text string |
|---|
<reponame>RizaXudayi/InterpScipy
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 12 12:46:21 2019
@author: Riza
Description: this code investigates the properties of different Scipy interpolation
functions for a 2D unstructured field which is the solution of the Advection-Diffusion PDE.
Different methods are c... |
"""
This is the script that is used for evaluating phases for physical or simulation forward model
This code and data is released under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC.) In a nutshell:
# The license is only for non-commercial use (commercial licenses can be obtain... |
<gh_stars>1-10
"""
ANN for evaluating model biases, differences, and other thresholds using
explainable AI for historical data for regional data
Author : <NAME>
Date : 2 December 2021
Version : 6 - standardizes observations by training data (only 7 models)
"""
### Import packages
import matplotlib.pyplo... |
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module implements plotter for DOS and band structure.
"""
import logging
from collections import OrderedDict, namedtuple
import numpy as np
import scipy.constants as const
from monty.json import jsanitize
from pyma... |
<filename>legacy/node.py
import re
import sympy as sym
import dataset
import wordsim
import utils
random_var_name = 0
def get_random_var_name(init=False):
global random_var_name
if init:
random_var_name = 0
random_var_name = random_var_name + 1
return f'var{random_var_name:03d}'
# mapping = {... |
# -*- coding: utf-8 -*-
# """
# Created on Thu Oct 25 17:19:30 2019
# @author: <NAME> and <NAME>
# Script to compute unstable periodic orbits at specified energies for the
# coupled quartic Hamiltonian using differential correction
# """
import numpy as np
from scipy.integrate import solve_ivp
import time
import m... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 31 18:50:35 2021
@author: cghiaus
Created on Thu Mar 25 07:14:57 2021
mo outdoor mass flow rate as an input.
Cooling as a control & parameter optimizaton problem
OOP implementation
Recycling, Cooling & desumidification (with by-pass), reheating
... |
import numpy as np
from scipy.io import loadmat, savemat, whosmat
vect = np.arange(10)
print(vect.shape)
savemat('np_vector.mat', {'vect': vect})
mat = loadmat('np_vector.mat')
print(whosmat('np_vector.mat'))
print(mat['vect'].shape)
|
<filename>pandas/util/print_versions.py
import os
import sys
def show_versions():
print("\nINSTALLED VERSIONS")
print("------------------")
print("Python: %d.%d.%d.%s.%s" % sys.version_info[:])
try:
sysname, nodename, release, version, machine = os.uname()
print("OS: %s %s %s %s" % (s... |
<gh_stars>0
"""fish_data module contains the helper functions for the model build of the
Nature Conservancy Fisheries Kaggle Competition.
Dependencies:
* numpy as np
* os
* scipy.ndimage as ndimage
* scipy.misc as misc
* scipy.special as special
* matplotlib.pyplot as plt
* tensorflow as tf... |
<gh_stars>0
#!/usr/bin/env python
# coding: utf-8
# In[11]:
#!/usr/bin/env python
# coding: utf-8
# In[7]:
import os
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import linear_model
from scipy.optimize import fmin_l_bfgs_b
#----------------------------... |
#===============================================================================
# BVMM
# Probability and Likelihood Functions
#===============================================================================
import math
import numpy as np
from . import tree
from scipy.special import gammaln
def _verify_alpha(alpha, a... |
<gh_stars>10-100
from invoke import task
import numpy
import random
import torch
import time
import json
import glob
from os.path import basename
import statistics
from pytorch_stateful_lstm import (
UnidirectionalSingleLayerLstm,
UnidirectionalLstm,
StatefulUnidirectionalLstm,
)
