def get_cxx_kernel(options):
options_cxx = soap.Options()
log << "Kernel:" << log.endl
for k, v in options.iteritems():
log << "- Setting %s=%s" % (k, str(v)) << log.endl
options_cxx.set(k, v)
kernel = soap.Kernel(options_cxx)
return kernel
def evaluate_kernel(dset, options):
kernel = soap.Kernel(options)
symmetric = True
K = kernel.evaluate(dset, dset, symmetric, "float64")
z = 1. / np.sqrt(K.diagonal())
K = K * np.outer(z, z)
log << log.mg << "Kernel =" << log.endl
log << K << log.endl
return K
def evaluate_kernel(dset, options=None, kernel=None):
assert not (options == kernel == None)
if kernel is None: kernel = soap.Kernel(options)
symmetric = True
kernel.evaluateAll(dset, dset, symmetric)
for slot in range(kernel.n_output):
K = kernel.getOutput(slot, "float64")
z = 1. / np.sqrt(K.diagonal())
K = K * np.outer(z, z)
log << "Kernel: Output slot" << slot << log.endl
log << K << log.endl
def test_reduction_matrix_average(options):
log << log.mg << "<test_reduction_matrix_average>" << log.flush
kernel_options = soap.Options()
kernel_options.set("basekernel_type", "dot")
kernel_options.set("base_exponent", 3.)
kernel_options.set("base_filter", False)
kernel_options.set("topkernel_type", "average")
kernel = soap.Kernel(kernel_options)
for i in range(9):
K_base = np.random.uniform(size=(i + 1, ((i + 2) % 4) + 1))
k = kernel.evaluateTop(K_base, "float64")
P = kernel.attributeTopGetReductionMatrix(K_base, "float64")
k_red = np.sum(K_base * P)
assert_equal(k - k_red, 0.0, 1e-6)
log << log.endl
def test_attribution_average(dset, options):
log << log.mg << "<test_attribution_average>" << log.flush
kernel_options = soap.Options()
kernel_options.set("basekernel_type", "dot")
kernel_options.set("base_exponent", 3.)
kernel_options.set("base_filter", False)
kernel_options.set("topkernel_type", "average")
kernel = soap.Kernel(kernel_options)
K = kernel.evaluate(dset, dset, False, "float64")
for i in range(len(dset)):
KK = kernel.attributeLeft(dset[i], dset, "float64")
KK_sum = np.sum(KK, axis=0)
for j in range(len(dset)):
assert_equal(KK_sum[j] - K[i, j], 0.0, 1e-6)
log << log.endl
if __name__ == "__main__":
configs = soap.tools.io.read('data/structures.xyz')
# Compute SOAP descriptors
options = soap.soapy.configure_default()
dset = evaluate_soap(configs, options)
# Multikernel: Variant A
log << log.mg << "Variant A" << log.endl
kernel_options = soap.Options()
kernel_options.set("basekernel_type", "dot")
kernel_options.set("base_exponent", 3.)
kernel_options.set("base_filter", False)
kernel_options.set("topkernel_type", "average;canonical;rematch")
kernel_options.set("canonical_beta", 0.5)
kernel_options.set("rematch_gamma", 0.05)
kernel_options.set("rematch_eps", 1e-6)
kernel_options.set("rematch_omega", 1.0)
evaluate_kernel(dset, options=kernel_options)
# Multikernel: Variant B
log << log.mg << "Variant B" << log.endl
kernel_options.set("topkernel_type", "")
kernel = soap.Kernel(kernel_options)
for beta in [0.01, 0.05, 0.1, 0.5, 1.0, 10.0, 100.0]:
kernel_options.set("topkernel_type", "canonical")
kernel_options.set("canonical_beta", beta)
kernel.addTopkernel(kernel_options)
evaluate_kernel(dset, kernel=kernel)
def __init__(self, kernel_options_top, kernel_options_super):
self.kernel_top = soap.Kernel(kernel_options_top)
self.kernel_super = soap.Kernel(kernel_options_super)
return