def toSparseTensor(X):
X = X.tocoo()
indices = np.vstack((X.row, X.col)).T # Transpose
values = X.data
shape = X.shape
return SparseTensor(indices, values, shape)
def generate_edge_features(self, x, a):
send = a.indices[:, 0]
receive = a.indices[:, 1]
if self.forward == True:
forwards = tf.gather(x[:, 3], send) <= tf.gather(x[:, 3], receive)
send = tf.cast(send[forwards], tf.int64)
receive = tf.cast(receive[forwards], tf.int64)
a = SparseTensor(indices=tf.stack([send, receive], axis=1),
values=tf.ones(tf.shape(send), dtype=tf.float32),
dense_shape=tf.cast(tf.shape(a), tf.int64))
diff_x = tf.subtract(tf.gather(x, receive), tf.gather(x, send))
dists = tf.sqrt(tf.reduce_sum(tf.square(diff_x[:, :3]), axis=1))
vects = tf.math.divide_no_nan(diff_x[:, :3],
tf.expand_dims(dists, axis=-1))
e = tf.concat(
[diff_x[:, 3:], tf.expand_dims(dists, -1), vects], axis=1)
return a, e
def generate_edge_features(self, x, a):
send = a.indices[:, 0]
receive = a.indices[:, 1]
if self.forward == True: #could maybe be improved
forwards = tf.gather(x[:, 3], send) <= tf.gather(x[:, 3], receive)
send = tf.cast(send[forwards], tf.int64)
receive = tf.cast(receive[forwards], tf.int64)
a = SparseTensor(indices = tf.stack([send, receive], axis = 1), values = tf.ones(tf.shape(send), dtype = tf.float32), dense_shape = tf.cast(tf.shape(a), tf.int64))
##distance vectors
diff_x = tf.subtract(tf.gather(x, receive), tf.gather(x, send))
dists = tf.sqrt(
tf.reduce_sum(
tf.square(
diff_x[:, :3]
), axis = 1
))
vects = tf.math.divide_no_nan(diff_x[:, :3], tf.expand_dims(dists, axis = -1))
if self.edgetype==0:
e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects], axis = 1)
if self.edgetype==1:
## SRT, could make this is a mask
prod_x = tf.math.multiply(tf.gather(x, receive), tf.gather(x, send))
srt = prod_x[:,5]
e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects, tf.expand_dims(srt, -1)], axis = 1)
if self.edgetype==2:
st=2699 # time scale, database specific
c=tf.constant(0.000299792458) #speed of light in km pr nanosec
speed = tf.math.divide_no_nan(dists, st*diff_x[:,3]) #could add fudge factor to account for ice c lower than vacuum c
speed = tf.math.greater_equal(speed, c)
speed = tf.cast(tf.where(speed, 0,1), tf.float32)
e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects, tf.expand_dims(speed,-1)], axis = 1)
if self.edgetype==3:
#srt comm?
prod_x = tf.math.multiply(tf.gather(x, receive), tf.gather(x, send))
srt = prod_x[:,5]
#higher than c?
st=2699 # time scale, database specific
c=tf.constant(0.000299792458) #speed of light in km pr nanosec
speed = tf.math.divide_no_nan(dists, st*diff_x[:,3]) #could add fudge factor to account for ice c lower than vacuum c
speed = tf.math.greater_equal(speed, c)
speed = tf.cast(tf.where(speed, 0,1), tf.float32)
e = tf.concat([diff_x[:, 3:], tf.expand_dims(dists, -1), vects, tf.expand_dims(srt, -1), tf.expand_dims(speed,-1)], axis = 1)
return a, e