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main.py
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from classifier.multiClassPerceptron import MulticlassPerceptron as MulticlassPerceptron
from sklearn.linear_model import Perceptron
from sklearn import datasets
from sklearn.neural_network import MLPClassifier
from process_data.house import generate_data
from process_data.position import GenerateData
import random
if __name__ == "__main__":
# -----------------iris data--------------------
iris = datasets.load_iris()
X = iris.data[:, :5]
y = iris.target
iris_class = iris.target_names
o = [i for i in range(len(X))]
random.shuffle(o)
ratio = 0.7
ol = int(len(o)*ratio)
X_train = [X[i] for i in o[:ol]]
X_test = [X[i] for i in o[ol:]]
y_train = [y[i] for i in o[:ol]]
y_test = [y[i] for i in o[ol:]]
errors = 0
print("=======skit-learn==========")
model = Perceptron(max_iter=50)
model.fit(X_train, y_train)
predicted = model.predict(X_test)
for i in range(len(predicted)):
if predicted[i] != y_test[i]:
errors += 1
print("accuracy:", str(1 - errors*1.0/len(predicted)))
print("=========My Model==========")
y = [iris_class[i] for i in y]
y_train = [y[i] for i in o[:ol]]
y_test = [y[i] for i in o[ol:]]
iris_model = MulticlassPerceptron(epoch=50, early_stopping=True)
iris_model.fit(X_train, y_train, iris_class)
iris_model.model_analysis(X_test, y_test)
# --------------------end---------------------
# ------------------------plot iris data----------------------
# x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
# y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
# plt.figure(2, figsize=(8, 6))
# plt.clf()
#
# # Plot the training points
#
# plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,
# edgecolor='k')
#
# plt.xlabel('Sepal length')
# plt.ylabel('Sepal width')
#
# plt.xlim(x_min, x_max)
# plt.ylim(y_min, y_max)
# plt.xticks(())
# plt.yticks(())
#
# plt.savefig('result.png')
# ---------------------------------------------------------
# ----------------house data-------------------
m, o, n, p = generate_data()
print(len(n))
print(len(p))
ratio = 0.7
i, j, ii, jj = [], [], [], []
random.shuffle(o)
lens = int(len(o)*ratio)
X_train = [t[1] for t in o[:lens]]
y_train = [t[0] for t in o[:lens]]
X_test = [t[1] for t in o[lens:]]
y_test = [t[0] for t in o[lens:]]
print("=========My Model=============")
model = MulticlassPerceptron(epoch=50, early_stopping=True)
model.fit(X_train, y_train, m)
model.model_analysis(X_test, y_test)
print("=======skit-learn==========")
model = Perceptron(max_iter=50)
model.fit(X_train, y_train)
predicted = model.predict(X_test)
errors = 0
for i in range(len(predicted)):
if predicted[i] != y_test[i]:
errors += 1
print("accuracy:", str(1 - errors*1.0/len(predicted)))
# # ---------------------------------------------
#
#
# # -----------------position data------------------
# m, mac_list, X_train, y_train, X_test, y_test = GenerateData()
# m, X_train, y_train = GenerateData()
# model = MulticlassPerceptron(epoch=100, early_stopping=True)
# y_train = [str(k) for k in y_train]
# print(X_train)
# print(y_train)
# print("========My model===========")
# model.fit(X_train, y_train, m)
# model.model_analysis(X_train, y_train)
#
# print("=======skit-learn==========")
# model = Perceptron(max_iter=50)
# model.fit(X_train, y_train)
# predicted = model.predict(X_train)
# errors = 0
#
# for i in range(len(predicted)):
# if predicted[i] != y_train[i]:
# errors += 1
# print("accuracy:", str(1 - errors * 1.0 / len(predicted)))
# # ------------------------------------------------
#
#