commit 68f9a6b1a26361214d84ae73517ce688eefa4779 Author: anon Date: Wed Jun 5 11:52:50 2024 +0200 init diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1397432 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +*.pkl diff --git a/boston_weather_data.csv b/boston_weather_data.csv new file mode 100644 index 0000000..cf11c5f --- /dev/null +++ b/boston_weather_data.csv @@ -0,0 +1,3654 @@ +time,tavg,tmin,tmax,prcp,wdir,wspd,pres +2013-03-01,3.2,1.1,5.0,0.0,342.0,15.1,1002.7 +2013-03-02,3.1,1.1,5.6,0.0,307.0,14.4,1004.2 +2013-03-03,2.6,0.6,6.1,0.0,,14.4,1002.6 +2013-03-04,1.8,-0.6,5.0,0.0,303.0,28.1,1003.0 +2013-03-05,2.9,0.0,6.1,0.0,,16.6,1013.5 +2013-03-06,3.2,1.7,5.0,1.3,58.0,36.7,1018.5 +2013-03-07,0.4,-1.1,2.8,11.7,20.0,38.2,1016.5 +2013-03-08,0.4,-2.2,5.6,23.9,9.0,29.5,1016.3 +2013-03-09,1.8,-0.6,3.3,0.0,35.0,17.6,1024.3 +2013-03-10,,-2.2,2.8,0.0,,12.6, +2013-03-11,4.8,1.7,10.0,0.0,198.0,15.8,1024.2 +2013-03-12,8.2,3.9,11.7,7.4,168.0,18.7,1015.1 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need {multiplication} +import numpy as np +# Pickle is used for object serialization, +# it will allow us to save our already trained models to disk +import pickle +# We will need to deep copy NN inputs to append a bias +# without ruining the sample +from copy import deepcopy + +def get_xor(): + r = [ + {'in': [0, 0], 'out': [0]}, + {'in': [1, 0], 'out': [1]}, + {'in': [0, 1], 'out': [1]}, + {'in': [1, 1], 'out': [0]}, + ] + return r + +# YYY: +# https://www.kaggle.com/datasets/swaroopmeher/boston-weather-2013-2023 +def get_pressure_data(): + import csv + r = [] + with open('boston_weather_data.csv', mode='r') as file: + reader = csv.reader(file) + for row in reader: + r.append(row[7]) + return r + +# Used for transforming a series to I/O batches to the form: +# { in: [a], out: a } +# `.in` is always consulted by our network +# `.out` is used for training only, prediction works without it +def batch_data(l : []): + # 'ino' as in 'arduINO', modelled after their map() + # (`map()˙ already un use by a python builtin used for constructing iterators) + def ino_map(x, old_min, old_max, new_min, new_max): + return ((x - old_min) / (old_max - old_min)) * (new_max - new_min) + new_min + def normalize(x): return ino_map(x / (1000 * 2), 0.48, 0.53, 0.0, 1) + def denormalize(x): return ino_map(x, 0.0, 1, 0.48, 0.53) * (1000 * 2) + # number of elements used for constructing a single batch + BATCH_SIZE = 8 + d = [] + for i in [l[i:i + BATCH_SIZE] for i in range(1, len(l), BATCH_SIZE)]: + try: + h = dict() + h['in'] = [normalize(float(f)) for f in i[:BATCH_SIZE-1]] + h['out'] = normalize(float(i[BATCH_SIZE-1])) + d.append(h) + except Exception as e: pass #print(e) + return {'data': d, 'normalize': normalize, 'denormalize': denormalize} + +# Activation function which could be swapped out arbitrarily +def sigmoid_activation(x): return 1/(1+np.exp(-x)) +# Derivative of sigmoid_activation(x) +# NOTE: the real deactivation would be `sigmoid_activation(x) * (1 - sigmoid_activation(x))` +# however, we will only be passing values which are already activated! +def sigmoid_deactivation(a): return a * (1 - a) + +class NN: + learning_rate = 0 + # Architecture: + # { + # ARCHITECTURE := [3, 4, 4, 1] + # O X O X O \ + # O X O X O - O + # O X O X O / + # \ O X O / + # // This will results in the following weight matrix shapes: + # (4, 3), (4, 4), (4, 1) + # // NOTE: notice how one layer is "missing", + # as we dont need weights for the input layer + # } + architecture = None + # [Layer] + layers = None + # Each layer will be stored as its own object. + # The reason for this is that: + # Storing each neuron individually would keep us from using + # numpy matrix operations on layers. + # While speed is not a top priority here, it would lead to much more + # verbose and confusing code. + # And, due to Python's primitive/reference type differentiation + # we would also have trouble with using for loops. + # Storing layers without a container class would result in a bunch of + # parallel arrays. Now, this could be benefitial for performance + # in a low level environment when interfacing with the GPU directly, + # but we are writting Python... + # Anyways, it should go without saying that parellel arrays are ugly. + class Layer: + def __init__(self, input_size : int, neuron_count : int, normalizer : int): + self.weights = np.random.rand(input_size, neuron_count) + # Normalizing the weights (based on the neuron count) is + # something ive seen in an example. + # Being a hyper-parameter, its hard to say whether it actually + # helps or not. What I can tell is that in simple problems + # {xor} it is counter productive. + self.weights = self.weights / np.sqrt(normalizer) + self.activation = sigmoid_activation + self.deactivation = sigmoid_deactivation + # Most sources save the activations, which is fundemantelly the same + # as what we are doing since the activations of this layer is + # the input of the next. However due to the input layer being "virtual", + # we are left with a choice either way, we either store the activations + # and shoehorn in the initial input or store the inputs and shoehorn + # in the last activation. With such a layer based program architecture + # i find the later more elegant as the last activation is also the overall + # output of the network and having a `prediction` variable does not hurt + # code quality: https://commadot.com/wtf-per-minute/ + self.inputs = None + self.deltas = None # buffer change values as calculated by backtracking + def __str__(self): # for printing our network architecture + r = f" \033[35m{self.weights.shape}\033[0m\n{self.weights}\n" + return r + def calculate_deltas(self, error : np.array): + # Notice how this is not a dot product, + # we actually want element-wise multiplication + # ie. weighting by the error + return error * self.deactivation(self.inputs) + def predict(self): + return self.activation( + np.dot(self.inputs, self.weights) + ) + + def __init__(self, architecture : [], learning_rate : int): + self.architecture = architecture + self.learning_rate = learning_rate + + self.layers = [] + # The neuron count of the previous layer tells us + # the input size, this gets combined with the current + # NOTE: `+ 1` is always the addition of a bias input node + for i in np.arange(0, len(architecture) - 2): + l = self.Layer(architecture[i] + 1, architecture[i+1] + 1, architecture[i]) + self.layers.append(l) + # The last layer does not get a bias + l = self.Layer(architecture[-2] + 1, architecture[-1], architecture[-2]) + self.layers.append(l) + def __str__(self): # also for printing + r = f"\033[1;34mNeural Network @ {id(self)}:\033[0m" + for i, l in enumerate(self.layers): + r += f"\n\033[34m--- Layer {i}:\033[0m\n" + r += str(l) + r += "\n\033[1;34m###\033[0m" + return r + + + # Boring serialization stuff + def save(self): + from datetime import datetime + with open(str(datetime.now()).replace(' ', '=') + ".pkl", 'wb') as f: + pickle.dump(self, f) + @staticmethod + def load(id_ : str): # Constructor from file + with open(id_ + ".pkl", 'rb') as f: + o = pickle.load(f) + return o + + # This internal function is used both by ˙train()˙ and `predict()` + def predict_(self, data : np.ndarray): + self.layers[0].inputs = np.atleast_2d(data) + for