def predict(url): concept = ClarifaiCustomModel() max_confidence = 0.0 classification = None for word in language.keys(): #print word result = concept.predict(url, word) confidence = result['urls'][0]['score'] print word, confidence if confidence > max_confidence: max_confidence = confidence classification = word if classification == None: return None else: return (classification, max_confidence)
from clarifai_basic import ClarifaiCustomModel from json import dumps from classifications import language concept = ClarifaiCustomModel() for model in language.keys(): print "current model to train: " + model for url in language[model]: concept.positive(url, model) #print "training url:%s on model %s" % (url, model) for key, value in language.iteritems(): if key != model: for neg_url in value: print neg_url concept.negative(neg_url, model) concept.train(model) #print "making url:%s from model: %s a negative case." %(neg_url, key) ''' for url in language['letter_c']: concept.positive(url, "letter_c") for neg_url in language['letter_a']: concept.negative(neg_url, "letter_c") for neg_url in language['letter_b']: concept.negative(neg_url, "letter_c") for neg_url in language['applause']: