import strftime
import math
import os
import pkg_resources
# _Define constants:
LATITUDE = '-30d14m26.700s' ... | {
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subroutine exco_read_salt
use hydrograph_module
use input_file_module
use organic_mineral_mass_module
use maximum_data_module
use exco_module
use constituent_mass_module
character (len=80) :: titldum, header
integer :: eof, imax, ob1, ob2
logical :: i_e... | {
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#! /usr/bin/env python
import rospy
import cv2
import sys
import numpy
import moveit_commander
import moveit_msgs.msg
import geometry_msgs.msg
import os
from cv_bridge import CvBridge
import shlex
import subprocess
import time
from sensor_msgs.msg import Image, CameraInfo, PointCloud2
# Switch controller server ... | {
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function r=rot4y(theta)
% r = rot4y(theta)
%
% roty produces a 4x4 rotation matrix representing
% a rotation by theta radians about the y axis.
%
% Argument definitions:
%
% theta = rotation angle in radians
c = cos(theta);
s = sin(theta);
r = [c 0 s 0;
0 1 0 0;
... | {
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module LakeTributaryModule
use ConstantsModule, only: DZERO, LENPACKAGENAME
use ListModule, only: ListType
private
public :: LakeTributaryType, ConstructLakeTributary, &
CastAsLakeTributaryType, AddLakeTributaryToList, &
GetTributaryFromList
type LakeTributaryType
integer ... | {
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[STATEMENT]
lemma nsqn_update_other [simp]:
fixes dsn dsk flag hops dip nhip pre rt ip
assumes "dip \<noteq> ip"
shows "nsqn (update rt ip (dsn, dsk, flag, hops, nhip)) dip = nsqn rt dip"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. nsqn (update rt ip (dsn, dsk, flag, hops, nhip)) dip = nsqn rt dip
[PROO... | {
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from torch.utils.data import DataLoader, Dataset
from torchvision.datasets import MNIST
import warnings
from typing import Dict, IO, Union
import os
import numpy as np
import torch
import codecs
import gzip
import lzma
from torchvision.datasets.utils import download_and_extract_archive
import cv2
from examples.mnist.ge... | {
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# Author: Hiroharu Sugawara <hsugawa@gmail.com>
# Copyright: (C) 2020 Hiroharu Sugawara
# Author: Eric P. Hanson
# Copyright: (C) 2018? Eric P. Hanson
# Author: Martin Vuk <martin.vuk@fri.uni-lj.si>
# Copyright: (C) 2016 Martin Vuk
# License: BSD 3-clause
"""
PandocFiltersLiveJuliaCode
Package to aid writing... | {
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@testset "basic" begin
@testset "GNNChain" begin
n, din, d, dout = 10, 3, 4, 2
g = GNNGraph(random_regular_graph(n, 4),
graph_type=GRAPH_T,
ndata= randn(Float32, din, n))
gnn = GNNChain(GCNConv(din => d),
Batch... | {
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import py, os, sys
from pytest import raises
import numpy as np
sys.path = [os.pardir] + sys.path
class TestOPTIMIZERS:
def setup_class(cls):
pass
def reset(self):
import SQSnobFit
# reset the random state for each method to get predictable results
SQSnobFit._gen_utils._randsta... | {
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#!/usr/bin/env python
"""
Hackable script to find threshold values.
