def get_training_data(self): waves = waveUtils.generateRandomFrequencies(self.input_s, num=self.num_freqs, freq_min=self.min_freq, freq_max=self.max_freq, num_to_combine=4) all_data = [] all_originals = [] for i in range(len(waves)): for j in range(i+1, len(waves)): all_data.append(waves[i]+waves[j]) all_originals.append([waves[i], waves[j]]) num_samples = len(all_data) perm = np.random.permutation(num_samples) all_data = np.asarray(all_data)[perm] all_originals = np.asarray(all_originals)[perm] self._val_data = all_data[:self.validation_samples] self._val_originals = all_originals[:self.validation_samples] return all_data[self.validation_samples:], all_originals[self.validation_samples:]
def get_training_data(self): waves = waveUtils.generateRandomFrequencies(self.input_s, self.num_freqs, self.min_freq, self.max_freq) all_data = [] all_originals = [] for i in range(len(waves)): for j in range(i, len(waves)): all_data.append(waves[i] + waves[j]) all_originals.append([waves[i], waves[j]]) self._val_data = np.asarray(all_data[:self.validation_samples]) self._val_originals = np.asarray( all_originals[:self.validation_samples]) return np.asarray(all_data[self.validation_samples:]), np.asarray( all_originals[self.validation_samples:])