Hi Rusyaidi,
In supervised learning, models require the provision of expected values or classes alongside the data entries they will predict on. Therefore, one way to create a training dataset from the provided text files is to assign all entries within a text file to a specific class. Here is a step-by-step procedure to accomplish this:
- Begin by loading the contents of the text files
fileNames = {'bearing_defect_ball.txt', 'bearing_defect_on_Retainer.txt'...
'bearing_defect_Outer_Race_vibration.txt','bearing_vibration_defect_on_seal.txt',...
'bearing_vibration_defect_on_shield.txt', 'defective_Inner_Race_vibration.txt',...
'Normal_bearing_condition_raw_data.txt'};
data{i} = readtable(fileNames{i}, 'Format', '%f%f', 'ReadVariableNames', false);
data{7} = readtable(fileNames{7}, 'HeaderLines', 1);
- Next, add an extra column to dataset. This column should indicate the class to which each entry belongs.
class = {'class_1','class_2','class_3','class_4','class_5', 'class_6','class_7'};
data{i}.Condition = repmat(class(i), height(data{i}), 1);
dataSet = vertcat(data{:});
cv = cvpartition(height(dataSet), 'HoldOut', 0.3);
trainData = dataSet(training(cv), :);
testData = dataSet(test(cv), :);
model = fitctree(trainData(:, {'Var1', 'Var2'}), trainData.Condition);
This method represents one of several approaches to constructing a training dataset for supervised models. The final structure of the training dataset will be determined by the intended use of the data contained in the text files.