NOTE(danny): Saved because I've needed code like this so many times
"""
import cv2
import numpy as np
# NOTE(danny): camera id goes here (or video file path)
cap = cv2.VideoCapture(1)
def nothing(x):
pass
# Creating a window for later use
cv2.n... | {
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from pathlib import Path
import json
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from ..abs_model_controller import ControllerBase
from .make_data_loader import get_loader
from . import trainer
from . import predictor
from . import saver
from ...models.retrain_clf.model import Mo... | {
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# Databricks notebook source
import matplotlib.pyplot as plt
from pyspark.sql import functions as F
import matplotlib.mlab as mlab
from matplotlib.ticker import MaxNLocator
from pyspark.ml.feature import VectorAssembler
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
from pyspark.ml.feature import MaxAbsScal... | {
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[STATEMENT]
lemma (in trace_top) LNil_safety: "safety A {LNil}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. safety A {LNil}
[PROOF STEP]
proof (unfold safety_def, clarify)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<And>t. \<lbrakk>t \<in> A\<^sup>\<infinity>; \<forall>r\<in>finpref A t. \<exists>s\<in>A\... | {
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def make_data(initial_data):
prev_data = initial_data['test'][0]
datas = pd.DataFrame(initial_data)
data = []
for i in range(1440):
arr = []
for j in range(60):
temp = prev_data * np.random.unifo... | {
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import numpy as np
def base_fun(key):
if key == 0:
return lambda x: x
if key == 1:
return lambda x: np.sin(x * 20)
if key == 2:
return lambda x: np.exp((x - 0.5) * 50) / (np.exp((x - 0.5) * 50) + 1)
if key == 3:
return lambda x: (np.arctan(x * 10) + np.sin(x * 10)) / 2
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function, unicode_literals
import json
import re
from bs4 import BeautifulSoup
from nltk.stem.porter import PorterStemmer
import numpy as np
from tqdm import trange
from . import HEATMAP_CSS_PATH
from .textutil import normal... | {
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from itertools import product
import numpy as np
import param
from ...core import CompositeOverlay, Element
from ...core import traversal
from ...core.util import match_spec, max_range, unique_iterator, unique_array, is_nan
from ...element.raster import Image, Raster, RGB
from .element import ColorbarPlot, OverlayPlo... | {
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import torch
import torch.nn.functional as F
import numpy as np
from utils.processing import BoundingBox
import cv2
def train(model, train_loader, optimizer, criterion, epoch, device, log_interval=175):
""" Function to train the model
Args:
model (nn.model object): Model to be trained
... | {
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\documentclass[a4paper, 11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage[lmargin=3cm]{geometry}
\usepackage{amssymb}
\usepackage{verbatim}
\hyphenation{FORTRAN NEKBONE}
%Inlucde common settings
\input{../BPG/deliverables-config.tex}
\newenvironment{code}%
{
\addtolength{\leftskip}{0.5cm}}%
{
}
\begin{documen... | {
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# -*- coding: utf-8 -*-
"""
Created on Thu May 26 12:25:24 2016
@author: kbefus
"""
import os,sys
import numpy as np
kbpath = r'C:/Research/Coastalgw/Model_develop/'
sys.path.insert(1,kbpath)
from cgw_model.prep import prep_utils as cprep
#%%
class CRM(object):
'''
'''
def ... | {
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import pandas as pd
import numpy as np
from .utils.numerical_utils import gaussian_kde
from ._configs import *
import sys
__all__ = ["identify_metastable_states", "approximate_FES"]
def identify_metastable_states(
colvar,
selected_cvs,
kBT,
bandwidth,
logweights=None,
f... | {
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import contextlib
from detectron2.data import DatasetCatalog, MetadataCatalog
from fvcore.common.timer import Timer
from fvcore.common.file_io import PathManager
import io
import logging
from detectron2.data.datasets.cityscapes import load... | {
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#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/Geometry>
#include "entity.hpp"
namespace cuauv {
namespace fishbowl {
entity::entity(double m, double r, const inertia_tensor& I, const Eigen::Quaterniond& btom_rq)
: m(m)
, r(r)
, I(I)
, btom_rq(btom_rq)
, btom_rm(btom_rq.matrix())
... | {
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"""Base classes for creating GUI objects to create manually selected points.
The definition of X,Y axis is the following:
xmin,ymin o---------o xmax,ymin
| |
| |
| |
| |
xmin,ymax o---------o xmax,ymax
"""
from __future__ import abs... | {
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import argparse
import datetime
import os
from supervised_model.sup_model import Frontend
from utils import config as cfg
import time
import numpy as np
import torch
import wandb
from torch.utils.tensorboard import SummaryWriter
from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer
f... | {
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from .context import assert_equal
import pytest
from sympy import Sum, I, Symbol, Integer
a = Symbol('a', real=True, positive=True)
b = Symbol('b', real=True, positive=True)
i = Symbol('i', real=True, positive=True)
n = Symbol('n', real=True, positive=True)
x = Symbol('x', real=True, positive=True)
def test_complex(... | {
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# Code from Chapter 18 of Machine Learning: An Algorithmic Perspective (2nd Edition)
# by Stephen Marsland (http://stephenmonika.net)
# You are free to use, change, or redistribute the code in any way you wish for
# non-commercial purposes, but please maintain the name of the original author.
# This code comes with no... | {
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# Author: weiwei
import numpy as np
from .metric import BaseMetric, filter_parameters, Compose
from .functional.sixd import projection_2d, add, cm_degree, add_error, add_auc, nearest_point_distance, angular_error, \
translation_error
# from leaf.metrics.metric import BaseMetric, filter_parameters, Compose
# from ... | {
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#Problem 20:
#n! means n × (n − 1) × ... × 3 × 2 × 1
#For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
#and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
#Find the sum of the digits in the number 100!
