def map_reward(self, state, electrode_weights=[], Fs=250, L=1000):
with tf.name_scope("Generate_Reward"):
reward = Utils.extract_frequency_bins(state, self.mLOWALPHA,
self.mHIGHALPHA, 1)
reward_flat = tf.reshape(reward, [-1], name="flatten_fft")
reward_summed = tf.reduce_sum(reward_flat, name="alphapow_summing")
return reward_summed
def test_2d_array(self):
with self.test_session() as sess:
expected_val = np.int64([[500, 0, 0, 0, 0], [0, 0, 0, 0, 500]])
Sin1 = [np.sin(2 * np.pi * (x / 250)) for x in range(1000)]
Sin20 = [np.sin(2 * np.pi * (x / 250) * 20) for x in range(1000)]
a = tf.constant([Sin1, Sin20], dtype=tf.complex64)
b = Utils.extract_frequency_bins(a, 0, 25, 5)
c = tf.cast(b, dtype=tf.int64)
#Write graph to file
writer = tf.summary.FileWriter(".\\Logs\\", sess.graph)
#Actual check
self.assertAllEqual(c.eval(), expected_val)
def run(self, _buf):
with tf.name_scope("RUN_BandPower"):
tf.assert_rank(_buf['data'],
2,
message="JCR: Input must be rank 2 tensor")
asserts = [
tf.assert_equal(
tf.shape(_buf['data'])[0],
self.mNCHAN,
message="JCR: Input Dim-0 must equal number of channels")
]
with tf.control_dependencies(asserts):
hz_per_point = self.mFS / self.mSIGLEN
points_per_bin = np.maximum(self.mBIN_WIDTH // hz_per_point,
1.0)
total_number_bins = int(self.mSIGLEN // points_per_bin)
total_number_points = int(total_number_bins * points_per_bin)
f_start = 0
f_end = total_number_points * self.mFS / self.mSIGLEN
dout = Utils.extract_frequency_bins(_buf['data'], f_start,
f_end, total_number_bins,
self.mSIGLEN, self.mFS)
asserts = [
tf.assert_equal(tf.shape(dout)[0],
self.mNCHAN,
message="JCR: Input/output shape mismatch",
name='FinalCheck')
]
with tf.control_dependencies(asserts):
return {
'data': dout,
'summaries': _buf['summaries'],
#pass dout shape to updates so that the assert gets evaluated
'updates': _buf['updates'] + [tf.shape(dout)[0]]
}