识别已知事件的Python机器学习算法 [英] Python Machine Learning Algorithm to Recognize Known Events

问题描述

我有两组数据.这些数据是电路中两点A和B的记录电压.电压A是电路的主要组件，电压B是子电路. B中的每个正电压都被认为是(1)B事件，而(2)被认为是A的合成.我已经包括了发生B电压事件4,4,0,0,4,4的样本数据.真实的训练数据集将具有更多可用数据.

I have two sets of data. These data are logged voltages of two points A and B in a circuit. Voltage A is the main component of the circuit, and B is a sub-circuit. Every positive voltage in B is (1) considered a B event and (2) known to be composite of A. I have included sample data where there is a B voltage event, 4,4,0,0,4,4. A real training data set would have many more available data.

如何训练Python机器学习算法来识别仅给出A数据的B事件?

How can I train a Python machine learning algorithm to recognize B events given only A data?

示例数据:

V(A), V(B)
0, 0
2, 0
5, 4
3, 4
1, 0
3, 4
4, 4
1, 0
0, 0
2, 0
5, 0
7, 0
2, 0
5, 4
9, 4
3, 0
5, 0
4, 4
6, 4
3, 0
2, 0

推荐答案

一个想法:

from sklearn.ensemble import RandomForestClassifier

n = 5
X = [df.A.iloc[i:i+n] for i in df.index[:-n+1]] 
labels = (df.B > 0)[n-1:]

model = RandomForestClassifier()
model.fit(X, labels)
model.predict(X)

这是什么，它将先前的n观测值用作'B'值的预测变量.在这个小的数据集上，它可以达到0.94的精度(可能会过拟合).

What this does is, it takes the previous n observations as predictors for the 'B' value. On this small data set it achieves 0.94 accuracy (could be overfitting).

更正了一个小的对齐错误.

Corrected a small alignment error.

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