MVPR 1.24

Multi-variable polynomial regression for curve fitting.

Overview

MVPR is available on PyPI, and can be installed via

pip install MVPR

This package fits a multi-variable polynomial equation to a set of data using cross validation. The solution is regularised using truncated singular value decomposition, where the truncation point is found using a golden section search.

Example

consider a 3-D set of data, plotted as follows:

First import the data:

df= pd.read_excel(r'C:\Users\filepath\data.xlsx')
data=df.to_numpy()
df= pd.read_excel(r'C:\Users\filepath\targets.xlsx')
targets=df.to_numpy()

select the proportions of data for cross-validation

proportion_training = 0.9
num_train_samples = round(len(data[:,0])*0.8)
num_val_samples = round(len(data[:,0]))-num_train_samples

standardise:

mean_dat = data[:, :].mean(axis=0)
std_dat = data[:, :].std(axis=0)

data -= mean_dat

if 0 not in std_dat:
    data[:, :] /= std_dat

training_data = data[:num_train_samples, :]
training_targets = targets[:num_train_samples, :]

validation_data = data[-num_val_samples :, :]
validation_targets = targets[-num_val_samples :, :]

call the following

M = MVP.MVPR_forward(training_data, training_targets, validation_data, validation_targets)

optimum_order = M.find_order()
coefficient_matrix = M.compute_CM(optimum_order)

predicted_validation = M.compute(coefficient_matrix, optimum_order, validation_data)

df = pd.DataFrame(predicted_validation)
df.to_excel(r'C:\Users\EEE Admin\Desktop\test_MVPR\predicted.xlsx')

The fitted curve: