text string |
|---|
<filename>statapy/regression/tests.py
import scipy.stats as stats
def mannwhitneyu(sample_0, sample_1, one_sided=False):
"""
Performs the Mann-Whitney U test
:param sample_0: array of values
:param sample_1: array of values
:param one_sided: True iff you want to use less than alternative hypothesi... |
"""Variational auto-encoder for MNIST data.
References
----------
http://edwardlib.org/tutorials/decoder
http://edwardlib.org/tutorials/inference-networks
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import numpy as np
import os
i... |
# # Customer cliff dive data challenge
# 2020-02-17
# <NAME>
# ## Summary
# ### The problem
# The head of the Yammer product team has noticed a precipitous drop in weekly active users, which is one of the main KPIs for customer engagement. What has caused this drop?
# ### My approach and results
# I began by comi... |
from collections import defaultdict
import heapq
from itertools import chain, repeat
from feature_dict import FeatureDictionary
import json
import numpy as np
import scipy.sparse as sp
class TokenCodeNamingData:
SUBTOKEN_START = "%START%"
SUBTOKEN_END = "%END%"
NONE = "%NONE%"
@staticmethod
def _... |
# coding=UTF-8
# ex:ts=4:sw=4:et=on
# Copyright (c) 2013, <NAME>
# All rights reserved.
# Complete license can be found in the LICENSE file.
from io import StringIO
from scipy.optimize import fmin_l_bfgs_b
from .exceptions import wrap_exceptions
def setup_project(projectf):
from pyxrd.file_parsers.json_parser ... |
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 20 14:03:18 2020
@author: Nicolai
"""
import sys
sys.path.append("../differential_evolution")
from JADE import JADE
import numpy as np
import scipy as sc
import testFunctions as tf
def downhillsimplex(population, function, minError, maxFeval):
'''
implementatio... |
<reponame>biagiom/models
import numpy as np
import scipy.linalg as la
from statsmodels.tsa.api import SimpleExpSmoothing, Holt
"""
@desc: From activity probe, calculate spike patterns
"""
def getSpikesFromActivity(self, activityProbes):
# Get number of probes (equals number of used cores)
numProbes = np.shape(... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Sentence level and Corpus level BLEU score calculation tool
"""
from __future__ import division, print_function
import io
import os
import math
import sys
import argparse
from fractions import Fraction
from collections import Counter
from functools import reduce
from... |
import time
import numpy as np
import scipy.sparse as sps
from gensim.models import Word2Vec
from tqdm import tqdm
from recommenders.recommender import Recommender
from utils.datareader import Datareader
from utils.evaluator import Evaluator
from utils.post_processing import eurm_to_recommendation_list
from recommender... |
# Copyright 2020 Makani Technologies LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... |
#!/usr/bin/env python3
import logging
import os
import pickle
import time
from os.path import join as pjoin
import matplotlib.pyplot as plt
import numpy as np
import scipy
from matplotlib import rc
from scipy.optimize import least_squares
import asymptotic_formulae
from asymptotic_formulae import GaussZ0
from asympto... |
# set up the environment by reading in libraries:
# os... graphics... data manipulation... time... math... statistics...
import sys
import os
from urllib.request import urlretrieve
import matplotlib as mpl
import matplotlib.pyplot as plt
import PIL as pil
from IPython.display import Image
import pandas as pd
from p... |
<filename>causal_rl/environments/multi_typed.py
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from gym import Env
from scipy.spatial import distance
from typing import Optional, Tuple, Any
from causal_rl.environments import CausalEnv
class MultiTyped(CausalEnv):
"""A simulation of ... |
<filename>tutorials/seq2seq_sated/seq2seq_sated_meminf.py
import os
import sys
from collections import defaultdict
import tensorflow as tf
import tensorflow.keras.backend as K
import numpy as np
import scipy.stats as ss
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve
from sklearn.linear_model imp... |
<reponame>liuyingbin19222/HSI_svm_pca_resNet50<gh_stars>10-100
import keras
from keras.layers import Conv2D, Conv3D, Flatten, Dense, Reshape, BatchNormalization
from keras.layers import Dropout, Input
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from... |
<filename>examples/services/classifier_service.py
'''
python3 classifier_service.py data.csv
This service runs a scikit-learn classifier on data provided by the csv file data.csv.
