sofianehaddad

# This function parses the logs of the FCE class
def parse_fce_logs(filename):
    f = open(filename, "r")
    path_indices = list()
    relative_cross_validation_error = list()
    q_squares = list()
    for line in f:
        tokens = line.split(" ")
        if len(tokens) > 0:
            sub_tokens = tokens[0].split("=")

sofianehaddad

import openturns as ot
import openturns.viewer as otv
import numpy as np
 
def draw_loo(error):
    g = ot.Graph()
    g.setXTitle("step")
    g.setYTitle("error")
    g.setAxes(True)
    g.setGrid(True)

sofianehaddad

import openturns as ot
from openturns.usecases.wingweight_function import WingWeightModel
import time
import pandas as pd

ot.Log.Show(ot.Log.NONE)
m = WingWeightModel()

inputNames = m.distributionX.getDescription()

sofianehaddad

import openturns as ot

inputDimension = 2

""" 
Learning data
Box in [0,10]x[0,10]
"""
levels = [6, 3]
box = ot.Box(levels)

sofianehaddad

import openturns as ot
import numpy as np

def compute_kriging_virtual_loo(inputSample, outputSample, covariance_model, basis=None, transformation=None):
    """
     We write here the Virtual cross validation system

      # K   b + F c = y
      # F^t b       = 0

sofianehaddad

import openturns as ot

inputDimension = 2

# Learning data
levels = [8, 5]
levels = [6, 3]
box = ot.Box(levels)
inputSample = box.generate()
# Scale each direction

sofianehaddad

# -*- coding: utf-8 -*-
"""
Created on Mon Nov 23 14:14:19 2020

@author: lbrevaul
"""

import openturns as ot
import math as m
import numpy as np

sofianehaddad

sofianehaddad

import openturns as ot

def getConditionalMarginalCovariance(krigingResult, x):

    def _getConditionalMarginalCovariancePoint(x):
        cov = krigingResult.getConditionalCovariance(x)
        return cov

    def _getConditionalMarginalCovarianceSample(x):
        cov_coll = []

sofianehaddad

Point ExpertMixture::evaluate_supervised(const Point & inP) const
{
  if (supervised_) return evaluate_supervised(inP);
  return evaluate_non_supervised(inP);
}

Point ExpertMixture::evaluate_non_supervised(const Point & inP) const
{
  const UnsignedInteger inputDimension = getInputDimension();
  if (inP.getDimension() != inputDimension) throw InvalidArgumentException(HERE) << "Error: expected a point of dimension=" << inputDimension << " and got a point of dimension=" << inP.getDimension();