blob: 65c6b04d5045f321789aaa445e883a5a3ca338e5 [file]
#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
#-------------------------------------------------------------
# This builtin function implements the prediction for NaiveBayes classification.
#
# INPUT:
# ------------------------------------------------------------------------------
# X Matrix of test data with N rows.
# P Class prior probabilities
# C Class conditional feature distributions
# ------------------------------------------------------------------------------
#
# OUTPUT:
# ------------------------------------------------------------------------------
# Y A matrix containing the top-K item-ids with highest predicted class.
# YRaw A matrix containing predicted class.
# ------------------------------------------------------------------------------
m_naiveBayesPredict = function(Matrix[Double] X, Matrix[Double] P, Matrix[Double] C)
return (Matrix[Double] YRaw, Matrix[Double] Y)
{
numRows = nrow(X)
model = cbind(C, P)
ones = matrix(1, rows=numRows, cols=1);
X_w_ones = cbind(X, ones);
YRaw = X_w_ones %*% t(log(model));
Y = rowIndexMax(YRaw);
}