The idea of this is a simple spam detector. In the file, you will see a number, 1 or
-1, followed by a pipe, followed by a piece of text.... |
from __future__ import print_function
from __future__ import division
from . import _C
import torch
from fuzzytorch.utils import TDictHolder, tensor_to_numpy, minibatch_dict_collate
import numpy as np
from fuzzytools.progress_bars import ProgressBar, ProgressBarMulti
import fuzzytools.files as files
import fuzzytools.... |
<filename>alphad3m/alphad3m/metalearning/grammar_builder.py
import logging
import numpy as np
from scipy import stats
from collections import OrderedDict
from alphad3m.metalearning.resource_builder import load_metalearningdb
from alphad3m.metalearning.dataset_similarity import get_similar_datasets
from alphad3m.primiti... |
import numpy as np
import scipy.io as sio
import os, glob, sys
import h5py_cache as h5c
sys.path.append('/home/yzhang/workspaces/smpl-env-gen-3d-internal')
sys.path.append('/home/yzhang/workspaces/smpl-env-gen-3d-internal/source')
from batch_gen_hdf5 import BatchGeneratorWithSceneMeshMatfile
import torch
'''
In t... |
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
def set_ax_range():
LEFT_AX.set_xlim(X_RANGE)
LEFT_AX.set_ylim(Y_RANGE)
def range_plot(ax, f, x_range, y_range):
bins = 50
xi, yi = np.mgrid[
min(x_range):max(x_range):bins*1j,
min(y_range):max(y_range):bins*1j
... |
<reponame>MartinThoma/cv-datasets<filename>hasy.py
# -*- coding: utf-8 -*-
"""Utility file for the HASYv2 dataset.
See https://arxiv.org/abs/1701.08380 for details.
"""
from __future__ import absolute_import
from keras.utils.data_utils import get_file
from keras import backend as K
import numpy as np
import scipy.nd... |
#!/usr/bin/env python3
import os
import colorsys
import cv2
import numpy as np
from scipy.stats import multivariate_normal
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
class ColorPredicate:
def __init__(self, name, images_path, n_max=10):
self.name = name
self._t... |
<filename>examples/cartpole_example/test/cartpole_PID_MPC_sim.py
import numpy as np
import scipy.sparse as sparse
from scipy.integrate import ode
from scipy.interpolate import interp1d
import time
import control
import control.matlab
import numpy.random
import pandas as pd
from ltisim import LinearStateSpaceSystem
from... |
import numpy as np
from scipy.ndimage import maximum_filter
class AttrDict(dict):
__setattr__ = dict.__setitem__
__getattr__ = dict.__getitem__
def signal2noise(r_map):
""" Compute the signal-to-noise ratio of correlation plane.
w*h*c"""
r = r_map.copy()
max_r = maximum_filter(r_map, (5,5,1)... |
#!/usr/bin/env python3
# -*-coding:utf-8-*-
"""
This module is used to extract features from the data
"""
import numpy as np
from scipy.fftpack import fft
from scipy.fftpack.realtransforms import dct
import python_speech_features
eps = 0.00000001
def file_length(soundParams):
"""Returns the file length, in sec... |
from scipy import io
import numpy as np
import random
import tensorflow as tf
class_num = 10
image_size = 32
img_channels = 3
def OneHot(label,n_classes):
label=np.array(label).reshape(-1)
label=np.eye(n_classes)[label]
return label
def prepare_data():
classes = 10
data1 = io.loadmat('./data/... |
<reponame>yanpei18345156216/COMBO_Python3
import numpy as np
import scipy.stats
def EI(predictor, training, test, fmax=None):
fmean = predictor.get_post_fmean(training, test)
fcov = predictor.get_post_fcov(training, test)
fstd = np.sqrt(fcov)
if fmax is None:
fmax = np.max(predictor.get_post_... |
<filename>sandbox/kl_div/kl.py
import numpy as np
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt
class GaussianMixture1D:
def __init__(self, mixture_probs, means, stds):
self.num_mixtures = len(mixture_probs)
self.mixture_probs = mixture_probs
self.means = means
... |
<filename>modules/niftitools.py
import os
import pydicom
import glob
import numpy as np
import nibabel as nib
from skimage import filters, morphology
from scipy.ndimage.morphology import binary_fill_holes