import sympy as sp
def main(num):
val = sp.factorial(num)
summa = 0
va... | {
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%!TEX TS-program = lualatex
%!TEX encoding = UTF-8 Unicode
\documentclass[letterpaper]{tufte-handout}
%\geometry{showframe} % display margins for debugging page layout
\usepackage{fontspec}
\def\mainfont{Linux Libertine O}
\setmainfont[Ligatures={Common,TeX}, Contextuals={NoAlternate}, BoldFont={* Bold}, ItalicFont=... | {
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import numpy as np
import matplotlib.pyplot as plt
def plot_bar_figure(
k8s,
nomad,
swarm,
ylabel,
xlabel,
xtick_labels,
figure_path
):
ind = np.arange(len(xtick_labels))
width = 0.27
fig = plt.figure()
ax = fig.add_subplot(111)
k8s_rects = ... | {
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function banner()
FIGlet.render("XtalsPyTools", FIGlet.availablefonts()[286])
end
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[STATEMENT]
lemma preserves_cones:
fixes J :: "'j comp"
assumes "cone J A D a \<chi>"
shows "cone J B (F o D) (F a) (F o \<chi>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. cone J (\<cdot>\<^sub>B) (F \<circ> D) (F a) (F \<circ> \<chi>)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal... | {
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# based on: https://github.com/rlcode/per
import numpy
import random
import numpy as np
# stored as ( s, a, r, s_ ) in SumTree
class PrioritizedReplayBuffer:
def __init__(self, capacity, alpha=0.6, beta=0.4, beta_increment_per_sampling=0.001, e=0.01):
self.tree = SumTree(capacity)
self.alpha = alp... | {
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import gym
import numpy as np
import sys
import matplotlib
from random import randint
if "../" not in sys.path:
sys.path.append("../")
from collections import defaultdict
from lib.envs.blackjack import BlackjackEnv
from lib import plotting
matplotlib.style.use('ggplot')
env = BlackjackEnv()
def make_epsilon_gree... | {
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"""
This function finds the hourly availability data for projects
to be passed to expected and actual market clearing modules.
"""
function find_project_availability_data(project::P,
availability_df::DataFrames.DataFrame,
num_invperiods::Int64,
... | {
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# -*- coding: utf-8 -*-
import numpy as np
from numpy import testing
from sktime.classification.dictionary_based import BOSSEnsemble, BOSSIndividual
from sktime.datasets import load_gunpoint, load_italy_power_demand
def test_boss_on_gunpoint():
# load gunpoint data
X_train, y_train = load_gunpoint(split="tra... | {
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#!/usr/bin/env python
# coding: utf-8
# ### This script was used to create a wikipedia linkfile for the entities in the FB15k and not the complete Freebase
import utils
import argparse
import logging
import os
import pickle
import string
import time
import numpy as np
import pandas as pd
data_repo_root = "../data/fb... | {
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source('covidmap/stage1.r')
# Wrapper script to recombine the results produced
# inparallel from a run of stage1_run.r
opt = covidmap_stage1_get_cmdline_options()
covidmap_stage1_combine(opt)
| {
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theory Variable
imports Main
begin
datatype var = V nat
primrec fresh' :: "var set \<Rightarrow> nat \<Rightarrow> nat" where
"fresh' xs 0 = 0"
| "fresh' xs (Suc x) = (if V (Suc x) \<in> xs then fresh' (xs - {V (Suc x)}) x else Suc x)"
definition fresh :: "var set \<Rightarrow> var" where
"fresh xs = V (fresh'... | {
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function [fs, bmg] = slfiltersize(fs0)
%SLFILTERSIZE Extracts information from filtersize
%
% $ Syntax $
% - [fs, bmg] = slfiltersize(fs0)
%
% $ Arguments $
% - fs0: The input filter size
% - fs: The full filter size form
% - bmg: The boundary margins
%
% $ Description $
% - [fs, bmg] = slfilt... | {
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function sdl_colors(c::Colorant)
sdl_colors(
convert(ARGB{Colors.FixedPointNumbers.Normed{UInt8,8}}, c)
)
end
sdl_colors(c::ARGB) = Int.(reinterpret.((red(c), green(c), blue(c), alpha(c))))
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import sys
import numpy as np
import myface.face as face
import myface.utils.utils as utils
image = utils.load_image('./fig/fig1.jpeg')
res = face.detect_face_and_encode(image)
print(res['encoded_faces'].__len__)
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#!/usr/bin/env python
"""
Convnets for image classification (1)
=====================================
"""
import numpy as np
import deeppy as dp
import matplotlib