from allennlp.modules.lstm... |
#
# ppm.py - Tools for accessing and visualising PPMstar data.
# Depends on the nugridpy package developed by the
# NuGrid collaboration
# (c) 2010 - 2013 <NAME>
# (c) 2011 - 2013 <NAME>
# (c) 2014 - 2015 <NAME>, <NAME>, <NAME>
# (c) 2016 - 2018 <NAME>, <NAME>, <NAME>
# (c) 2018 - 2019 <NAME>, <NAME>
... |
import numpy as np
import scipy.ndimage as ndimage
import sys,os
sys.path.append('/home/zongdaoming/cv/multi-organ/multi-organ-ijcai')
def random_zoom(img_numpy, min_percentage=0.8, max_percentage=1.1):
"""
:param img_numpy:
:param min_percentage:
:param max_percentage:
:return: zoom in/out aig... |
"""
예측분석과 머신러닝 - (1) PreProcessing(전처리)
"""
import numpy as np
from sklearn import preprocessing
from scipy.stats import anderson
# 네덜란드 기상청의 일별 강수량 - 평균, 분산, 앤더슨 달링 검정
rain = np.load('ch10.rain.npy')
rain = .1 * rain
rain[rain < 0] = .05/2
print("Rain mean", rain.mean())
print("Rain variance", rain.var())
print("A... |
<gh_stars>1-10
"""
Script looks at NCEP/NCAR reanalysis trends
Notes
-----
Source : http://www.esrl.noaa.gov/psd/data/gridded/data.
ncep.reanalysis.derived.html
Author : <NAME>
Date : 2 November 2016
"""
### Import modules
import numpy as np
import matplotlib.pyplot as plt
import datetime
... |
<filename>default_profile/extensions/__init__.py<gh_stars>1-10
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
===========
Extensions
===========
This houses a collection of IPython extensions I've written.
"""
import logging
extensions_logger = logging.getLogger(name="default_profile").getChild("extensions")
exte... |
"""Defines Corrector classes that utilize Kepler/K2/TESS Cotrending Basis Vectors.
"""
import logging
import copy
import requests
import urllib.request
from astropy.io import fits as pyfits
from astropy.table import Table
from astropy.time import Time
from astropy.timeseries import TimeSeries
from astropy.units import... |
<reponame>s-miramontes/project5
import numpy as np
from scipy.spatial.distance import cdist
class Silhouette:
def __init__(self, metric: str = "euclidean"):
"""
inputs:
metric: str
the name of the distance metric to use
"""
self.metric = metric
def ... |
import os
import cv2
import numpy as np
from collections import namedtuple
import scipy.io
import imageio
import random
from PIL import Image, ImageFont, ImageDraw
import ade20k_labels
# DATAROOT = '/home/xjw/Downloads/code/mmsegmentation-0.21.0/data/potsdam'
DATAROOT = '/home/xjw/Downloads/code/mmsegmentation-0.21.0... |
<reponame>XSilverBullet/scikit-multilearn
from builtins import object
from builtins import range
import numpy
import numpy.core.umath as umath
import scipy.sparse
from scipy.sparse import issparse
from ..base import MLClassifierBase
# copyright <NAME>
class Neuron(object):
def __init__(self, startpoint, labe... |
import numpy as np
from sklearn.decomposition import NMF as NMFSklearn
from sklearn.preprocessing import normalize
from scipy.sparse.linalg import eigs, LinearOperator, ArpackNoConvergence
import config
import data
from base import nbprint
from util import ProgressIterator
from embedding.main import get_model
from m... |
base = "/l/gaddc1/Dropbox/"
root = "MixtureOfExperts/demos/santner/santner_ARD/ARD/Apr15_fromTriton/"
CI = ['hpd', 'hpd_union', 'quant']
interval = 0.95
import sys, os
sys.path.insert(0, base)
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
import MixtureOfExperts
from Mixture... |
import dominate
from dominate.tags import *
# Note here it is important to use the scipy imsave as it allows us to save
# the image into a file object
from scipy.misc import imsave as sp_imsave
from skimage import img_as_int
import os
from io import BytesIO
from base64 import b64encode
import datetime
def format_di... |
<filename>olsapy/olsa.py
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 25 14:35:23 2018
a module for orthogonal linear separation analysis (OLSA)
@author: setsuo,shotaro, and tadahaya
"""
import sys
import csv
import math
import os
import numpy as np
import pandas as pd
np.seterr(divide='ignore', inval... |
# -*- coding: utf-8 -*-
"""
@Time:2020/8/14 21:26
@Auth"JunLin615
@File:peak_processing.py
@IDE:PyCharm
@Motto:With the wind light cloud light mentality, do insatiable things
@email:<EMAIL>
"""
from scipy.signal import find_peaks
import numpy as np
import math
def search_peaks(x_data, y_data, height=0.1, distance=10... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import networkx as networkx
import numpy as numpy
import scipy as scipy
import scipy.integrate
class SEIRSModel():
"""
A class to simulate the Deterministic SEIRS Model
===========================... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from scipy.stats.distributions import f
def gelman_rubin(chains, return_cdf=False):
"""
Compute the Gelman-Rubin R-statistic from an ensemble of chains. `chains`
is expected to have shape `(nsteps, nchains)` if samples are one
dimensio... |
# !/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Python code to create, manipulate, and analyze an array of crops from TIF images or videos.
