<jupyter_start><jupyter_text>Distances<jupyter_code>activator = "sgmd"

mnist_sgmd = []
hands_sgmd = []
fashn_sgmd = []

for i in range(0, 10):
    dataset = 'mnist'
    df_cnn_relu0_1 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")
    dataset = 'handsign_mnist'
    df_cnn_relu0_2 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")
    dataset = 'fashion_mnist'
    df_cnn_relu0_3 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")

    max_norm = np.linalg.norm(df_cnn_relu0_1.corrwith(df_cnn_relu0_1))
    
    mnist_sgmd.append(np.linalg.norm(df_cnn_relu0_1.corrwith(df_cnn_relu0_2)) / max_norm)
    hands_sgmd.append(np.linalg.norm(df_cnn_relu0_2.corrwith(df_cnn_relu0_3)) / max_norm)
    fashn_sgmd.append(np.linalg.norm(df_cnn_relu0_3.corrwith(df_cnn_relu0_1)) / max_norm)

activator = "tanh"

mnist_tanh = []
hands_tanh = []
fashn_tanh = []

for i in range(0, 10):
    dataset = 'mnist'
    df_cnn_relu0_1 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")
    dataset = 'handsign_mnist'
    df_cnn_relu0_2 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")
    dataset = 'fashion_mnist'
    df_cnn_relu0_3 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")

    max_norm = np.linalg.norm(df_cnn_relu0_1.corrwith(df_cnn_relu0_1))
    
    mnist_tanh.append(np.linalg.norm(df_cnn_relu0_1.corrwith(df_cnn_relu0_2)) / max_norm)
    hands_tanh.append(np.linalg.norm(df_cnn_relu0_2.corrwith(df_cnn_relu0_3)) / max_norm)
    fashn_tanh.append(np.linalg.norm(df_cnn_relu0_3.corrwith(df_cnn_relu0_1)) / max_norm)
    
activator = "relu"

mnist_relu = []
hands_relu = []
fashn_relu = []

for i in range(0, 10):
    dataset = 'mnist'
    df_cnn_relu0_1 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")
    dataset = 'handsign_mnist'
    df_cnn_relu0_2 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")
    dataset = 'fashion_mnist'
    df_cnn_relu0_3 = pd.read_csv(dataset + "/results/" + activator + "/cnn_K" + str(i) + ".csv")

    max_norm = np.linalg.norm(df_cnn_relu0_1.corrwith(df_cnn_relu0_1))
    
    mnist_relu.append(np.linalg.norm(df_cnn_relu0_1.corrwith(df_cnn_relu0_2)) / max_norm)
    hands_relu.append(np.linalg.norm(df_cnn_relu0_2.corrwith(df_cnn_relu0_3)) / max_norm)
    fashn_relu.append(np.linalg.norm(df_cnn_relu0_3.corrwith(df_cnn_relu0_1)) / max_norm)<jupyter_output><empty_output>
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