from scipy.ndimage import label
from dipy.segment.mask import median_otsu
def padvolume(volume):
"Applies a pa... |
#!/usr/bin/python
from __future__ import division
from __future__ import with_statement
import matplotlib
from matplotlib import rcParams
from matplotlib import pyplot
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.mplot3d import Axes3D
from PIL import Image
#import Image
from pylab import *
... |
<reponame>richplane/PyREmatcher
# Renewable generation at Findhorn
from windpowerlib import WindFarm
from windpowerlib import WindTurbine
from windpowerlib import WindTurbineCluster
from windpowerlib.turbine_cluster_modelchain import TurbineClusterModelChain
import pvlib
from pvlib.pvsystem import PVSystem
from pvlib.l... |
import os
import csv
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
def mean_confidence_interval(data, confidence=0.95):
a = 1.0 * np.array(data)
n = len(a)
m, se = np.mean(a), scipy.stats.sem(a)
h = se * scipy.stats.t.ppf((1 + confidence) / 2.... |
<filename>src/server/noize_reduction.py
import scipy as sp
from pyssp.util import (
get_frame, add_signal, compute_avgpowerspectrum
)
def writeWav(param, signal, filename):
import wave
with wave.open(filename, 'wb') as wf:
wf.setparams(param)
s = sp.int16(signal * 32767.0).tostring()
... |
<filename>Code/3_linear_regression_on_pixels.py
# -*- coding: utf-8 -*-
"""3_Linear_regression_on_pixels.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1nhECM9OxwIw8BjEqsQcSUwojX2KYrh1I
"""
from google.colab import drive #to retrieve data from... |
<reponame>pablohawz/tfg-Scan-Paint-clone
import os
import tempfile
from time import time
import numpy as np
import sounddevice as sd
from PySide2.QtWidgets import QApplication, QFileDialog
from scipy.io.wavfile import write
# Config
t = 3 # s
fs = 44100
def save(x, fs):
# You have to create a QApp in order to ... |
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 15 14:03:52 2015
@author: jemanjohnson
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import scipy.io
from sklearn import preprocessing
from time import time
from sklearn.preprocessing import MinMaxScaler
# Image Reshape Function
def img_as_array(img... |
import numpy as np
from scipy.linalg import sqrtm
from sklearn.preprocessing import StandardScaler
def make_linear_regression(n_samples=10000,
n_uncorr_features=10, n_corr_features=10,
n_drop_features=4,
include_intercept=True,
... |
import json
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as signal
import yaml
from mpl_toolkits.mplot3d.axes3d import Axes3D
from scipy.interpolate import interp1d
from tf_pwa.config_loader import ConfigLoader
from tf_pwa.experimental.extra_amp impor... |
from sympy import *
import pandas as pd
def bisection(xl, xu, tolerance, function):
x = Symbol('x')
f = parse_expr(function)
iteration = 0
data = pd.DataFrame(columns=['iteration','xl','xu','xr','f(xl)','f(xu)','f(xr)','f(xl)f(xr)','error'])
while abs(xu-xl)>=tolerance:
xr = (xl + xu)/2
... |
import tensorflow as tf
import tensorflow_probability as tfp
from scipy.stats import expon
from videos.linalg import safe_cholesky
from manim import *
# shortcuts
tfd = tfp.distributions
kernels = tfp.math.psd_kernels
def default_float():
return "float64"
class State:
def __init__(self, kernel, x_grid, x... |
import numpy as np
import time
import matplotlib.pyplot as plt
import imageio
from scipy.optimize import fsolve
from body import Body
def get_position_from_Kepler(semimajor_axis, eccentricity, inclination, ascending_node, argument_of_periapsis, mean_anomaly, mass_orbit, G=6.67430 * 10**(-11)):
"""
Get the pos... |
<reponame>MarcoFerrari128/Portfolio<gh_stars>0
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import ode
import FLC
import pyprind
from numpy.linalg import eig
import pandas as pd
def impulse(lenght):
i = 0
Impulse = []
while i < lenght:
if i == 99:
Impulse.app... |
<reponame>neal-siekierski/kwiver<filename>arrows/pytorch/seg_utils.py
# ckwg +28
# Copyright 2018 by Kitware, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source ... |
import math