Created: Summer of 2020
Authors: <NAME> & <NAME>.
'''
# Conventions.
# module_name, package_name, ClassName, method_name,
# ExceptionName, function_name, GLOBAL_CONSTANT_NAME,... |
# <NAME> (<EMAIL>)
# April 2018
import os, sys
BASE_DIR = os.path.normpath(
os.path.join(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(os.path.join(BASE_DIR, '..'))
from datasets import *
from generate_outputs import *
from scipy.optimize import linear_sum_assignment
import json
import numpy as... |
<reponame>dongzizhu/GraphGallery
import numpy as np
import scipy.sparse as sp
from utils import normalized_laplacian_matrix
from sklearn import preprocessing
class GLEE:
r"""An implementation of `"Geometric Laplacian Eigenmaps" <https://arxiv.org/abs/1905.09763>`_
from the Journal of Complex Networks ... |
"""
Code about best uniform approximation (based on distance between CDFs) and its
comparison with spline approaches.
"""
import numpy as np
import scipy.stats as ss
from scipy.integrate import quad
from scipy.interpolate import UnivariateSpline
from scipy.optimize import fsolve, least_squares, minimize
import matplotl... |
# Author - <NAME>
# Div-A Batch - B1
# Roll No.-09 Gr. No.- 11810636
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from scipy.stats import multivariate_normal
x, y = np.mgrid[-3.0:3.0:30j, -3.0:3.0:30j]
# Need an (N, 2) array of (x, y) pairs.
xy =... |
<reponame>mhsnjvd/numfun
import matplotlib.pylab as plt
import numpy as np
from barycentric import barycentric_interpolation
from scipy.interpolate import CubicSpline
from numfun.function import Function
x = np.linspace(0, 5, 21)
a = 1.0
b = 4
c = 1
w = np.random.randn(len(x))
def y_true(xx):
return a + b * xx ... |
#!/usr/bin/env python3
import netCDF4 as nc
import sys
import numpy as np
import matplotlib.pyplot as plt
import argparse
import scipy.ndimage as sn # contains the filters
from plotTools import addImagePlot
from netcdfTools import read3dDataFromNetCDF
from utilities import selectFromList
#============================... |
import numpy as np
from scipy.io import loadmat
import os
from pathlib import Path
# from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
# plotting parameters
sns.set(font_scale=1.1)
sns.set_context("talk")
sns.set_palette(['#701f57', '#ad1759', '#e1... |
<filename>mathics/core/expression.py
# cython: language_level=3
# -*- coding: utf-8 -*-
import sympy
import math
import time
import typing
from typing import Any, Optional
from itertools import chain
from bisect import bisect_left
from mathics.core.atoms import from_python, Number, Integer
from mathics.core.convert ... |
<reponame>bgraille/pylbm<filename>demo/examples/stability/advection_d1q3.py
"""
Stability analysis of the
D1Q3 solver for the advection equation
d_t(u) + c d_x(u) = 0
"""
import sympy as sp
import pylbm
# pylint: disable=invalid-name
# symbolic variables
U, X = sp.symbols('U, X')
# symbolic parameters
LA, C... |
<reponame>JordanRex/YAAML
# misc functions
###############################################################################################################################
## EDA
###############################################################################################################################
import math
... |
<gh_stars>0
import sys
import time
import os
import gc
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from scipy.signal import argrelextrema
import scipy.linalg as LA
import scipy.sparse.linalg as spLA
import majoranaJJ.operators.sparse_operators as spop #sparse operators
from majoranaJ... |
"""Unit tests for the functions file."""
