Created
January 24, 2017 21:04
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text ANN predict
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# probability threshold | |
ERROR_THRESHOLD = 0.2 | |
# load our calculated synapse values | |
synapse_file = 'synapses.json' | |
with open(synapse_file) as data_file: | |
synapse = json.load(data_file) | |
synapse_0 = np.asarray(synapse['synapse0']) | |
synapse_1 = np.asarray(synapse['synapse1']) | |
def classify(sentence, show_details=False): | |
results = think(sentence, show_details) | |
results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD ] | |
results.sort(key=lambda x: x[1], reverse=True) | |
return_results =[[classes[r[0]],r[1]] for r in results] | |
print ("%s \n classification: %s" % (sentence, return_results)) | |
return return_results | |
classify("sudo make me a sandwich") | |
classify("how are you today?") | |
classify("talk to you tomorrow") | |
classify("who are you?") | |
classify("make me some lunch") | |
classify("how was your lunch today?") | |
print() | |
classify("good day", show_details=True) |
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