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Perceptron.py
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Perceptron.py
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import numpy as np
import matplotlib.pyplot as plt
from LossFunction import LossFunction
import random
class Perceptron:
def __init__(
self,
inputs_training,
outputs_training,
nb_iter,
loss_func,
weights=None,
) -> None:
self.inputs_training = inputs_training
self.outputs_training = outputs_training
self.nb_iter = nb_iter
self.training_factor = 0.01
self.bias = 0
self.nb_feature = len(self.inputs_training[0])
if weights:
self.weights = weights
else:
self.weights = [self.bias] + [0 for i in range(self.nb_feature)]
self.loss_func = loss_func
self.activation_function = LossFunction.loss_methods_switch_process()[
self.loss_func
]
self.nb_inputs = len(self.inputs_training)
self.E = []
def format_input_bias(self, input, value) -> list:
return [value] + input
def train(self, display_loss=True) -> None:
for k in range(self.nb_iter):
output_list = []
for i in range(self.nb_inputs):
sum_val = 0
inputs_tmp = np.array(
self.format_input_bias(self.inputs_training[i], 1)
)
sum_val = np.sum(np.multiply(inputs_tmp, self.weights))
output = self.activation_function(sum_val)
output_list.append(output)
for j in range(self.nb_feature + 1):
self.weights[j] += (
self.training_factor
* (self.outputs_training[i] - output)
* inputs_tmp[j]
)
error = self.compute_error(self.outputs_training, output_list)
self.E.append(error)
# if display_loss and k % (self.nb_iter / 100) == 0:
if display_loss and self._compute_iter_display_error(k):
print(f"Pourcentage d'erreur ({k}) : {error} -> {round(error*100, 2)}%")
def _compute_iter_display_error(self, iter) -> bool:
if iter % (10 ** (max(0, len(str(self.nb_iter)) - 2))) == 0:
return True
else:
return False
def compute_error(self, target, output) -> float:
sum_tmp = 0
for i in range(self.nb_inputs):
sum_tmp += (output[i] - target[i]) ** 2
return (1 / self.nb_inputs) * sum_tmp
def display_mean_squared_loss_graph(self) -> None:
plt.plot(range(self.nb_iter), self.E)
plt.xlabel("Itération")
plt.ylabel("Pourcentage d'erreur")
plt.title(f"Taux d'erreur avec la fonction d'activation {self.loss_func}")
plt.show()
def guess(self, input) -> float:
inputs_tmp = np.array(self.format_input_bias(input, 1))
sum_val = np.sum(np.multiply(inputs_tmp, self.weights))
return self.activation_function(sum_val)