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model.py
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model.py
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# Ładujemy moduły
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
import pickle
# Ładujemy moduł iris i dane z niego
iris=load_iris()
df = pd.DataFrame(data = np.c_[iris['data'], iris['target']],
columns=iris['feature_names']+['target'])
# Tworzymy zbiory X i y
X = iris.data[:100]
y = iris.target[:100]
# Tworzymy sieć
class Perceptron:
def __init__(self, eta=0.01, n_iter=10):
self.eta = eta
self.n_iter = n_iter
def fit(self, X, y):
self.w_ = np.zeros(1 + X.shape[1])
self.errors_ = []
for _ in range(self.n_iter):
errors = 0
for xi, target in zip(X,y):
update=self.eta*(target-self.predict(xi))
self.w_[1:] += update *xi
self.w_[0] += update
errors += int(update != 0.0)
self.errors_.append(errors)
return self
def net_input(self, X):
X = np.squeeze(np.asarray(X))
return np.dot(X, self.w_[1:]) + self.w_[0]
def predict(self, X):
return np.where(self.net_input(X) >= 0, 1, -1)
# Trenujemy model
perceptron = Perceptron()
perceptron.fit(X, y)
# Zapisujemy go
perceptron_file = open('model.pkl', 'wb')
pickle.dump(perceptron, perceptron_file)
perceptron_file.close()