% Example Training Data (Categorical)
training_categorical_data = {'cat', 'dog', 'fish', 'dog', 'cat'};
% Convert Categorical Data to Numeric for Training
[unique_categories, ~, numeric_categories] = unique(training_categorical_data);
category_to_number_map = containers.Map(unique_categories, num2cell(1:length(unique_categories)));
numeric_training_data = cell2mat(values(category_to_number_map, num2cell(training_categorical_data)));
% Training Process with numeric_training_data
% [Your neural network training code goes here]
% Example New Data (Categorical)
new_categorical_data = {'dog', 'cat', 'bird'};
% Convert New Categorical Data to Numeric Using Training Mapping
numeric_new_data = zeros(size(new_categorical_data));
for i = 1:length(new_categorical_data)
if isKey(category_to_number_map, new_categorical_data{i})
numeric_new_data(i) = category_to_number_map(new_categorical_data{i});
else
% Handle unseen categories, e.g., assign a special number or ignore
numeric_new_data(i) = NaN; % Assign NaN for unseen categories
end
end
% Now, numeric_new_data is ready for use with the trained model
% [Your prediction or evaluation code goes here]
- The training data training_categorical_data is a cell array of categorical strings. This is converted to numeric_training_data using a mapping (category_to_number_map).
- The new data new_categorical_data is then converted using the same mapping. Unseen categories (like 'bird' in this example) are handled separately; here, I've assigned NaN to them, but you can choose another method as appropriate.
- You'll need to insert your specific neural network training and prediction code where indicated. The numeric_training_data and numeric_new_data arrays are what you'd use for training and prediction, respectively.
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