def log1p(expr):
# Use trick to compute log1p for small values more accurate
# https://www.johndcook.com/blog/2012/07/25/trick-for-computing-log1x/
expr = ast.IdExpr(expr, to_reuse=True)
expr1p = utils.add(ast.NumVal(1.0), expr, to_reuse=True)
expr1pm1 = utils.sub(expr1p, ast.NumVal(1.0), to_reuse=True)
return ast.IfExpr(
utils.eq(expr1pm1, ast.NumVal(0.0)), expr,
utils.div(utils.mul(expr, ast.LogExpr(expr1p)), expr1pm1))
def _cosine_kernel(self, support_vector):
support_vector_norm = np.linalg.norm(support_vector)
if support_vector_norm == 0.0:
support_vector_norm = 1.0
feature_norm = ast.SqrtExpr(utils.apply_op_to_expressions(
ast.BinNumOpType.ADD, *[
utils.mul(ast.FeatureRef(i), ast.FeatureRef(i))
for i in range(len(support_vector))
]),
to_reuse=True)
safe_feature_norm = ast.IfExpr(utils.eq(feature_norm, ast.NumVal(0.0)),
ast.NumVal(1.0), feature_norm)
kernel = self._linear_kernel(support_vector / support_vector_norm)
kernel = utils.div(kernel, safe_feature_norm)
return kernel