#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------


# Builtin function Implements binary-class SVM with squared slack variables
#
# INPUT PARAMETERS:
# ---------------------------------------------------------------------------------------------
# NAME            TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# X               Double  ---         matrix X of feature vectors
# Y               Double  ---         matrix Y of class labels have to be a single column
# intercept       Boolean False       No Intercept ( If set to TRUE then a constant bias column is added to X)
# epsilon         Double  0.001       Procedure terminates early if the reduction in objective function 
#                      value is less than epsilon (tolerance) times the initial objective function value.
# lambda          Double  1.0         Regularization parameter (lambda) for L2 regularization
# maxiterations   Int     100         Maximum number of conjugate gradient iterations
# verbose         Boolean False       Set to true if one wants print statements updating on loss.
# column_id       Int     -1          The column Id used if one wants to add a ID to the print statement, 
#                                     Specificly usefull when L2SVM is used in MSVM.
# ---------------------------------------------------------------------------------------------
 

#Output(s)
# ---------------------------------------------------------------------------------------------
# NAME            TYPE    DEFAULT     MEANING
# ---------------------------------------------------------------------------------------------
# model           Double   ---        model matrix


m_l2svm = function(Matrix[Double] X, Matrix[Double] Y, Boolean intercept = FALSE,
    Double epsilon = 0.001, Double lambda = 1, Integer maxIterations = 100, 
    Boolean verbose = FALSE, Integer columnId = -1)
  return(Matrix[Double] model)
{
  #check input parameter assertions
  if(nrow(X) < 2)
    stop("L2SVM: Stopping due to invalid inputs: Not possible to learn a binary class classifier without at least 2 rows")
  if(epsilon < 0)
    stop("L2SVM: Stopping due to invalid argument: Tolerance (tol) must be non-negative")
  if(lambda < 0)
    stop("L2SVM: Stopping due to invalid argument: Regularization constant (reg) must be non-negative")
  if(maxIterations < 1)
    stop("L2SVM: Stopping due to invalid argument: Maximum iterations should be a positive integer")
  if(ncol(Y) < 1)
    stop("L2SVM: Stopping due to invalid multiple label columns, maybe use MSVM instead?")

  #check input lables and transform into -1/1
  check_min = min(Y)
  check_max = max(Y)

  num_min = sum(Y == check_min)
  num_max = sum(Y == check_max)

  # TODO make this a stop condition for l2svm instead of just printing.
  if(num_min + num_max != nrow(Y))
    print("L2SVM: WARNING invalid number of labels in Y: "+num_min+" "+num_max)

  # Scale inputs to -1 for negative, and 1 for positive classification
  if(check_min != -1 | check_max != +1)
    Y = 2/(check_max - check_min)*Y - (check_min + check_max)/(check_max - check_min)
    
  # If column_id is -1 then we assume that the fundamental algorithm is MSVM, 
  # Therefore don't print message.
  if(verbose & columnId == -1)
    print('Running L2-SVM ')

  num_samples = nrow(X)
  num_classes = ncol(Y)
  
  # Add Bias 
  num_rows_in_w = ncol(X)
  if (intercept) {
    ones  = matrix(1, rows=num_samples, cols=1)
    X = cbind(X, ones);
    num_rows_in_w += 1
  }
  
  w = matrix(0, rows=num_rows_in_w, cols=1)

  g_old = t(X) %*% Y
  s = g_old

  Xw = matrix(0, rows=nrow(X), cols=1)

  iter = 0
  continue = TRUE
  while(continue & iter < maxIterations)  {
    # minimizing primal obj along direction s
    step_sz = 0
    Xd = X %*% s
    wd = lambda * sum(w * s)
    dd = lambda * sum(s * s)
    continue1 = TRUE
    while(continue1){
      tmp_Xw = Xw + step_sz*Xd
      out = 1 - Y * (tmp_Xw)
      sv = (out > 0)
      out = out * sv
      g = wd + step_sz*dd - sum(out * Y * Xd)
      h = dd + sum(Xd * sv * Xd)
      step_sz = step_sz - g/h
      continue1 = (g*g/h >= epsilon)
    }

    #update weights
    w = w + step_sz*s
    Xw = Xw + step_sz*Xd

    out = 1 - Y * Xw
    sv = (out > 0)
    out = sv * out
    obj = 0.5 * sum(out * out) + lambda/2 * sum(w * w)
    g_new = t(X) %*% (out * Y) - lambda * w

    if(verbose) {
      colstr = ifelse(columnId!=-1, ", Col:"+columnId + " ,", " ,")
      print("Iter:" + toString(iter) + colstr + " Obj:" + obj)
    }

    tmp = sum(s * g_old)
    continue = (step_sz*tmp >= epsilon*obj & sum(s^2) != 0);

    #non-linear CG step
    be = sum(g_new * g_new)/sum(g_old * g_old)
    s = be * s + g_new
    g_old = g_new

    iter = iter + 1
  }
  model = w
}