def run_test_data(self, epoch_data: NNData, one_hot=0) -> float:
"""
Helper function. Runs testing pool example data through the input
layer, and then compares the predicted values to the expected label
values. Returns the root mean square error of the test run.
Args:
epoch_data (NNData): Object containing data set.
one_hot: one hot encoding data
Returns:
rmse (float): Root mean squared error of the test run.
"""
error = 0
size = epoch_data.get_number_samples(NNData.Set.TEST)
for _ in range(size):
single_data = epoch_data.get_one_item(NNData.Set.TEST)
self.send_data_to_inputs(single_data[0])
self._visualize_x.append(single_data[0])
self._visualize_y_nw.append(self.collect_outputs(one_hot))
self._visualize_y.append(single_data[1])
self.print_testing_data(single_data[0],
self.collect_outputs(one_hot),
single_data[1])
error += self.calculate_error(single_data[1])
return self.calculate_rmse(size, error)
def run_train_data(self, epoch_data: NNData, verbosity, epoch):
"""
Helper function. Sends input data to the input layer, calculates
the error from the output nodes against the expected values,
and then sends the expected values to the output nodes to backprop
through the network.
Args:
epoch_data (NNData): Object containing the data set.
verbosity: how often to print epoch details
epoch: current epoch
Returns:
The root mean squared error of the epoch. (float)
"""
error = 0
size = epoch_data.get_number_samples(NNData.Set.TRAIN)
outputs = []
labels = []
for _ in range(size):
single_data = epoch_data.get_one_item(NNData.Set.TRAIN)
self.send_data_to_inputs(single_data[0])
error += self.calculate_error(single_data[1])
self.send_data_to_outputs(single_data[1])
outputs.append(self.collect_outputs())
labels.append(single_data[1])
self.print_training_data(verbosity, epoch, outputs, labels,
self.calculate_rmse(size, error))
def test(self, data_set: NNData, order=NNData.Order.RANDOM, one_hot=0):
"""
This function runs the testing data through the neural network.
Args:
data_set (NNData): Object containing the testing data
order: Order selection to randomize data or keep sequential
one_hot: one hot encoding label data
"""
if data_set.y is None:
raise EmptySetException
else:
print("\nTesting:")
data_set.prime_data(NNData.Set.TEST, order)
rmse = self.run_test_data(data_set, one_hot)
print("Final RMSE:", rmse)
def run_XOR():
network = FFBPNetwork(2, 1)
network.add_hidden_layer(3)
XORx = [[0, 0], [1, 0], [0, 1], [1, 1]]
XORy = [[0], [1], [1], [0]]
data = NNData(XORx, XORy, 100)
network.train(data, 1001)
network.test(data, one_hot=1)
def train(self,
data_set: NNData,
epochs: int = 1000,
verbosity=2,
order=NNData.Order.RANDOM):
"""
Runs the training data through the neural network for the given
number of epochs.
Args:
data_set (NNData): Object containing the training data.
epochs (int): number of epochs to train the data.
verbosity: Level of print output desired
order: Randomize, or keep data sequential.
"""
if data_set.x is None:
raise EmptySetException
else:
print("\nTraining:")
for epoch in range(epochs):
data_set.prime_data(NNData.Set.TRAIN, order)
self.run_train_data(data_set, verbosity, epoch)
def nn_data_decoder(dct):
""" Takes in a json file and outputs a NNData object
Args:
dct: json object to decode
Returns:
NNData object with all attributes initialized"""
if "__NNData__" not in dct:
return dct
data = dct['__NNData__']
new_data = NNData(data['x'], data['y'], data['train_percentage'])
new_data.train_indices = data['train_indices']
new_data.test_indices = data['test_indices']
test_pool = data['test_pool']
train_pool = data['train_pool']
new_data.test_pool = deque(test_pool['__deque__'])
