xs_lt_n6 = []
ys_lt_n6 = []
# 4) Fit Model
with open(args.data, "r") as f:
file_lines = f.readlines()
for line in file_lines:
#print( "reading " + str( line ) )
split = line.split("\n")[0].split(",")
my_assert_equals_thrower("split.length", len(split), 2)
try:
cpp_structs = jack_mouse_test.read_mouse_data(split[0], split[1])
source_input = cpp_structs[0]
ray_input = cpp_structs[1]
output = cpp_structs[2]
#source_input, ray_input, output = generate_data_from_files( line, False )
except AssertError:
continue
predictions = model.predict(x=[source_input, ray_input])
my_assert_equals_thrower("len( predictions )", len(predictions),
len(output))
for i in range(0, len(predictions)):
'''
denorm_val=output[ i ][ 0 ]
norm_val = denorm_val
if norm_val > 1:
norm_val = norm_val**0.75
for x in range(0, len(output_no_resid)):
my_assert_equals_thrower("len(output_no_resid[x])",
len(output_no_resid[x]), 1)
val = output_no_resid[x][0]
#Stunt large values
if (val > 1):
val = val**0.75
#subtract mean of -2:
val += 2.0
#divide by span of 3:
val /= 3.0
return source_input_no_resid, ray_input_no_resid, output_no_resid
t0 = time.time()
cpp_structs = jack_mouse_test.read_mouse_data(input_file_path,
output_file_path)
t1 = time.time()
py_res_input, py_ray_input, py_output = generate_data_from_files(
input_file_path, output_file_path, False)
t2 = time.time()
if (len(cpp_structs) < 3):
print("CPP found an exception")
exit(cpp_structs[0])
cpp_res_input = cpp_structs[0]
cpp_ray_input = cpp_structs[1]
cpp_output = cpp_structs[2]
#print( cpp_output.shape )
#print( py_output.shape )
#exit( 0 )