import numpy as np
import sklearn.svm


def dataset3Params(X, y, Xval, yval):
    """returns your choice of C and sigma. You should complete
    this function to return the optimal C and sigma based on a
    cross-validation set.
    """

# You need to return the following variables correctly.
    C = 1
    sigma = 0.3

# ====================== YOUR CODE HERE ======================
# Instructions: Fill in this function to return the optimal C and sigma
#               learning parameters found using the cross validation set.
#               You can use svmPredict to predict the labels on the cross
#               validation set. For example, 
#                   predictions = svmPredict(model, Xval)
#               will return the predictions on the cross validation set.
#
#  Note: You can compute the prediction error using 
#        mean(double(predictions ~= yval))
#


# =========================================================================
    return C, sigma

import numpy as np
import sklearn.svm


def dataset3Params(X, y, Xval, yval):
    """returns your choice of C and sigma. You should complete
    this function to return the optimal C and sigma based on a
    cross-validation set.
    """

# You need to return the following variables correctly.
    C = 1
    sigma = 0.3

# ====================== YOUR CODE HERE ======================
# Instructions: Fill in this function to return the optimal C and sigma
#               learning parameters found using the cross validation set.
#               You can use svmPredict to predict the labels on the cross
#               validation set. For example, 
#                   predictions = svmPredict(model, Xval)
#               will return the predictions on the cross validation set.
#
#  Note: You can compute the prediction error using 
#        mean(double(predictions ~= yval))
#


# =========================================================================
    return C, sigma

returns your choice of C and sigma. You should complete     this function to return the optimal C and sigma based on a     cross-validation set. # You need to return the following variables correctly. # ====================== YOUR CODE HERE ====================== # Instructions: Fill in this function to return the optimal C and sigma #               learning parameters found using the cross validation set. #               You can use svmPredict to predict the labels on the cross #               validation set. For example, #                   predictions = svmPredict(model, Xval) #               will return the predictions on the cross validation set. # #  Note: You can compute the prediction error using #        mean(double(predictions ~= yval)) # # =========================================================================