new_data.train_pool = deque(train_pool['__deque__'])
new_data.train_data = (new_data.train_indices, new_data.train_pool)
new_data.test_data = (new_data.test_indices, new_data.test_pool)
return new_data
def run_iris():
network = FFBPNetwork(4, 3)
network.add_hidden_layer(6)
Iris_X = [[5.1, 3.5, 1.4, 0.2], [4.9, 3, 1.4,
0.2], [4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2], [5, 3.6, 1.4,
0.2], [5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3], [5, 3.4, 1.5,
0.2], [4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1], [5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2], [4.8, 3, 1.4, 0.1], [4.3, 3, 1.1, 0.1],
[5.8, 4, 1.2, 0.2], [5.7, 4.4, 1.5,
0.4], [5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3], [5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3], [5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4], [4.6, 3.6, 1,
0.2], [5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2], [5, 3, 1.6, 0.2], [5, 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2], [5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2], [4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4], [5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2], [4.9, 3.1, 1.5,
0.1], [5, 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2], [4.9, 3.1, 1.5, 0.1],
[4.4, 3, 1.3, 0.2], [5.1, 3.4, 1.5, 0.2], [5, 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3], [4.4, 3.2, 1.3, 0.2],
[5, 3.5, 1.6, 0.6], [5.1, 3.8, 1.9, 0.4], [4.8, 3, 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2], [4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2], [5, 3.3, 1.4, 0.2], [7, 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5], [6.9, 3.1, 4.9,
1.5], [5.5, 2.3, 4, 1.3],
[6.5, 2.8, 4.6, 1.5], [5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6], [4.9, 2.4, 3.3,
1], [6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4], [5, 2, 3.5, 1], [5.9, 3, 4.2, 1.5],
[6, 2.2, 4, 1], [6.1, 2.9, 4.7, 1.4], [5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4], [5.6, 3, 4.5, 1.5], [5.8, 2.7, 4.1, 1],
[6.2, 2.2, 4.5, 1.5], [5.6, 2.5, 3.9,
1.1], [5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4, 1.3], [6.3, 2.5, 4.9,
1.5], [6.1, 2.8, 4.7,
1.2], [6.4, 2.9, 4.3, 1.3],
[6.6, 3, 4.4, 1.4], [6.8, 2.8, 4.8, 1.4], [6.7, 3, 5, 1.7],
[6, 2.9, 4.5, 1.5], [5.7, 2.6, 3.5, 1], [5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1], [5.8, 2.7, 3.9, 1.2], [6, 2.7, 5.1, 1.6],
[5.4, 3, 4.5, 1.5], [6, 3.4, 4.5, 1.6], [6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3], [5.6, 3, 4.1, 1.3], [5.5, 2.5, 4, 1.3],
[5.5, 2.6, 4.4, 1.2], [6.1, 3, 4.6, 1.4], [5.8, 2.6, 4, 1.2],
[5, 2.3, 3.3, 1], [5.6, 2.7, 4.2, 1.3], [5.7, 3, 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3], [6.2, 2.9, 4.3,
1.3], [5.1, 2.5, 3,
1.1], [5.7, 2.8, 4.1, 1.3],
[6.3, 3.3, 6, 2.5], [5.8, 2.7, 5.1, 1.9], [7.1, 3, 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8], [6.5, 3, 5.8, 2.2], [7.6, 3, 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7], [7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8], [7.2, 3.6, 6.1,
2.5], [6.5, 3.2, 5.1, 2],
[6.4, 2.7, 5.3, 1.9], [6.8, 3, 5.5, 2.1], [5.7, 2.5, 5, 2],
[5.8, 2.8, 5.1, 2.4], [6.4, 3.2, 5.3,
2.3], [6.5, 3, 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2], [7.7, 2.6, 6.9, 2.3], [6, 2.2, 5, 1.5],
[6.9, 3.2, 5.7, 2.3], [5.6, 2.8, 4.9, 2], [7.7, 2.8, 6.7, 2],
[6.3, 2.7, 4.9, 1.8], [6.7, 3.3, 5.7,
2.1], [7.2, 3.2, 6, 1.8],
[6.2, 2.8, 4.8, 1.8], [6.1, 3, 4.9,
1.8], [6.4, 2.8, 5.6, 2.1],
[7.2, 3, 5.8, 1.6], [7.4, 2.8, 6.1, 1.9], [7.9, 3.8, 6.4, 2],
[6.4, 2.8, 5.6, 2.2], [6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4], [7.7, 3, 6.1,