from __future__ import division, print_function, absolute_import
from numpy.testing import assert_equal, assert_allclose
import pytest
import numpy as np
from scipy.optimize import check_grad
import tensorflow as tf
from safe_learning.functions import (_Triangulation, Triangu... |
<reponame>FSLobao/Spectrum-Cortex<filename>compute_channel_distance.py
#!/usr/bin/python3
# # Compute the distance between traces for clustering
# Import standard libraries
import pandas as pd
import numpy as np
from scipy import signal
from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot... |
<reponame>covid-models/ventilator-supply-demand<filename>model.py<gh_stars>0
import math
import pandas as pd
import numpy as np
import scipy.integrate
from datetime import timedelta
import shared
# import world_data
# import population
def model(Y, x, N, beta0, days0, beta1, gamma, sigma):
# :param array... |
<gh_stars>0
"""
This script analyzes the boundary of regions
"""
import argparse
import os
import numpy as np
from config import MEAN_MARKERS, MEAN_DNASE_DIR
from data_provider import SeqLoader
from models import chromatin_classifier
from models import ChromosomeSegmentation
from models.ChromosomeSegmentation import Se... |
<gh_stars>10-100
"""Single phase constant Vdc PV-DER code."""
from __future__ import division
import numpy as np
import math
import cmath
import scipy
import six
import pdb
import warnings
from pvder.DER_components import SolarPVDER,PVModule
from pvder.grid_components import BaseValues
from pvder import utility_func... |
import numpy as np
import scipy.stats
import os
import logging
from astropy.tests.helper import pytest, catch_warnings
from astropy.modeling import models
from astropy.modeling.fitting import _fitter_to_model_params
from stingray import Powerspectrum
from stingray.modeling import ParameterEstimation, PSDParEst, \
... |
import numpy as np
from scipy.linalg import solveh_banded
from six.moves import xrange
class Baseline(object):
def _fit_one(self, bands, intensities):
'''bands: array of length n
intensities: array of length n
Returns baseline array of length n
'''
raise NotImplementedError()
def _fit_m... |
import numpy as np
import tensorflow as tf
import time
import random
import scipy.io as sio
from video_dataset import Dataset
import utils
import options
from test import test
import os
args = options.parser.parse_args()
if not os.path.exists('./ckpt/' + args.model_name):
os.makedirs('./ckpt/' + args.model_name)... |
<gh_stars>0
import os
import numpy as np
from matplotlib import pyplot as plt
from scipy import interpolate
import figlatex
import toy
import textbox
import template as _template
prefix = 'nuvhd_lf_3x_tile57_77K_64V_6VoV_1'
noisefile = 'noises/merged_000886-adc_W201_Ch00.npz'
tau = [8, 12, 16, 64, 384]
snr = np.lins... |
# -*- coding: utf-8 -*-
"""
Basic Arithmetic
The functions here are the basic arithmetic operations that you might find on a calculator.
"""
from mathics.version import __version__ # noqa used in loading to check consistency.
import sympy
import mpmath
from mathics.builtin.arithmetic import _MPMathFunction, creat... |
import logging
from functools import partial
from typing import Dict, Iterable, List, Optional, Sequence, Union
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from scipy.sparse import csr_matrix, vstack
from torch.distributions import Normal
from scvi import REGISTRY_KEYS
from scvi._c... |
<filename>examples/testsCommon.py
def readOutputFile():
import os
head, dirname = os.path.split(os.getcwd())
outputFilename = "regcoil_out."+dirname+".nc"
if not os.path.isfile(outputFilename):
print "Error! The output file "+outputFilename+" has not been created."
