import numpy as np

def eval_adaBoost_leastSquare(X, alphaK, para):
    # INPUT:
    # para		: parameters of simple classifier (K x (D +1)) 
    #           : dimension 1 is w0
    #           : dimension 2 is w1
    #           : dimension 3 is w2
    #             and so on
    # alphaK    : classifier voting weights (K x 1)
    # X         : test data points (numSamples x numDim)
    #
    # OUTPUT:
    # classLabels: labels for data points (numSamples x 1)
    # result     : weighted sum of all the K classifier (scalar)

    #####Insert your code here for subtask 1e#####

    return [classLabels, result]


import numpy as np

def eval_adaBoost_leastSquare(X, alphaK, para):
    # INPUT:
    # para		: parameters of simple classifier (K x (D +1)) 
    #           : dimension 1 is w0
    #           : dimension 2 is w1
    #           : dimension 3 is w2
    #             and so on
    # alphaK    : classifier voting weights (K x 1)
    # X         : test data points (numSamples x numDim)
    #
    # OUTPUT:
    # classLabels: labels for data points (numSamples x 1)
    # result     : weighted sum of all the K classifier (scalar)

    #####Insert your code here for subtask 1e#####

    return [classLabels, result]


# INPUT: # para		: parameters of simple classifier (K x (D +1)) #           : dimension 1 is w0 #           : dimension 2 is w1 #           : dimension 3 is w2 #             and so on # alphaK    : classifier voting weights (K x 1) # X         : test data points (numSamples x numDim) # # OUTPUT: # classLabels: labels for data points (numSamples x 1) # result     : weighted sum of all the K classifier (scalar) #####Insert your code here for subtask 1e#####