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:])