exit(1)
... |
<filename>Definitions/definitions_for_speed_data_processing.py
#!/usr/bin/env python
# coding: utf-8
# In[23]:
import pandas as pd
import numpy as np
import scipy as sp
import scipy.fftpack
import geopy.distance as geo_di
from scipy.signal import savgol_filter
# In[3]:
# add distance column
# In[11]:
def b... |
<filename>statsmodels/genmod/_prediction.py
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 19 11:29:18 2014
Author: <NAME>
License: BSD-3
"""
import numpy as np
from scipy import stats
# this is similar to ContrastResults after t_test, partially copied and adjusted
class PredictionResults(object):
def __init__... |
# Copyright 2021 NREL
# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
# the License at http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distri... |
"""
Miscellaneous utilities for wallscraper
"""
from fractions import Fraction
import sys
import pathlib
WALLPAPER_FOLDER = pathlib.Path(__file__).parent / 'wallpapers'
def get_aspect_ratio(width, height):
# Credit to https://en.wikipedia.org/wiki/Aspect_ratio_(image)
common = [
(4, 3), # (COMMON) ... |
"""
creating a flappy Bird playing
"""
from PIL import Image
import numpy as np
import pyscreenshot as ImageGrab
import matplotlib.pyplot as plt
import cv2
import scipy.misc
import time
import pyautogui
from skimage import measure
try:
from skimage import filters
except ImportError:
from skimage import filt... |
""" calculates the nearest weather station to a requested location"""
from _datetime import datetime
from typing import Union, Tuple, Optional
import numpy as np
import pandas as pd
import logging
from scipy.spatial import cKDTree
from wetterdienst.additionals.functions import (
check_parameters,
parse_enumer... |
"""Surrogate Model based on a logistic regression model, with regularization to adjust for
overfitting. Based on work from `Python logistic regression
<http://blog.smellthedata.com/2009/06/python-logistic-regression-with-l2.html>`_.
"""
import logging
from random import seed
try:
import numpy as np
from nump... |
<filename>bayes_optim/surrogate/gaussian_process/__init__.py
from __future__ import annotations
import warnings
from functools import partial
from typing import List, Tuple, Union
import numpy as np
from scipy.linalg import solve_triangular
from scipy.spatial.distance import cdist
from sklearn.gaussian_process import... |
import random
import os.path as osp
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, SAGEConv, GATConv, global_add_pool
from torch_geometric.utils import k_hop_subgraph, to_scipy_sparse_matrix
from torch_geometric.data import Data, ... |
from collections import namedtuple
import numpy as np
import pytest
from sklearn_vw import VW, VWClassifier, VWRegressor, tovw
from sklearn import datasets
from sklearn.utils.validation import NotFittedError
from scipy.sparse import csr_matrix
"""
Test utilities to support integration of Vowpal Wabbit and scikit-lear... |
<filename>code/walker_with_generalisation.py<gh_stars>0
#!/usr/bin/python
import os
import getopt
import sys
import bz2
import subprocess
import numpy as np
import coding_theory as code
import develop as d
import scipy.io as sio
# set defaults
noName = 1
includeTop = 0 # don;t include the directiory you call walker ... |
import numpy as np
import matplotlib.pyplot as plt
import pygad as pg
from scipy import stats
import glob
from multiprocessing import Pool
import utils
s, h, g = pg.prepare_zoom('/ptmp/mpa/naab/REFINED/M1196/SF_X/4x-2phase/out/snap_M1196_4x_470', gas_trace='/u/mihac/data/M1196/4x-2phase/gastrace_disc', star_form=None)... |
"""Extension of model originally published by <NAME> : https://github.com/tdeetjen/simple_dispatch
The first class, StorageModel, was written by <NAME>.
The following classes were written by <NAME> and adjusted or added to by <NAME>.
"""
class StorageModel(object):
"""Added by <NAME>. Storage can be used in two w... |
<gh_stars>0
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from argparse import ArgumentParser
import keras
from keras import backend as K
import sys
import tensorflow as tf
import os
from pdb import set_trace
from sklearn.model_selection import train_tes... |
<filename>scripts/plotByvsEfit.py
"""
Usage: fitByvsEnergy.py [-o OUTPUT_FILE] -i INPUT_FILE -p PAR_FILE -f FLAVOR -c CHANNEL -e E_MIN -E E_MAX
Options:
-h --help Help.
-o --output_file OUTPUT_FILE Output file.
-i --input_file INPUT_FILE Input file.
