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self.predict_vector = predict_vector
self.digit = digit
self.weights = None
if isinstance(transpose, bool):
self.transpose = transpose
else:
self.transpose = False
if isfile(file):
matrix_param = __obj_file_handler__(self, file)
elif isinstance(matrix, dict):
matrix_param = __obj_matrix_handler__(matrix, transpose)
else:
matrix_param = __obj_vector_handler__(
self, actual_vector, predict_vector, threshold, sample_weight)
if len(matrix_param[0]) < 2:
raise pycmVectorError(CLASS_NUMBER_ERROR)
self.classes = matrix_param[0]
self.table = matrix_param[1]
self.matrix = self.table
self.normalized_table = normalized_table_calc(self.classes, self.table)
self.normalized_matrix = self.normalized_table
self.TP = matrix_param[2]
self.TN = matrix_param[3]
self.FP = matrix_param[4]
self.FN = matrix_param[5]
statistic_result = class_statistics(
TP=matrix_param[2],
TN=matrix_param[3],
FP=matrix_param[4],
FN=matrix_param[5],
classes=matrix_param[0],
table=matrix_param[1])