def test_segment_contains(self):
sg1 = segment((0,0),(-8,0))
self.assertTrue(sg1.contains((-2,0)))
self.assertFalse(sg1.contains((-2,1)))
sg1 = segment((0,0),(0,10))
self.assertFalse(sg1.contains((0,-20)))
self.assertTrue(sg1.contains((0,1)))
def make_system(self): # called first
self.size = self.options['size']
self.resolution = self.options['resolution']
lx, ly = self.size[0], self.size[1]
cell_size_max = max(lx / self.resolution[0], ly / self.resolution[1])
if (self.min_mean_free_path != None
and cell_size_max > self.min_mean_free_path):
message = "Max cell size is {:e} m and higher than the min mean free path which is {:e}.".format(
cell_size_max, self.min_mean_free_path)
warnings.warn(message)
self.size = [lx, ly, self.options['lz']]
walls = [segment(Point(0,0),Point(0,ly)), segment(Point(0,0),Point(lx,0)), \
segment(Point(lx,0),Point(lx,ly)), segment(Point(0,ly),Point(lx,ly))]
return walls
def start():
files = []
files.extend([
os.path.join(".saved/tags/blocks", f)
for f in os.listdir(".saved/tags/blocks")
if os.path.isfile(os.path.join(".saved/tags/blocks", f))
])
files.extend([
os.path.join(".saved/tags/entity_types", f)
for f in os.listdir(".saved/tags/entity_types")
if os.path.isfile(os.path.join(".saved/tags/entity_types", f))
])
files.extend([
os.path.join(".saved/tags/items", f)
for f in os.listdir(".saved/tags/items")
if os.path.isfile(os.path.join(".saved/tags/items", f))
])
files.extend([
os.path.join(".saved/tags/liquids", f)
for f in os.listdir(".saved/tags/liquids")
if os.path.isfile(os.path.join(".saved/tags/liquids", f))
])
for f in files:
sp = f.split("/")
if main.segment("minecraft:", 0, sp[-1]):
split = f.split(":")
if not split[1].endswith(".txt"):
os.rename(f, f"{'/'.join(sp[:-1])}/minecraft_{split[1]}.txt")
else:
os.rename(f, f"{'/'.join(sp[:-1])}/minecraft_{split[1]}")
def test_segment_intersection(self):
sg1 = segment([0,0], [2,0])
sg2 = segment([1,0], [1,2])
self.assertEqual(sg1.intersect(sg2), (1,0))
self.assertEqual(sg2.intersect(sg1), (1,0))
sg3 = segment((10,0),(20,0))
self.assertIsNone(sg1.intersect(sg3))
sg1 = segment((0,0),(8,0))
sg2 = segment((6,3),(6,7))
self.assertIsNone(sg1.intersect(sg2))
self.assertIsNone(sg2.intersect(sg1))
sg1 = segment((0,0),(-8,0))
sg2 = segment((-2,1),(-2,-7))
self.assertEqual(sg1.intersect(sg2), (-2,0))
self.assertEqual(sg2.intersect(sg1), (-2,0))
# loss_tmp, acc_tmp = sess.run([loss_op, accuracy], feed_dict={X: batch_x, X_length: batch_x_length,
# Y: batch_y})
# print(acc_tmp)
# pdb.set_trace()
res = {} # key: three keys for each word in tweet
for j in range(batch_x_length[i]):
res[j, 'state'] = state_list[0][0][j]
res[j, 'word'] = vocab_by_value[batch_x[i][j]]
res[j, 'predicted'] = predicted[j]
result.append(res)
return previous_valid_acc, result, batch_y
embed_size = 20
full, vocab_dict, embedding_matrix = segment('train.csv', size=embed_size)
vocab_by_value = {}
for key in vocab_dict:
vocab_by_value[vocab_dict[key]] = key
num_hidden = 3
dropout = 1 #this is actually 1-dropout...
previous_valid_acc, result, batch_y = train_and_visualize(num_hidden, dropout)
def plot_neuron(neuron_num):
for i in range(len(result)): #each res is a tweet
words = [] #contains words and prediction so far
acts = []