import matplotlib.pyplot as plt
# Fetch MNIST data
dataset = dp.dataset.MNIST()
x_train, y_train, x_test, y_test = dataset.data(dp_dtypes=True)
# Bring... | {
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import tensorrt as trt
import numpy as np
import os
import cv2
import torch
from efficientdet.scripts.utils import *
#from utils import *
import re
TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE)
def get_engine(model_path: str):
if os.path.exists(model_path) and model_path.endswith('trt'):
print(f"Reading ... | {
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import sys
import os
import numpy as _np
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../src/")
import finoptions as fo
def test_monte_carlo():
S = 100
K = 100
t = 1 / 12
sigma = 0.4
r = 0.10
b = 0.1
path_length = 30
mc_samples = 5000
mc_loops = 50
eps ... | {
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###### Content under Creative Commons Attribution license CC-BY 4.0, code under MIT license (c)2014 L.A. Barba, C.D. Cooper, G.F. Forsyth.
# Spreading out
Welcome to the fifth, and last, notebook of Module 4 "_Spreading out: diffusion problems,"_ of our fabulous course **"Practical Numerical Methods with Python."**
... | {
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//==================================================================================================
/**
Copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
//=====================================... | {
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import sys
if sys.version_info.major < 3:
from customaxesimage import CustomAxesImage
else:
from lib.customaxesimage import CustomAxesImage
import numpy as np
import math
from kernel import setupkernel,setupintegratedkernel
import globals
if globals.debug > 0: from time import time
try:
from numba import ... | {
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import csv
import logging
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
import random
import statistics as stat
import glob
import os
import yaml
logger = logging.getLogger(__name__)
def set_size(w,h, ax=None):
""" w, h: width, height in inches """
if not ax: ax=plt.gca()
l =... | {
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'''
Created on Sep 23, 2021
@author: immanueltrummer
'''
import codexdb.code.generic
from contextlib import redirect_stdout
from io import StringIO
import pandas as pd
import sys
class PythonGenerator(codexdb.code.generic.Generator):
""" Generates Python code. """
def execute(self, db_id, question, gene... | {
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#=
This is a very crude first stab at the Tables.jl interface
https://github.com/JuliaData/Tables.jl
=#
using Tables
Tables.istable(::Type{<:KeyedArray}) = true
Tables.rowaccess(::Type{<:KeyedArray}) = true
function Tables.rows(A::Union{KeyedArray, NdaKa})
L = hasnames(A) ? (dimnames(A)..., :value) : # should ... | {
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#!/usr/bin/env python
#
# Copyright 2019 DFKI GmbH.
#
# 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 limitation the rights to use, copy, modify, merg... | {
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import inspect
import sys
import itertools
import random
from abc import ABC, abstractproperty
from distutils.version import LooseVersion
import base64
import hashlib
import logging
import os
from typing import Union
import cv2
import numpy as np
from ipso_phen.ipapi.base.ip_abstract import BaseImagePr... | {
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#[0] is ours
##whole_level[1] calais
#[2] ritter
#[3] stanford
import datetime
from threading import Thread
import random
import math
from queue import Queue
import pandas as pd
import warnings
import numpy as np
import time
import pickle
import matplotlib.pyplot as plt
import copy
import matplotlib.ticker as ticker... | {
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[STATEMENT]
lemma lr_of_tran_fbs_acceptD:
assumes s1: "valid_prefixes rt" "has_default_route rt"
assumes s2: "no_oif_match fw"
shows "generalized_sfw (lr_of_tran_fbs rt fw ifs) p = Some (r, oif, simple_action.Accept) \<Longrightarrow>
simple_linux_router_nol12 rt fw p = Some (p\<lparr>p_oiface := oif\<rparr>)"
... | {
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from pathfinding.core.diagonal_movement import DiagonalMovement
from pathfinding.core.grid import Grid
from pathfinding.finder.best_first import BestFirst
import entities as ent
import numpy as np
finder = BestFirst(diagonal_movement=DiagonalMovement.never)
def sub(a,b):
n=a[0]-b[0]
c=a[1]-b[1]
nc=(n,c)
... | {
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subroutine setlats_r(lats_nodes_r,global_lats_r,iprint,lonsperlar)
!
