# method = 3
start_cluster = 2
end_cluster = 10
np_cluster = np.array([i for i in range(start_cluster, end_cluster + 1, 1)])
# Show Method List
print('<---1. Use the calinski_harabaz_score to evaluate--->')
print('<---2. Use the calculate_by_deviation to evaluate--->')
print('<---3. Use the silhouette_score to evaluate--->')
print('<---4. Show Cluster Scatter--->')
method = input('<---Please Choose--->: ')
for i in range(1, 7, 1):
# Load data and reduced the dimension
org_data, org_label = LoadData.get_split_original_data(i)
reduced_data = np.array([])
if algorithm == 'PCA' or 'pca':
reduced_data = ra.pca(org_data, dim)
elif algorithm == 'Isomap' or 'isomap':
reduced_data = ra.isomap(org_data, dim)
elif algorithm == 'tSNE':
reduced_data = ra.tsne(org_data, dim)
else:
print('<---None dimension reduced--->')
normalized_data = Normalize.normalization(reduced_data)
evaluated_scores = np.array([])
values = np.array([])
elif method == '2':
dim = 3
all_label = ['C1', 'C2', 'C3', 'C4', 'C5', 'C6']
# cluster_num = {'C1': 6, 'C2': 5, 'C3': 5, 'C4': 5, 'C5': 5, 'C6': 4}
# cluster_num = {'C1': 0, 'C2': 2, 'C3': 2, 'C4': 2, 'C5': 0, 'C6': 0}
cluster_num = {'C1': 2, 'C2': 2, 'C3': 2, 'C4': 2, 'C5': 2, 'C6': 2}
header = ['Dim1', 'Dim2', 'Dim3', 'Label']
pd_data = pd.DataFrame()
np_data = np.array([])
nn_category = np.array([])
for element in all_label:
if cluster_num[element] == 0:
nn_category = np.append(nn_category, element)
# 讀檔, 降維, 正規化
org_data, org_label = LoadData.get_split_original_data(element[1])
org_label = np.array(['C' + str(i) for i in org_label])
reduced_data = ra.tsne(org_data, dim)
normalized_data = Normalize.normalization(reduced_data).astype(
'float64')
np_tmp = np.concatenate(
(normalized_data, org_label.reshape(-1, 1)), axis=1)
pd_tmp = pd.DataFrame(np_tmp, columns=header)
pd_data = pd.concat((pd_data, pd_tmp), axis=0)
else:
for num in range(cluster_num[element]):
nn_category = np.append(nn_category, element + '_' + str(num))
normalized_data, org_label = LoadData.get_refactor_data(
element, num)
# print(normalized_data, normalized_data.shape)
