76 lines
1.9 KiB
Python
Executable file
76 lines
1.9 KiB
Python
Executable file
#!/usr/bin/env python3
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import xcdat
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import os
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# Prepare the dataset of keywords
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keyset = xcdat.Keyset()
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keyset.append('AirPods')
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keyset.append('AirTag')
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keyset.append('Mac')
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keyset.append('MacBook')
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keyset.append('MacBook_Air')
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keyset.append('MacBook_Pro')
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keyset.append('Mac_Mini')
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keyset.append('Mac_Pro')
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keyset.append('iMac')
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keyset.append('iPad')
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keyset.append('iPhone')
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keyset.append('iPhone_SE')
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keyset.complete()
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# You can choose a trie type from the four types.
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Trie = xcdat.Trie8
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# Trie = xcdat.Trie16
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# Trie = xcdat.Trie7
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# Trie = xcdat.Trie15
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# Build the trie dictionary
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trie = Trie(keyset)
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# Get the statistics
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print(f'Number of keys: {trie.num_keys()}')
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print(f'Number of trie nodes: {trie.num_nodes()}')
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print(f'Number of DA units: {trie.num_units()}')
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print(f'Memory usage in bytes: {trie.memory_in_bytes()}')
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# Lookup the ID for a query key.
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print(f'Lookup(Mac_Pro) = {trie.lookup("Mac_Pro")}')
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print(f'Lookup(Google_Pixel) = {trie.lookup("Google_Pixel")}')
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# Decode the key for a query ID.
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print(f'Decode(4) = {trie.decode(4)}')
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# Common prefix search
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print('CommonPrefixSearch(MacBook_Air) = {')
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itr = trie.make_prefix_iterator('MacBook_Air')
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while itr.next():
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print(f' ({itr.decoded()}, {itr.id()})')
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print('}')
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# Predictive search (in lexicographical order).
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print('PredictiveSearch(Mac) = {')
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itr = trie.make_predictive_iterator('Mac')
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while itr.next():
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print(f' ({itr.decoded()}, {itr.id()})')
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print('}')
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# Enumerate all the keys (in lexicographical order).
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print('Enumerate() = {')
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itr = trie.make_enumerative_iterator()
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while itr.next():
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print(f' ({itr.decoded()}, {itr.id()})')
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print('}')
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# Save the trie to the file
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trie.save('dic.bin')
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# Load the trie from the file
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other = Trie('dic.bin')
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assert trie.num_keys() == other.num_keys()
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assert trie.num_nodes() == other.num_nodes()
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assert trie.num_units() == other.num_units()
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assert trie.memory_in_bytes() == other.memory_in_bytes()
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os.remove('dic.bin')
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