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Model.py
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Model.py
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from sklearn import metrics
from Knn import Knn
import os
import numpy as np
class EmgModel:
def __init__(self, labels):
self.model = Knn(k=5)
self.labels = labels
self.all_data = list()
self.all_target = list()
for pose in labels:
label = labels[pose]
data = self.load_data(label)
target = [pose] * len(data)
self.all_data += data
self.all_target += target
def run(self):
# 训练数据和测试数据分离
train_data = self.all_data[0::2]
train_target = self.all_target[0::2]
predict_data = self.all_data[1::2]
predict_target = self.all_target[1::2]
# 模型训练
self.model.fit(train_data, train_target)
# 模型预测
y = self.model.predict(predict_data)
num = 0
for i in range(len(y)):
if predict_target[i] == y[i]:
num += 1
cm = metrics.confusion_matrix(y, predict_target)
print("混淆矩阵")
print(cm)
print("分类报告")
cr = metrics.classification_report(y, predict_target)
print(cr)
print("准确率:%s %%" % ((num / len(predict_target)) * 100))
def load_data(self, label):
parent_dir = "data/%s" % label
res = []
for file in os.listdir(parent_dir):
filename = "%s/%s" % (parent_dir, file)
f = open(filename, 'r')
data = []
for line in f.readlines():
row = line.replace('\n', '').replace("[", "").replace("]", "").split(",")
row = [float(x) for x in row]
data.append(row)
f.close()
data = np.array(data).T
res.append(data)
return res
if __name__ == "__main__":
pose_data = {1: "left", 2: "right", 3: "rest", 4: "open"}
model = EmgModel(pose_data)
model.run()