import numpy as np
import scipy.constants as sp
import copy
import time
X = 0 # Cartesian indices
Y = 1
L = 0 # Lower
U = 1 # Upper
def gaussian(x, delay, spread):
return np.exp( - ((x-delay)**2 / (2*spread**2)) )
def subsId(id):
if id is None:
return -1
else:
return id-1
cl... |
import os
import sys
sys.path.append(os.path.dirname(__file__) + "/../")
from scipy.misc import imread
from util.config import load_config
from nnet import predict
from util import visualize
from dataset.pose_dataset import data_to_input
cfg = load_config("demo/pose_cfg.yaml")
# Load and setup CNN part detector
s... |
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from copy import deepcopy
from typing import Union, Type, Any, Tuple
import numpy as np
import torch
import torch.nn as nn
from scipy.signal import find_peaks_cwt
from .net import MyNN, MyNNRegressor
from .utils import autoregression_matrix, ... |
import numpy as np
from numpy import random, linspace, cos, pi
import math
import random
import matplotlib.pyplot as plt
from scipy.fft import fft, fftfreq
from scipy.fft import rfft, rfftfreq
import copy
from mpl_toolkits.mplot3d import axes3d
from mpl_toolkits import mplot3d
from plotly import __version__
import pand... |
<gh_stars>0
"""
ebb_fit_prior : fits a Beta prior by estimating the parameters from the data using
method of moments and MLE estimates
augment : given data and prior, computes the shrinked estimate, credible intervals and
augments those in the given dataframe
check_fit : plots the true average and the shrinked averag... |
<filename>word2vec.py
#!/usr/bin/python -W all
"""
word2vec.py: process tweets with word2vec vectors
usage: word2vec.py [-x] [-m model-file [-l word-vector-length]] -w word-vector-file -T train-file -t test-file
notes:
- optional model file is a text file from which the word vector file is built
- ... |
# runs t-tests over the null hypothesis
# avg_gini if (priority == "newer") == avg_gini if (priority == "more active")
import csv
import numpy as np
from scipy.stats import ttest_ind
from scipy.special import stdtr
def readCsvFile(fileName):
'''
(string) => list of dicts
Read the file called fileName a... |
#!/usr/bin/python
# Created by: <NAME>
# Date: 2013 June 28
# Program: This program correct the imagen .fit (Science) by Syntethic Flat
# 1 m Reflector telescope, National Astronomical Observatory of Venezuela
# Mode f/5, 21 arcmin x 21 arcmin
# Project: Omega Centauri, Tidal Tails.
# The program Astrometry_V1.py def... |
# -*- coding: utf-8 -*-
"""
---------------------------------------------
File Name: 粗避障
Desciption:
Author: fanzhiwei
date: 2019/9/5 9:58
---------------------------------------------
Change Activity: 2019/9/5 9:58
-------------------------------------... |
'''
Function and classes representing statistical tools.
'''
__author__ = ['<NAME>']
__email__ = ['<EMAIL>']
from hep_spt.stats.core import chi2_one_dof, one_sigma
from hep_spt.core import decorate, taking_ndarray
from hep_spt import PACKAGE_PATH
import numpy as np
import os
from scipy.stats import poisson
from scipy... |
import os
import xml.etree.ElementTree as ET
import numpy as np
import scipy.sparse
import scipy.io as sio
import cPickle
import subprocess
import uuid
def Get_Class_Ind(Class_INT):
concepts = []
concepts.append(('Animal', [
'n01443537', 'n01503061', 'n01639765', 'n01662784', 'n01674464', 'n01726692'... |
"""
Encodes SPOT MILP as the structure of a CART tree in order to apply CART's pruning method
Also supports traverse() which traverses the tree
"""
import numpy as np
from mtp_SPO2CART import MTP_SPO2CART
from decision_problem_solver import*
from scipy.spatial import distance
def truncate_train_x(train_x, train_x_pre... |
<reponame>ajferraro/fastreg
import numpy as np
from scipy import stats
import utils
def fit(xdata, ydata):
"""Calculate 2D regression.
Args:
xdata (numpy.ndarray): 1D array of independent data [ntim],
where ntim is the number of time points (or other independent
points).