-p --parameter_file PAR_FILE P... |
<reponame>iwa-shi/fidelity_controllable_compression
import argparse
import cv2
from glob import glob
from itertools import product
import numpy as np
import os
from tqdm import tqdm
from scipy.special import erf
import torch
import torch.nn as nn
from model.model import CompModel
import arithmetic_coding as ac
MAX_N... |
<filename>pyplots/fftpack_frequency.py
import numpy as np
from scipy import fftpack
import pylab as pl
np.random.seed(1234)
time_step = 0.02
period = 5.
time_vec = np.arange(0, 20, time_step)
sig = np.sin(2 * np.pi / period * time_vec) + \
0.5 * np.random.randn(time_vec.size)
sample_freq = fftpack.fftfreq(si... |
# Author: <NAME>
from pylab import *
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import datetime as dt
from scipy.signal import savgol_filter
from pathlib import Path
############### Basic use #############################
# example: plot_country("France",dataParam,fitParam,... |
<filename>utils/fileiter.py
import mxnet as mx
import numpy as np
from mxnet.io import DataIter
from mxnet.io import DataBatch
import random
import cv2
import sys
import settings as settings
import os
import logging
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filte... |
<gh_stars>0
from __future__ import print_function
from itertools import product
import os
import pickle
import bdpy
from bdpy.dataform import Features
from bdpy.util import dump_info, makedir_ifnot
import numpy as np
from stability_selection import stability_selection
from sklearn.decomposition import PCA
from scipy im... |
<filename>utility/Archive/PSD_fileplot.py<gh_stars>1-10
import scipy.signal as sig
import librosa
import matplotlib.pyplot as plt
def PSD_fileplot(file_path,
PSD_window='hamming',
PSD_nperseg = 128,
PSD_nfft=512,
PSD_scaling='spectrum',
... |
<reponame>isingmodel/voiceproject
import os
import wave
import glob
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
from python_speech_features import mfcc
import pprint
from scipy import spatial, linalg, mat, dot
def main():
domain = 1000
test = []
folderlist = glob.glob(... |
"""chain module.
This module contains information about a depletion chain. A depletion chain is
loaded from an .xml file and all the nuclides are linked together.
"""
from collections import OrderedDict, defaultdict
from io import StringIO
from itertools import chain
import math
import re
import os
# Try to use lxm... |
import numpy as np
from astropy.io import fits
import os
from scipy import optimize, stats
import argparse
import time
import emcee
# from corner import corner
from logllh_ebins_funcs import get_cnt_ebins_normed, log_pois_prob
from ray_trace_funcs import ray_trace_square
from drm_funcs import get_ebin_ind_edges, DRMs,... |
<gh_stars>10-100
import os
import warnings
from typing import Dict, Optional, Union
import h2o
import numpy as np
import pandas as pd
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from scipy import sparse as sps
from .util import benchmark_convergence_tolerance, runtime
def _build_and_fit(model_args,... |
<gh_stars>0
from __future__ import division, absolute_import, print_function
import sys
import numpy.linalg as nl
import scipy.linalg as sl
from numpy.testing import assert_
from numpy.random import rand
def random(size):
return rand(*size)
class Bench(object):
params = [
[20, 100, 500, 1000],
... |
<gh_stars>0
import os
import json
import math
import pickle
from random import randint, uniform
import numpy as np
import argparse
from pyquaternion import Quaternion
from scipy.spatial import distance
from tqdm import tqdm
import torch
from src.datasets.utils import angle_between
from src.poseVisualizer import visu... |
<reponame>paulaWesselmann/testing_pydial
###############################################################################
# PyDial: Multi-domain Statistical Spoken Dialogue System Software
###############################################################################
#
# Copyright 2015-16 Cambridge University Engineer... |
<reponame>icdm-extract/extract
#!/usr/env/python
import numpy as np
from scipy.ndimage import filters
# from sklearn.decomposition import TruncatedSVD
from joblib import Memory
memory = Memory('./output', verbose=0)
from ..datasets import synthetic as synth
from ..datasets import read_msrc as msrc
from ..utils impo... |
<gh_stars>0
from numpy import ndarray
import scipy.signal as sg
import numpy as np
def validate(coeffs, scores, data):
return ((coeffs.dot(scores.T).ravel() - data.T.ravel()) ** 2).mean()
def factoran(data: ndarray, n_factors: int = 2, demean=True, scale=True):
from sklearn.decomposition import FactorAnalys... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 13 16:00:10 2019
@author: nico
"""
import os
import numpy as np
from scipy import signal as sig
