vikram-s-narayan

Precogly Individual Contributor License Agreement

Thank you for your interest in contributing to Precogly. This Contributor License Agreement ("Agreement") documents the rights granted by contributors to Precogly.

By signing this Agreement, you accept and agree to the following terms and conditions for your present and future Contributions submitted to Precogly. Except for the license granted herein to Precogly and recipients of software distributed by Precogly, you reserve all right, title, and interest in and to your Contributions.

1. Definitions

"You" (or "Your") shall mean the copyright owner or legal entity authorized by the copyright owner that is making this Agreement with Precogly. For legal entities, the entity making a Contribution and all other entities that control, are controlled by, or are under common control with that entity are considered to be a single Contributor.

vikram-s-narayan

# The purpose of this gist is to help pinpoint a potential bug in 
# QuasiMonteCarlo. This gist shows the working of the GEKPLS surrogate model 
# without the potential interferences from other Surrogates dependencies. 
# To run the gist and reproduce the results:
# Run this gist with QuasiMonteCarlo@0.2.19; it can be seen that it produces
# far better results without the first data point in the test dataset
# Additional notes: 
# - Ensure that no other packages that depend on QuasiMonteCarlo are in your package environment
# - QuasiMonteCarlo@0.2.17 and QuasiMonteCarlo@0.2.18 throw errors. More info for the cause can 
#   be seen here - https://github.com/SciML/QuasiMonteCarlo.jl/releases/tag/v0.2.17

vikram-s-narayan

# The purpose of this gist is to help pinpoint a potential bug in 
# QuasiMonteCarlo. This gist shows the working of the GEKPLS surrogate model 
# without the potential interferences from other Surrogates dependencies. 
# To run the gist and reproduce the results:
# Run this gist with QuasiMonteCarlo@0.2.16 => all tests pass (evidenced by 3 true prints)
# Then run this gist with QuasiMonteCarlo@0.2.19 => all tests fail in a fashion similar to 
# the failing tests of Surogates.jl - https://github.com/SciML/Surrogates.jl/pull/420
# Additional notes: 
# - Ensure that no other packages that depend on QuasiMonteCarlo are in your package environment
# - QuasiMonteCarlo@0.2.17 and QuasiMonteCarlo@0.2.18 throw errors. More info for the cause can 

vikram-s-narayan

Project Details:

Org: SciML-NumFOCUS

Contributor: Vikramaditya Narayan

Mentors: Chris Rackauckas and Ranjan Anantharaman

What Work Was Done:

vikram-s-narayan

#Add this line below 
#above https://github.com/SMTorg/smt/blob/4a4df255b9259965439120091007f9852f41523e/smt/surrogate_models/krg_based.py#L1165
#to try different theta values
optimal_theta = np.array([0.0001, 0.0001]) 

optimal_rlf_value, optimal_par = self._reduced_likelihood_function(
    theta=optimal_theta
)

vikram-s-narayan

using Surrogates
using Zygote
using SurrogatesPolyChaos
using SurrogatesAbstractGPs
using AbstractGPs

function vector_of_tuples_to_matrix(v)
    #helper function to convert training data generated by surrogate sampling into a matrix suitable for GEKPLS
    num_rows = length(v)
    num_cols = length(first(v))

vikram-s-narayan

using Surrogates
using Zygote
using SurrogatesPolyChaos
using SurrogatesAbstractGPs
using AbstractGPs

function vector_of_tuples_to_matrix(v)
    #helper function to convert training data generated by surrogate sampling into a matrix suitable for GEKPLS
    num_rows = length(v)
    num_cols = length(first(v))

vikram-s-narayan

using Surrogates
using Zygote
using SurrogatesPolyChaos

function vector_of_tuples_to_matrix(v)
    #helper function to convert training data generated by surrogate sampling into a matrix suitable for GEKPLS
    num_rows = length(v)
    num_cols = length(first(v))
    K = zeros(num_rows, num_cols)
    for row in 1:num_rows

vikram-s-narayan

using Statistics
using ScikitLearn
using LinearAlgebra

@sk_import cross_decomposition: PLSRegression

####### START OF ALL FUNCTIONS ##########

function _ge_compute_pls(X, y, n_comp, grads, delta_x, xlimits, extra_points)
    """

vikram-s-narayan

using LinearAlgebra
using ExtendableSparse

Base.copy(t::Tuple) = t

mutable struct RadialBasis{F,Q,X,Y,L,U,C,S,D} <: AbstractSurrogate
    phi::F
    dim_poly::Q
    x::X
    y::Y