89 lines
1.9 KiB
Python
89 lines
1.9 KiB
Python
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import nlu
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examples = [
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{
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"text": "icecream is cold",
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"parsed": ("exists-property-with-value", 'icecream', 'cold'),
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},
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{
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"text": "lava is dangerous",
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"parsed": ("exists-property-with-value", 'lava', 'dangerous')
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},
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{
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"text": "earth is a planet",
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"parsed": ("pertenence-to-group", 'earth', 'planet'),
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},
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{
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"text": "Green is a color",
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"parsed": ("pertenence-to-group", 'green', 'color'),
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},
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{
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"text": "a plane can fly",
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"parsed": ("has-capacity", 'plane', 'fly')
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},
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{
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"text": "a wale can swim",
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"parsed": ("has-capacity", 'wale', 'swim')
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}
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]
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base_knowledge = {
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'icecream': {
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"groups": ['noun', 'object', 'comestible', 'sweet'],
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},
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'lava': {
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"groups": ['noun', 'object'],
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},
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'earth': {
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"groups": ['noun', 'object', 'planet'],
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},
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'green': {
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"groups": ['noun', 'color', 'concept'],
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},
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'plane': {
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"groups": ['noun', 'object', 'vehicle', 'fast'],
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},
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'car': {
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"groups": ['noun', 'object', 'vehicle', 'slow-ish'],
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},
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'wale': {
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"groups": ['noun', 'object', 'living-being']
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},
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'cold': {
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"groups": ['property', 'temperature'],
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},
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'dangerous': {
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"groups": ['property'],
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},
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'planet': {
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"groups": ['noun', 'group'],
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},
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'color': {
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"groups": ['property', 'group'],
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},
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'fly': {
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"groups": ['verb'],
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},
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'swim': {
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"groups": ['verb'],
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},
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}
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def main():
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knowledge = nlu.KnowledgeBase(
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examples=[],
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trained=[],
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knowledge=base_knowledge
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)
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knowledge = nlu.train(knowledge, examples)
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for test in [{'text': 'a bus can run'}, {'text': 'io is a moon'}]:
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row = test['text'].lower().split()
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fit = nlu.get_fit(knowledge, row)
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print(test['text'], fit)
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if __name__ == '__main__':
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main()
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