2.3], [6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8], [6, 3, 4.8, 1.8], [6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4], [6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9], [6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5], [6.7, 3, 5.2, 2.3], [6.3, 2.5, 5, 1.9],
[6.5, 3, 5.2, 2], [6.2, 3.4, 5.4, 2.3], [5.9, 3, 5.1, 1.8]]
Iris_Y = [[
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
1,
0,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
1,
0,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
], [
0,
0,
1,
]]
data = NNData(Iris_X, Iris_Y, 45)
network.train(data, 1001, verbosity=0)
network.test(data, one_hot=1)
def run_sin():
network = FFBPNetwork(1, 1)
network.add_hidden_layer(25)
sin_X = [[0], [0.01], [0.02], [0.03], [0.04], [0.05], [0.06], [0.07],
[0.08], [0.09], [0.1], [0.11], [0.12], [0.13], [0.14], [0.15],
[0.16], [0.17], [0.18], [0.19], [0.2], [0.21], [0.22], [0.23],
[0.24], [0.25], [0.26], [0.27], [0.28], [0.29], [0.3], [0.31],
[0.32], [0.33], [0.34], [0.35], [0.36], [0.37], [0.38
], [0.39],
[0.4], [0.41], [0.42], [0.43], [0.44], [0.45], [0.46], [0.47],
[0.48], [0.49], [0.5], [0.51], [0.52], [0.53], [0.54], [0.55],
[0.56], [0.57], [0.58], [0.59], [0.6], [0.61], [0.62], [0.63],
[0.64], [0.65], [0.66], [0.67], [0.68], [0.69], [0.7], [0.71],
[0.72], [0.73], [0.74], [0.75], [0.76], [0.77], [0.78],
[0.79], [0.8], [0.81], [0.82], [0.83], [0.84], [0.85], [0.86],
[0.87], [0.88], [0.89], [0.9], [0.91], [0.92], [0.93], [0.94],
[0.95], [0.96], [0.97], [0.98], [0.99], [1], [1.01], [1.02],
[1.03], [1.04], [1.05], [1.06], [1.07], [1.08], [1.09], [1.1],
[1.11], [1.12], [1.13], [1.14], [1.15], [1.16], [1.17
], [1.18],
[1.19], [1.2], [1.21], [1.22], [1.23], [1.24], [1.25], [1.26],
[1.27], [1.28], [1.29], [1.3], [1.31], [1.32], [1.33], [1.34],
[1.35], [1.36], [1.37], [1.38], [1.39], [1.4], [1.41], [1.42],
[1.43], [1.44], [1.45], [1.46], [1.47], [1.48], [1.49], [1.5],
[1.51], [1.52], [1.53], [1.54], [1.55], [1.56], [1.57]]
sin_Y = [[0], [0.00999983333416666], [0.0199986666933331],
[0.0299955002024957], [0.0399893341866342],
[0.0499791692706783], [0.0599640064794446],
[0.0699428473375328], [0.0799146939691727],
[0.089878549198011], [0.0998334166468282],
[0.109778300837175], [0.119712207288919], [0.129634142619695],
[0.139543114644236], [0.149438132473599], [0.159318206614246],
[0.169182349066996], [0.179029573425824], [0.188858894976501],
[0.198669330795061], [0.2084598998461], [0.218229623080869],
[0.227977523535188], [0.237702626427135], [0.247403959254523],
[0.257080551892155], [0.266731436688831], [0.276355648564114],
[0.285952225104836], [0.29552020666134], [0.305058636443443],
[0.314566560616118], [0.324043028394868], [0.333487092140814],
[0.342897807455451], [0.35227423327509], [0.361615431964962],
[0.370920469412983], [0.380188415123161], [0.389418342308651],
[0.398609327984423], [0.40776045305957], [0.416870802429211],
[0.425939465066], [0.43496553411123], [0.44394810696552],
[0.452886285379068], [0.461779175541483], [0.470625888171158],
[0.479425538604203], [0.488177246882907], [0.496880137843737],
[0.505533341204847], [0.514135991653113], [0.522687228930659],
[0.531186197920883], [0.539632048733969], [0.548023936791874],
[0.556361022912784], [0.564642473395035], [0.572867460100481],
[0.581035160537305], [0.58914475794227], [0.597195441362392],
[0.60518640573604], [0.613116851973434], [0.62098598703656],
[0.628793024018469], [0.636537182221968], [0.644217687237691],
[0.651833771021537], [0.659384671971473], [0.666869635003698],
[0.674287911628145], [0.681638760023334], [0.688921445110551],
[0.696135238627357], [0.70327941920041], [0.710353272417608],