import matplotlib.pyplot as plt
import control
os.system ("clear") # limpia la terminal de python
plt.close("all") #cierra todos los graficos
num =... |
<gh_stars>1-10
# coding: utf-8
# Importations
import numpy as np
import scipy
from scipy import signal
from sklearn.decomposition import PCA
import pandas as pd
import tables
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.animation as animation
from ... |
from ample import gs, uniform, srs, kmeanspp
import numpy as np
from scanorama import transform
from scipy.sparse import csr_matrix, find
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import normalize
from subprocess import Popen
from time impo... |
###TAKEN and modified FROM https://github.com/danielvarga/string-art
import sys
import numpy as np
import scipy
import scipy.sparse
import scipy.sparse.linalg
from scipy.misc import imread, imresize, imsave
import math
from collections import defaultdict
from bresenham import *
def image(filename, size):
img =... |
import numpy as np
import scipy
import os
import time
import multiprocessing
from scipy.sparse import csgraph,csr_matrix
import dijkstra_mp64
def dijkstra_MP_test():
scriptdir = os.path.dirname(os.path.abspath(__file__))
os.chdir( scriptdir )
#load data file
#move relative to github directory
os.c... |
<reponame>yalov4uk/ML-labs
from __future__ import division
import scipy.io as sio
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import optimize as opt
def sigmoid(z):
return 1 / (1 + np.exp(-z))
def h0x(X, theta):
m = len(X)
ones = np.ones((m, 1))
a1 = np.hstack((... |
<filename>PearsonCC.py
import pandas as pd
from tqdm import tqdm
from scipy import stats
###################################
# Pearson correlation coefficient and p-value Of Features
###################################
df = pd.read_csv('Training Dataset.csv')
#data = arff.loadarff('Training Dataset original.arff')
#df... |
# -*- coding: utf-8 -*-
import os
import sys
import numpy as np
import logging
import csv
from sklearn import preprocessing
from sklearn.model_selection import LeaveOneGroupOut
from sklearn.externals import joblib
from lib.data import Data
from sklearn import svm, datasets, ensemble
from scipy import interp
... |
<reponame>cosunae/gridtools
# -*- coding: utf-8 -*-
import itertools
import math
import os
import re
import statistics
from perftest import result, time
from pyutils import log
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt # noqa: E402
plt.style.use('ggplot')
prop_cycle = matplotli... |
import numpy as np
from scipy.special import logsumexp
from scipy.special import digamma, gamma
from prml.rv.rv import RandomVariable
class VariationalGaussianMixture(RandomVariable):
def __init__(self, n_components=1, alpha0=None, m0=None, W0=1., dof0=None, beta0=1.):
"""
construct variational g... |
#!/usr/bin/python
#
# Test the helper functions
#
# Copyright 2017 University of Washington
# Developed by <NAME> and <NAME>
# BioRobotics Lab, University of Washington
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:... |
#!/usr/bin/env python
# -*- coding:UTF-8 -*-
# File Name :
# Purpose :
# Creation Date : 05-01-2019
# Author : <NAME> [sli[at]informatik[dot]uni-hamburg[dot]de]
# Author : <NAME> [liang[at]informatik[dot]uni-hamburg[dot]de]
from __future__ import print_function, division
import numpy as np
impo... |
# coding=utf-8
"""
This file contains the classes used to implement the ToPs predictive algorithm.
The main class to use for the users is ToPs
"""
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array
from sklearn.utils.multiclass import ... |
<reponame>radoslavvlaskovski/performance_anomaly_detection_system
import enum
import numpy as np
from scipy.stats import norm
class Detector:
def __init__(self, buffer_size=100):
self.buffer_real_values = []
self.buffer_prediction_values = []
self.buffer_size = buffer_size
def add_v... |
import os
import dash
import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
import geopandas as gpd
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from scipy import stats
from peaky_finders.predictor import (
create_load_d... |
<filename>run_offline_acc.py
#!/usr/bin/env python3
import torch
import numpy as np
from smart_home_dataset import SmartHomeDataset
from classifier import Classifier
from torch import optim
import utils
import callbacks as cb
import time
from generative_replay_learner import GenerativeReplayLearner;
import arg_params
... |
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import os
from PIL import Image
import numpy as np
# import torch
import json
import sys
from tqdm import tqdm, trange
from pycocotools.coco import COCO
import skimage.io as io
import pylab
from convert_fat_coco import *
from mpl_toolkits.axes_grid1 impo... |
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