... |
<reponame>uiuc-cse/2014-01-30-cse<gh_stars>1-10
from __future__ import division
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.special import jn, jn_zeros
import subprocess
def drumhead_height(n, k, distance, angle, t):
... |
<reponame>taimurhassan/crc<filename>scripts/mot_neural_solver/pl_module/pair_nuclei.py
import sacred
from sacred import Experiment
import os.path as osp
import pandas as pd
import scipy.io as sio
import numpy as np
from sacred import SETTINGS
SETTINGS.CONFIG.READ_ONLY_CONFIG=False
def pair_nuclei_and_generate_outpu... |
from scipy.sparse import dok_matrix
import pandas as pd
from cytoolz import itemmap
def long_dataframe_to_sparse_matrix(
df, index, vars, values, id_to_row=None, var_to_column=None
):
if id_to_row is None:
unique_index_values = df[index].unique()
id_to_row = dict(zip(unique_index_values, range... |
import inspect
import math as _math
from copy import deepcopy
import matplotlib.pyplot as _plt
import numpy as np
import pandas as pd
import statsmodels.api as _sm
from statslib._lib.gcalib import CalibType
class GeneralModel:
def __init__(self, gc, DM):
self.gc = deepcopy(gc)
self.DM = deepcopy... |
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy.fftpack import fft
from combined_functions import check_ft_grid
from scipy.constants import pi, c, hbar
from numpy.fft import fftshift
from scipy.io import loadmat
from time import time
import sys
import matplotlib.pyplot as plt
fr... |
<reponame>Kamysek/DeepLocalShapes
#!/usr/bin/env python3
# Based on: https://github.com/facebookresearch/DeepSDF using MIT LICENSE (https://github.com/facebookresearch/DeepSDF/blob/master/LICENSE)
# Copyright 2021-present <NAME>, <NAME>. All Rights Reserved.
import functools
import json
import logging
import math
impo... |
<reponame>kaka-lin/ML-Notes
import numpy as np
from scipy.special import softmax
np.set_printoptions(precision=6)
def k_softmax(x):
exp = np.exp(x)
return exp / np.sum(exp, axis=1)
if __name__ == "__main__":
x = np.array([[1, 4.2, 0.6, 1.23, 4.3, 1.2, 2.5]])
print("Input Array: ", x)
print("Sof... |
import numpy as np
from . import vector as V
def rbm_to_dualquat(rbm):
import cgkit.cgtypes as cg
q0 = cg.quat().fromMat(cg.mat3(rbm[:3,:3].T.tolist()))
q0 = q0.normalize()
q0 = np.array([q0.w, q0.x, q0.y, q0.z])
t = rbm[:3, 3]
q1 = np.array([
-0.5*( t[0]*q0[1] + t[1]*q0[2] + t[2]*... |
<gh_stars>10-100
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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... |
from collections import defaultdict, Counter
from itertools import product, permutations
from glob import glob
import json
import os
from pathlib import Path
import pickle
import sqlite3
import string
import sys
import time
import matplotlib as mpl
from matplotlib import colors
from matplotlib import pyplot as plt
fro... |
import simpy as sp
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats, integrate
def client(env, lamda, q, tic):
meant = 1/lamda
while True:
t = np.random.exponential(meant)
yield env.timeout(t)
q.put('job')
tic.append(env.now)
def ... |
<filename>clustviz/clarans.py
import random
from typing import Tuple, Dict, Any
import scipy
import itertools
import graphviz
import numpy as np
import pandas as pd
from clustviz.pam import plot_pam
from pyclustering.utils import euclidean_distance_square
from pyclustering.cluster.clarans import clarans as clarans_py... |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 11 09:18:37 2019
@author: if715029
"""
import pandas as pd
import numpy as np
import sklearn.metrics as skm
import scipy.spatial.distance as sc
#%% Leer datos
data = pd.read_excel('../data/Test de películas(1-16).xlsx', encoding='latin_1')
#%% Seleccionar datos (a mi e... |
####################################################################################################
