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@@ -13,87 +13,6 @@ from fdc.fdc import canberra_modified, FDC, Clustering
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from fdc.dataSheet import DataSheet
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-# class FdcToolbox:
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-#
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-# def __init__(self, file_name, index_col=0):
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-# data = pd.read_csv(file_name, index_col=0)
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-# self.data = data.sample(frac=1)
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-#
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-# self.value_dict = {}
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-# self.value_dict_rev = {}
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-# self.cols_cont = []
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-# self.cols_ord = []
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-# self.cols_nom = []
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-#
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-# for k in self.data.dtypes.keys():
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-# t = str(self.data.dtypes[k])
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-# if t[:3] == "int":
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-# self.cols_ord.append(k)
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-# elif t == "object":
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-# self.cols_nom.append(k)
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-# else:
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-# self.cols_cont.append(k)
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-#
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-# self.has_missing_values = False
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-# self.updateMissingValuesState()
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-#
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-# def updateMissingValuesState(self):
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-# self.has_missing_values = False
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-# for k in self.data.isna().sum():
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-# if k > 0:
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-# self.has_missing_values = True
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-# break
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-#
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-# def showStatistic(self):
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-# print(f"Fratures: {self.data.shape[1]}")
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-# print(f"Points: {self.data.shape[0]}")
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-# print(f"Columns:")
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-#
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-# for k in self.data.dtypes.keys():
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-# t = str(self.data.dtypes[k])
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-# e = " c"
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-# if k in self.cols_ord:
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-# e = " o"
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-# if k in self.cols_nom:
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-# e = " n"
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-# indentPair(k, t, e)
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-# print()
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-# print(f"Missing values:")
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-#
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-# n = 0
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-# d = self.data.isna().sum()
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-# for k in d.keys():
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-# if d[k] > 0:
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-# indentPair(k, str(d[k]))
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-# n += 1
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-# if n == 0:
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-# print(" none")
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-#
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-#
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-# def fixDatatypes(self):
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-# columnsToFix = []
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-# for k in self.data.dtypes.keys():
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-# if str(self.data.dtypes[k]) == "object":
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-# columnsToFix.append(k)
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-#
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-# self.value_dict = {}
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-# self.value_dict_rev = {}
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-# for c in columnsToFix:
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-# histogram = self.data[c].value_counts()
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-# self.value_dict[c] = { k : n for n, k in enumerate(histogram.keys()) }
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-# self.value_dict_rev[c] = { n : k for n, k in enumerate(histogram.keys()) }
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-#
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-# if len(self.value_dict.keys()) > 0:
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-# self.data.replace(self.value_dict, inplace=True)
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-#
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-# def fix_missing_values(self):
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-# self.data = fix_missing_values(self.data, 4)
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-# self.updateMissingValuesState()
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-#
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-
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-
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filename='healthcare-dataset-stroke-data.csv'
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np.random.seed(42)
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tb = DataSheet(filename)
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