predictPhases()

Description

predictPhases() is a function that reads the input dataset and makes prediction on the molecule states based on a pre-trained Machine Learning model. It is similar to the getPhases() function, however it uses coordinates and distances array instead of the instance of the System class as input.

The coordinates and distances arrays can be obtained using respectively the functions getCoordinates() and getDistances(). The models can be trained using the function generateModel().

Argument, keywords and outputs

Input(s) / Argument(s)

Name	Flag	Type	Description
Coordinates		np.ndarray	Array of the coordinates of the atoms of the molecules, merged between all systems and all frames. Dimension(s) should be in (N frames * N molecules, 2 * N atoms per molecule).
Distances		np.ndarray	Array of the distances of the atoms of the molecules, merged between all systems and all frames. Dimension(s) should be in (N frames * N molecules, N distances).
Models		str or dict of models	Path to the model file to load or dictionary of the Scikit-Learn models to use to predict the states of the molecules.

Output(s)

Name	Type	Description
Phases	np.ndarray	Array of all the molecule phases predicted in the system. Dimension(s) are in (N frames, N molecules).

Examples

Predict the phases from a file

The following example will analyse the coordinates array coordinates_array and the distances array distances_array using the models pre-trained in the file new_model.lpm and return the phases of the molecules in the variable phase_array.

import mllpa

phase_array = mllpa.predictPhases(coordinates_array, distances_array, "new_model.lpm")

Predict the phases from a model dictionary

The following example will analyse the coordinates array coordinates_array and the distances array distances_array using the models pre-trained in the variable models and return the phases of the molecules in the variable phase_array.

phase_array = mllpa.predictPhases(coordinates_array, distances_array, models)