예제 #1
0
def trial_running_pca():
    lower_dimension = 4
    iris_dataset = IRIS(crossvalidation=1)
    test_data,test_label,train_data,train_label = iris_dataset.load_data()
    test_points,test_dimension = test_data.shape
    train_points,train_dimension = train_data.shape
    complete_data = zeros((test_points+train_points,test_dimension),float32)
    complete_data[:test_points,:] = test_data
    complete_data[test_points:,:] = train_data
    '''
    A = LDA(complete_data.T)
    dimension_reduced_data = dot(complete_data,A)
    markers = {'1':'r.','2':'g.','3':'b.'}
    plot_2d(dimension_reduced_data,test_label.tolist()+train_label.tolist(),markers)
    '''
    pca = PCA(train_data, output_dim=lower_dimension)
    dimension_reduced_test_data = pca.execute(test_data,n=lower_dimension)
    dimension_reduced_train_data = pca.execute(train_data,n=lower_dimension)
    train_data = train_data.T
    test_data  = test_data.T
    dimension_reduced_test_data = dimension_reduced_test_data.T
    dimension_reduced_train_data = dimension_reduced_train_data.T
    classification_error(dimension_reduced_train_data, train_label, dimension_reduced_test_data, test_label )
    print train_data.shape
    print test_data.shape
    print dimension_reduced_test_data.shape    
    
    markers = {'1':'r.','2':'g.','3':'b.', '4':'b*', '5':'g*', '6':'r*','7':'rs', '8':'gs','9':'bs'}
    plot_2d(dimension_reduced_test_data,test_label.tolist(),markers)
예제 #2
0
def run_kde():
    print "-- Starting kde--"
    landsat_dataset = IRIS(crossvalidation = 0)
    test_data,test_label,train_data,train_label = landsat_dataset.load_data()
    
    print test_data.shape
    print train_data.shape
    
    lower_dimension = 2
    kde_cub = KDECUB(train_data, train_label, lower_dimension, test_data = test_data, test_label = test_label)
    kde_cub.train()