#
# congruence_closure_module.py
#
# Authors:
# <NAME>
# <NAME>
#
# This module maintains a union-find structure for terms in Blackboard, which is currently only used
# for congruence closure. It should perhaps be integ... |
<filename>examples/acados_python/test/generate_c_code.py
#
# Copyright 2019 <NAME>, <NAME>, <NAME>,
# <NAME>, <NAME>, <NAME>, <NAME>,
# <NAME>, <NAME>, <NAME>, <NAME>,
# <NAME>, <NAME>, <NAME>, <NAME>, <NAME>
#
# This file is part of acados.
#
# The 2-Clause BSD License
#
# Redistribution and use in source and binary f... |
import cmath
import math
import logging
import random
import plotly
import pandas
|
# coding=utf-8
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed t... |
import copy
import cmath
import numpy
import scipy.linalg
from pauxy.estimators.thermal import greens_function, one_rdm_from_G, particle_number
from pauxy.estimators.mixed import local_energy
from pauxy.walkers.stack import PropagatorStack
from pauxy.walkers.walker import Walker
from pauxy.utils.linalg import regularis... |
# -*- coding: utf-8 -*-
################################################################################
# Copyright 2014, Distributed Meta-Analysis System
################################################################################
"""
This file provides methods for handling weighting across GCMs under
delta meth... |
# coding: utf-8
# In[1]:
import keras
# In[2]:
# scipy
import scipy
print( ' scipy: %s ' % scipy.__version__)
# numpy
import numpy
print( ' numpy: %s ' % numpy.__version__)
# matplotlib
import matplotlib
print( ' matplotlib: %s ' % matplotlib.__version__)
# pandas
import pandas
print( ' pandas: %s ' % pandas.__... |
# Author: <NAME>
# Collaborators: <NAME>, <NAME>, <NAME>
# Email : <EMAIL>
# Affiliation : Imperial Centre for Inference and Cosmology
# Status : Under Development
'''
Perform all additional operations such as interpolations
'''
import os
import logging
import numpy as np
import scipy.interpolate as itp
from typing i... |
<reponame>janismac/ksp_rtls_launch_to_rendezvous
import sys
import subprocess
import time
import json
import krpc
import math
import scipy.integrate
import numpy as np
from PrePlanningChecklist import PrePlanningChecklist
from PlannerUiPanel import PlannerUiPanel
from MainUiPanel import MainUiPanel
from ConfigUiPanel i... |
<reponame>catubc/MOTION
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import cv2, os, sys, glob
import scipy
import sklearn
import imageio
import matplotlib.cm as cm
import matplotlib
import time
from sklearn import decomposition, metrics, manifold, svm
from tsne import bh_s... |
import numpy as np
from sympy import simplify, sqrt, symbols
from sympy.stats import Normal, covariance as cov, variance as var
def regcoeffs(x, y, z):
covxy = cov(x, y)
covyz = cov(y, z)
varx = var(x)
vary = var(y)
varz = var(z)
# forward
f1 = simplify(covxy / varx)
f2 = simplify(covy... |
"""Defining and analysing axisymmetric optical systems."""
import itertools
from functools import singledispatch
from dataclasses import dataclass
from abc import ABC, abstractmethod
from typing import Sequence, Tuple, Mapping
import numpy as np
import scipy.optimize
from . import abcd, paraxial, functions, ri
from .fu... |
from operator import add, sub
import numpy as np
from scipy.stats import norm
class Elora:
def __init__(self, times, labels1, labels2, values, biases=0):
"""
Elo regressor algorithm for paired comparison time series prediction
Author: <NAME>
Args:
times (array of np.... |
import numpy as np
from scipy.stats import binom
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import MinMaxScaler
from scipy.special import erf
from learnware.algorithm.anomaly_detect.base import BaseAnomalyDetect
class iForest(BaseAnomalyDetect):
def __init__(self, n_estimators=100,
... |
<gh_stars>1-10
import senti_lexis
import datetime, string, numpy, spwrap, random time, sys, re
from sklearn import svm
from sklearn import cross_validation
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.cross_validation import KFold
from scipy.sparse import csr_matrix
def main():
for i in... |
import os
import cv2
from sklearn.cluster import KMeans, DBSCAN, MiniBatchKMeans
from scipy import spatial
from sklearn.preprocessing import StandardScaler
import numpy as np
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser(description='Challenge presentation example')
parser.add_argument('--data... |
import sys
from scipy.special import softmax
