33     samples, responses = a[:,1:], a[:,0]
34     return samples, responses
52     def unroll_responses(self, responses):  argument
53         sample_n = len(responses)
55         resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
63     def train(self, samples, responses):  argument
68 …      self.model.train(samples, cv2.CV_ROW_SAMPLE, responses, varType = var_types, params = params)
78     def train(self, samples, responses):  argument
79         self.model.train(samples, responses)
90     def train(self, samples, responses):  argument
93         new_responses = self.unroll_responses(responses)
110     def train(self, samples, responses):  argument
114         self.model.train(samples, responses, params = params)
124     def train(self, samples, responses):  argument
126         new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
159     samples, responses = load_base(args['--data'])  variable
170         model.train(samples[:train_n], responses[:train_n])
173     train_rate = np.mean(model.predict(samples[:train_n]) == responses[:train_n])
174     test_rate  = np.mean(model.predict(samples[train_n:]) == responses[train_n:])