use mod_param, only: nodes,latr,lonr,icolor,liope
implicit none
!
integer lats_nodes_r(nodes)
integer global_lats_r(latr)
integer iprint,opt,ifin,nodesio
integer ... | {
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#-*-coding:utf-8-*-
import csv
import re
import pandas as pd
import gensim
import nltk
from nltk.corpus import stopwords
import numpy as np
import math
import ssl
import random
import collections
def cos_sim(a, b):
a_norm = np.linalg.norm(a)
b_norm = np.linalg.norm(b)
cos = np.dot(a,b)/(a_norm * b_norm)
... | {
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# Copyright 2019-2020 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" fil... | {
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#!/usr/bin/env python
import argparse
import numpy as np
import scipy.io as sio
import os
import sys
sys.path.insert(1, '.')
import h5py
from vdetlib.vdet.dataset import imagenet_vdet_classes
from vdetlib.utils.common import quick_args
from vdetlib.utils.protocol import proto_load, proto_dump, bbox_hash
import gzip
im... | {
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import os
import re
from conllu import *
import pandas as pd
import numpy as np
from collections import Counter
def get_lexique_maju_mini(lexique):
''' Standardize the capitalization of words, with only PROPN capitalized '''
lexique_maju_mini = list()
for terme in lexique:
if terme[1]==... | {
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import random
import torch
import logging
import copy
import os
import numpy as np
from functools import partial
from transformers import (
MODEL_MAPPING,
AutoConfig,
AutoTokenizer,
AutoModel,
)
from densephrases import Encoder
logger = logging.getLogger(__name__)
def set_seed(args):
random.seed... | {
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import os, sys
BASE_DIR = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__))))
import argparse
import numpy as np
import json
import datetime
from collections import defaultdict
from data_utils import *
import torch
import torch.nn as nn
import torch.nn.functional as F
def compu... | {
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struct AdamIterable{T, F, S, R}
w0::T
f::F
stepsize::S
beta1::R
beta2::R
epsilon::R
end
AdamIterable(w0, f; stepsize=1e-3, beta1=0.9, beta2=0.999, epsilon=1e-8) = AdamIterable(w0, f, to_iterator(stepsize), beta1, beta2, epsilon)
Base.IteratorSize(::Type{<:AdamIterable}) = Base.IsInfinite()
mu... | {
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/*********************************************************************
* Software License Agreement (BSD License)
*
* Copyright (c) 2018, PickNik LLC
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditi... | {
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import itertools
import numpy
import pandas
import sklearn.naive_bayes as naive_bayes
# Set some options for printing all the columns
pandas.set_option('precision', 13)
# Define a function to visualize the percent of a particular target category by a nominal predictor
def RowWithColumn (
rowVar, ... | {
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%!TEX root = forallx-ubc.tex
\chapter{SL Trees}
\label{ch.sl.trees}
So far we have learned one way to evaluate SL argument forms for validity: an argument is valid just in case no interpretation satisfies the premises but falsifies the conclusion. We can check to see whether an argument is valid by constructing truth ... | {
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# 1. design model - we need input size and output size, forward pass with