[0.717356090899523], [0.724287174370143], [0.731145829726896],
[0.737931371109963], [0.744643119970859], [0.751280405140293],
[0.757842562895277], [0.764328937025505], [0.770738878898969],
[0.777071747526824], [0.783326909627483], [0.78950373968995],
[0.795601620036366], [0.801619940883777], [0.807558100405114],
[0.813415504789374], [0.819191568300998], [0.82488571333845],
[0.83049737049197], [0.836025978600521], [0.841470984807897],
[0.846831844618015], [0.852108021949363], [0.857298989188603],
[0.862404227243338], [0.867423225594017], [0.872355482344986],
[0.877200504274682], [0.881957806884948], [0.886626914449487],
[0.891207360061435], [0.895698685680048], [0.900100442176505],
[0.904412189378826], [0.908633496115883], [0.912763940260521],
[0.916803108771767], [0.920750597736136], [0.92460601240802],
[0.928368967249167], [0.932039085967226], [0.935616001553386],
[0.939099356319068], [0.942488801931697], [0.945783999449539],
[0.948984619355586], [0.952090341590516], [0.955100855584692],
[0.958015860289225], [0.960835064206073], [0.963558185417193],
[0.966184951612734], [0.968715100118265], [0.971148377921045],
[0.973484541695319], [0.975723357826659], [0.977864602435316],
[0.979908061398614], [0.98185353037236], [0.983700814811277],
[0.98544972998846], [0.98710010101385], [0.98865176285172],
[0.990104560337178], [0.991458348191686], [0.992712991037588],
[0.993868363411645], [0.994924349777581], [0.99588084453764],
[0.996737752043143], [0.997494986604054], [0.998152472497548],
[0.998710143975583], [0.999167945271476], [0.999525830605479],
[0.999783764189357], [0.999941720229966], [0.999999682931835]]
data = NNData(sin_X, sin_Y, 45)
network.train(data, 1, verbosity=1)
network.test(data)
network.plot_output_comparison()
def main():
"""Main Unit test for module"""
xor_x = [[0, 0], [1, 0], [0, 1], [1, 1]]
xor_y = [[0], [1], [1], [0]]
xor_data = NNData(xor_x, xor_y, 90)
xor_data_encoded = json.dumps(xor_data, cls=NNDataJson)
xor_data_decoded = json.loads(xor_data_encoded,
object_hook=nn_data_decoder)
network = FFBPNetwork(2, 1)
network.add_hidden_layer(3)
network.train(xor_data_decoded, 1001)
network.test(xor_data_decoded, one_hot=1)
sin_json = """{"__NNData__": {"train_percentage": 10,
"x": [[0.0], [0.01], [0.02], [0.03], [0.04],
[0.05], [0.06], [0.07], [0.08], [0.09],
[0.1], [0.11], [0.12], [0.13], [0.14],
[0.15], [0.16], [0.17], [0.18], [0.19],
[0.2], [0.21], [0.22], [0.23], [0.24],
[0.25], [0.26], [0.27], [0.28], [0.29],
[0.3], [0.31], [0.32], [0.33], [0.34],
[0.35], [0.36], [0.37], [0.38], [0.39],
[0.4], [0.41], [0.42], [0.43], [0.44],
[0.45], [0.46], [0.47], [0.48], [0.49],
[0.5], [0.51], [0.52], [0.53], [0.54],
[0.55], [0.56], [0.57], [0.58], [0.59],
[0.6], [0.61], [0.62], [0.63], [0.64],
[0.65], [0.66], [0.67], [0.68], [0.69],
[0.7], [0.71], [0.72], [0.73], [0.74],
[0.75], [0.76], [0.77], [0.78], [0.79],
[0.8], [0.81], [0.82], [0.83], [0.84],
[0.85], [0.86], [0.87], [0.88], [0.89],
[0.9], [0.91], [0.92], [0.93], [0.94],
[0.95], [0.96], [0.97], [0.98], [0.99],
[1.0], [1.01], [1.02], [1.03], [1.04],
[1.05], [1.06], [1.07], [1.08], [1.09],
[1.1], [1.11], [1.12], [1.13], [1.14],
[1.15], [1.16], [1.17], [1.18], [1.19],
[1.2], [1.21], [1.22], [1.23], [1.24],
[1.25], [1.26], [1.27], [1.28], [1.29],
[1.3], [1.31], [1.32], [1.33], [1.34],
[1.35], [1.36], [1.37], [1.38], [1.39],
[1.4], [1.41], [1.42], [1.43], [1.44],
[1.45], [1.46], [1.47], [1.48], [1.49],
[1.5], [1.51], [1.52], [1.53], [1.54],
[1.55], [1.56], [1.57], [1.58], [1.59],
[1.6], [1.61], [1.62], [1.63], [1.64],
[1.65], [1.66], [1.67], [1.68], [1.69],
[1.7], [1.71], [1.72], [1.73], [1.74],
[1.75], [1.76], [1.77], [1.78], [1.79],
[1.8], [1.81], [1.82], [1.83], [1.84],