import torch.onnx
import onnxruntime as ort
import numpy as np
import tensorflow as tf
from tensorflow.keras import backend as K
from pytorch2keras.converter import pytorch_to_keras
from models.faceboxes import FaceBoxes
input_dim = 1024
num_classes = 2
model_path = "weig... |
# -*- coding: utf-8 -*-
"""
------ What is this file? ------
This script targets the istanbul_airbnb_raw.csv file. It cleans the .csv
file in order to prepare it for further analysis
"""
#%% --- Import Required Packages ---
import os
import pathlib
from pathlib import Path # To wrap around filepaths
impor... |
<filename>python/genre_classifier.py
import scipy.io.wavfile as wav
import numpy as np
import os
import pickle
import random
import operator
from python_speech_features import mfcc
dataset = []
training_set = []
test_set = []
# Get the distance between feature vectors
def distance(instance1, instance2, k):
mm1 =... |
<gh_stars>0
import numpy as np
from scipy.constants import mu_0, epsilon_0
import matplotlib.pyplot as plt
from PIL import Image
import warnings
warnings.filterwarnings('ignore')
from ipywidgets import interact, interactive, IntSlider, widget, FloatText, FloatSlider, fixed
from .Wiggle import wiggle, PrimaryWave, Refl... |
from __future__ import print_function
import os
import sys
import serial.tools.list_ports
from PyQt4 import QtCore
from PyQt4 import QtGui
from photogate_ui import Ui_PhotogateMainWindow
from photogate_serial import PhotogateDevice
from photogate_serial import getListOfPorts
import dependency_hack
try:
import scipy... |
# Copyright 2017 Regents of the University of California
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the follow... |
<filename>test.py
import random
from random import shuffle
import numpy as np
import tensorflow as tf
from tensorflow.python.tools import freeze_graph
import datetime
import time
import queue
import threading
import logging
from PIL import Image
import itertools
import yaml
import re
import os
import glob
import shutil... |
# coding=utf-8
import numpy as np
import scipy.interpolate as intpl
import scipy.sparse as sprs
def to_sparse(D, format="csc"):
"""
Transform dense matrix to sparse matrix of return_type
bsr_matrix(arg1[, shape, dtype, copy, blocksize]) Block Sparse Row matrix
coo_matrix(arg1[, shape, dtype, ... |
#!/usr/bin/env python
# ===============================================================================
# Copyright 2015 Geoscience Australia
#
# 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
... |
<reponame>tasicarl/TransitionListerner_public
"""
The transitionFinder module is used to calculate finite temperature
cosmological phase transitions: it contains functions to find the phase
structure as a function of temperature, and functions to find the transition
(bubble nucleation) temperature for each phase.
In co... |
import copy
import numpy as np
import torch
from scipy import optimize
import logging
def sharpness(model, criterion_fn, A, epsilon=1e-3, p=0, bounds=None):
"""Computes sharpness metric according to https://arxiv.org/abs/1609.04836.
Args:
model: Model on which to compute sharpness
criterion_... |
import numpy as np
from sympy import *
from math import *
from timeit import default_timer as timer
start = None
end = None
def maxXi(Xn,X):
n = None
d = None
for i in range(Xn.shape[0]):
if(np.copy(Xn[i,0]) != 0):
nk = abs(np.copy(Xn[i,0]) - np.copy(X[i,0]))/abs(np.copy(Xn[i,0]))
... |
import argparse
from genericpath import exists
import os
import time
import re
from tqdm import tqdm
import numpy as np
from scipy.io import wavfile
from wiener_scalart import wienerScalart
TIME = time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime())
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
WORKPLACE_DI... |
<filename>islandGen.py
#Import libraries
import random
import os
import noise
import numpy
import math
import sys
from chunks import Chunks as chk
from PIL import Image
import subprocess
from scipy.misc import toimage
import threading
random.seed(os.urandom(6))
#Delete old chunks
filelist = [ f for f in os.listdir(... |
import matplotlib.pyplot as plt
import csv
import statistics
import math
plt.title('Population Diversity')
plt.ylabel('Diversity Score')
plt.xlabel('Iteration Number')
random = []
randombars = []
rmin = []
rmax = []
hill = []
hillbars = []
hmin = []
hmax = []
evo = []
emin = []
emax = []
evobars = []
cross = []
cross... |
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