Example how to generate a local kernel matrix with FCHL

fchl_example.py

#!/usr/bin/env python2

import sys
sys.path.insert(0,"/home/andersx/dev/qml/fchl_arad/build/lib.linux-x86_64-2.7")
# sys.path.insert(0,"/home/andersx/dev/qml/fchl/build/lib.linux-x86_64-2.7")

import numpy as np

from time import time

import qml
import qml.fchl
from qml.fchl import generate_fchl_representation
from qml.fchl import get_local_symmetric_kernels_fchl

def parse_dir(xyz_dir, datafile):

    f = open(datafile, "r")
    lines = f.readlines()
    f.close()

    compounds = []

    for line in lines[:1000]:

        if "#" in line[:2]: continue

        tokens = line.split()
        name = tokens[0]

        energy = float(tokens[1])

        xyz_name = "%s%s" % (xyz_dir, name)
        c = qml.Compound(xyz=xyz_name)
        c.properties = energy
        compounds.append(c)


    return compounds

cut_distance = 1e6

if __name__ == "__main__":

    xyz_dir = sys.argv[1]
    datafile = sys.argv[2]

    compounds = parse_dir(xyz_dir, datafile)
    Y = np.array([compound.properties for compound in compounds])
    np.save("Y.npy", Y)
    nmax = max([compound.natoms for compound in compounds])

    X = []

    for c in compounds:

        rep = generate_fchl_representation(c.coordinates, c.nuclear_charges,
            size=nmax, neighbors=nmax, cut_distance=cut_distance)
        X.append(rep)

    X = np.array(X)
    np.save("X.npy", X)
    print X.shape
    sigmas = [0.01 * 2**i for i in range(20)]
    print "K ..."
    t_start = time()
    K = get_local_symmetric_kernels_fchl(X, sigmas,
        cut_distance=cut_distance,
        alchemy='off',

        )

    print "done",  time() - t_start
    np.save("K_arad.npy" , K)
    print K[10]
    print K.shape