# all the different layers
# 2. construct the loss and optimizer
# 3. training loop
# a. compute prediction
# b. do backward pass to get gradients
# c. update our weights
# slight adjustments to model and cost function.
import torch... | {
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// ------------------------------------ //
#include "GlobalCEFHandler.h"
#include "GUI/GuiCEFApplication.h"
#include "include/cef_app.h"
#include <boost/filesystem.hpp>
#include <iostream>
using namespace Leviathan;
// ------------------------------------ //
DLLEXPORT bool Leviathan::GlobalCEFHandler::CEFFirstChe... | {
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/*
learn.c
*/
#include <stdio.h>
#include <stdlib.h>
#include <stdbool.h>
#include <math.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_multifit.h>
#include "learn.h"
#include "feature.h"
#include "imatrix.h"
#include "dmatrix.h"
#include "util.h"
#include "likelihood.h"
#include "hyper.h"
void mvslda_learn(documen... | {
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import numpy as np
from flexx import flx
from bokeh.plotting import figure
# import trainer.lib as lib
x = np.linspace(0, 6, 50)
p1 = figure()
p1.line(x, np.sin(x))
p2 = figure()
p2.line(x, np.cos(x))
class DebuggerGui(flx.PyWidget):
# def __init__(self):
# # self.bs: List[Optional[flx.Button]] = [Non... | {
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# Morphological Transforamtions
# using HSV (hue satutaiton value )
import cv2
import numpy as np
cap = cv2.VideoCapture(0) # select the first camera in the system
while True:
_ , frame = cap.read()
hsv = cv2.cvtColor(frame , cv2.COLOR_BGR2HSV)
# Now filter the video ( remove Noise , ... | {
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# Copyright 2020 Amazon.com, Inc. or its affiliates. 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. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file acc... | {
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"include": true,
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from collections import OrderedDict
import numpy as np
from gym.spaces import Box, Dict
from multiworld.envs.env_util import get_stat_in_paths, \
create_stats_ordered_dict, get_asset_full_path
from multiworld.core.multitask_env import MultitaskEnv
from multiworld.envs.mujoco.sawyer_xyz.base import SawyerXYZEnv
fro... | {
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# -*- coding: utf-8 -*-
"""
Creates and saves CNN model for keyword detection.
"""
import json
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
DATA_PATH = "data.json"
SAVED_MODEL_PATH = "model.h5"
LEARNING_RATE = 0.001
EPOCHS = 40
BATCH_SIZE = 32
NUMBER_OF_KE... | {
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import os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)),"../"))
from skdecide.builders.discrete_optimization.generic_tools.do_problem import Solution, Problem, EncodingRegister, TypeAttribute, \
ObjectiveRegister, TypeObjective, ObjectiveHandling, ModeOptim
from typing import List, Un... | {
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# -*- coding：utf-8 -*-
# &Author AnFany
import pandas as pd
import numpy as np
# 训练数据文件路径
train_path = 'C:/Users/GWT9\Desktop/Adult_Train.csv'
# 测试数据文件路径
test_path = 'C:/Users/GWT9\Desktop/Adult_Test.csv'
# 因为测试数据native-country中不存在Holand-Netherlands，不便于独热编码。
# 因此在测试文件中添加一个native-country为Holand-N... | {
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[STATEMENT]
lemma map_color_of: "color_of (map f t) = color_of t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. color_of (RBT_Impl.map f t) = color_of t
[PROOF STEP]
by (induct t) simp+ | {
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from __future__ import (absolute_import, division, print_function)
"""
This example shows how to plot data on rectangular 2D grids
(grids that are not rectlinear in geographic or native map projection
coordinates).
An example of such a grid is the 'POP' grid which is used in
the ocean component NCAR Community Climate... | {
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import random
import numpy as np
import matplotlib
from matplotlib import pyplot
from matplotlib.animation import FuncAnimation
def draw_image(idx, centroids, width, height):
"""
Draw image from
:param idx:
:param centroids:
:param width:
:param height:
:return:
"""
data = np.zeros... | {
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import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import chi2_contingency, chi2
#%%
df = pd.read_csv('data/advertisement_clicks.csv')
df.head()
#%% Create the contingency table
df_crosstab = pd.crosstab(df['advertisement_id'], df['action'], margins = False)
... | {
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import cv2
import numpy as np
import os
# Capturing the user's web cam
camera = cv2.VideoCapture(0)
# Creating a classifier object
classifier = cv2.CascadeClassifier("..\Datasets\haarcascade_frontalface_default.xml")
# The name of the file where data is stored
file_name = "saved_data.npy"
# Attributes boxes around ... | {
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import keras
import pandas as pd
import numpy as np
from numpy.random import randint
from keras.models import Sequential
from keras.layers import *
import pdb
def evaluate_metrics(Yt, Yp):
tp = Yt.sum()
tn = Yt.size - tp
fp = Yp[Yt == 0].sum()
fn = (1 - Yp[Yt == 1]).sum()
prec = tp / (tp + fp)
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using Statistics
using StatsBase
using SpecialFunctions
using Roots
include("load_data.jl")
###############################################################################
###############################################################################
### functions that work on interaction activity
"""
coefficie... | {
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