previous_layer, current_layer in [(self.layers[li-1], self.layers[li]) for li in range(1, len(self.layers))]: + # NOTE: `.predict()` feeds from ˙.inputs` + #self.layers[li].inputs = self.layers[li-1].predict() + current_layer.inputs = previous_layer.predict() + return self.layers[-1].predict() + + def train(self, data, epochs): + def train_(data : {}): + # We do so called 'online learning' where the weights are adjusted after + # each input (in contrast to calculating the loss for the entire dataset) + prediction = self.predict_(data['in']) + # For every layer we will work out the error in reverse order. + # NOTE: some sources do `prediction - data['out']` which is equivalent, + # as long as we dont confuse the operands + delta_output_sum = -(data['out'] - prediction) + self.layers[-1].deltas = delta_output_sum * self.layers[0].deactivation(prediction) + + # We wish to iterate in reverse order so we create a nifty "alias" + rl = self.layers[::-1] + # For ever consequent layer we utalize the deltas of the previous + for previous_layer, current_layer in [(rl[li], rl[li+1]) for li in range(len(rl)-1)]: + error = np.dot(previous_layer.deltas, previous_layer.weights.T) + current_layer.deltas = previous_layer.calculate_deltas(error) + # We update the weights + for l in self.layers: + l.weights -= self.learning_rate * np.dot(l.inputs.T, l.deltas) + # Unlike the error calculations we use to update the weights, + # this function operates on the while dataset and serves + # as an overview of learning + def loss_function(data : [], targets : []): + targets = np.atleast_2d(targets) + predictions = np.empty_like(targets) + for i, d in enumerate(data): + predictions[0][i] = self.predict_(d) + return .5 * np.sum((predictions - targets) ** 2) + + # Appending input layer bias + data_buffer = deepcopy(data) + for d in data_buffer: d['in'] = np.append(d['in'], 1) + + for epoch in range(epochs): + for d in data_buffer: train_(d) + if epoch % 100 == 0: + #loss = loss_function(d['in'], d['out']) + loss = loss_function([d['in'] for d in data_buffer], [d['out'] for d in data_buffer]) + print(f"[INFO] epoch={epoch}, loss={loss}") + + def predict(self, data : []): + p = np.append(data, 1) + return self.predict_(p) + +# ----- + +def xor(): + data = get_xor() + # Simpler architectures have an easier time learning easier problems, + # requiring less epochs + # Conceptulize it as the model "overthinking" the problem + #n = NN([2, 2, 4, 2, 3, 1], .5) + n = NN([2, 2, 1], .5) + print(n) + + n.train(data, epochs=2000) + print(n) + + for d in data: + out = round(n.predict(d['in'])[0][0], 3) + print(f"In: {d['in']}\nOut: {out}\nActual: {d['out'][0]}\n") + +def pressure(): + samples = batch_data(get_pressure_data()) + + PARTITIONING_INDEX = int(len(samples['data']) * .75) + training_data = samples['data'][:PARTITIONING_INDEX] + checking_data = samples['data'][PARTITIONING_INDEX:] + + # This architecture seems to be doing slightly better than + # something more bloated such as `NN([7, 8, 8, 1], .5)` + # and significantly better than something longer + # such as NN([7, 4, 4, 4, 1], .5) + n = NN([7, 4, 4, 1], .5) + #n = NN.load('2024-06-05=10:56:28.122959') + print(n) + + n.train(training_data, epochs=5000) + print(n) + #n.save() + + # This makes our denormalization valid as a matrix operation + vd = np.vectorize(samples['denormalize']) + for d in checking_data: + out = n.predict(d['in']) + out_actual = vd(round(out[0][0], 3)) + in_actual = vd(d['in']) + actual_actual = vd(d['out']) + print(f"In: {in_actual}\nOut: {out_actual}\nActual: {actual_actual}\n") + +if __name__ == '__main__': + #xor() + pressure()