[1.85], [1.86], [1.87], [1.88], [1.89],
[1.9], [1.91], [1.92], [1.93], [1.94],
[1.95], [1.96], [1.97], [1.98], [1.99],
[2.0], [2.01], [2.02], [2.03], [2.04],
[2.05], [2.06], [2.07], [2.08], [2.09],
[2.1], [2.11], [2.12], [2.13], [2.14],
[2.15], [2.16], [2.17], [2.18], [2.19],
[2.2], [2.21], [2.22], [2.23], [2.24],
[2.25], [2.26], [2.27], [2.28], [2.29],
[2.3], [2.31], [2.32], [2.33], [2.34],
[2.35], [2.36], [2.37], [2.38], [2.39],
[2.4], [2.41], [2.42], [2.43], [2.44],
[2.45], [2.46], [2.47], [2.48], [2.49],
[2.5], [2.51], [2.52], [2.53], [2.54],
[2.55], [2.56], [2.57], [2.58], [2.59],
[2.6], [2.61], [2.62], [2.63], [2.64],
[2.65], [2.66], [2.67], [2.68], [2.69],
[2.7], [2.71], [2.72], [2.73], [2.74],
[2.75], [2.76], [2.77], [2.78], [2.79],
[2.8], [2.81], [2.82], [2.83], [2.84],
[2.85], [2.86], [2.87], [2.88], [2.89],
[2.9], [2.91], [2.92], [2.93], [2.94],
[2.95], [2.96], [2.97], [2.98], [2.99],
[3.0], [3.01], [3.02], [3.03], [3.04],
[3.05], [3.06], [3.07], [3.08], [3.09],
[3.1], [3.11], [3.12], [3.13], [3.14]],
"y": [[0.0], [0.00999983333416666],
[0.0199986666933331],
[0.0299955002024957],
[0.0399893341866342],
[0.0499791692706783],
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[0.0699428473375328],
[0.0799146939691727],
[0.089878549198011],
[0.0998334166468282],
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[0.129634142619695],
[0.139543114644236],
[0.149438132473599],
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[0.179029573425824],
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[0.218229623080869],
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[0.237702626427135],
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[0.266731436688831],
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[0.717356090899523],
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[0.764328937025505],
[0.770738878898969],
[0.777071747526824],
[0.783326909627483],
[0.78950373968995],
[0.795601620036366],
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[0.807558100405114],
[0.813415504789374],
[0.819191568300998],
[0.82488571333845], [0.83049737049197],
[0.836025978600521],
[0.841470984807897],
[0.846831844618015],
[0.852108021949363],
[0.857298989188603],
[0.862404227243338],
[0.867423225594017],
[0.872355482344986],
[0.877200504274682],
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[0.891207360061435],
[0.895698685680048],
[0.900100442176505],
[0.904412189378826],
[0.908633496115883],
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[0.916803108771767],
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[0.92460601240802],
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"train_indices": [8, 13, 44, 48, 58, 67, 69,
70, 71, 75, 77, 83, 102,
112, 127, 130, 143, 164,
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286, 301, 308],
"test_indices": [0, 1, 2, 3, 4, 5, 6, 7, 9,
10, 11, 12, 14, 15, 16, 17,
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25, 26, 27, 28, 29, 30, 31,
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"test_pool": {
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231, 232, 233, 234, 235,
236, 237, 238, 239, 241,
242, 244, 245, 246, 247,
248, 249, 250, 251, 252,
253, 254, 255, 256, 258,
259, 261, 262, 263, 264,
265, 266, 267, 268, 269,
270, 271, 272, 273, 274,
275, 276, 277, 278, 279,
280, 281, 282, 283, 284,
285, 287, 288, 289, 290,
291, 292, 293, 294, 295,
296, 297, 298, 299, 300,
302, 303, 304, 305, 306,
307, 309, 310, 311, 312,
313, 314]}}}
"""
network = FFBPNetwork(1, 1)
sin_encoded = json.loads(sin_json, object_hook=nn_data_decoder)
network.train(sin_encoded, 3001, verbosity=0)
network.test(sin_encoded)