Hands-On Industrial Internet of Things

Building the surrogate model with logistic regression and Scikit-Learn

The first step is to build our simulated data. The following code generates the training dataset. The dataset is made up of two columns, wind and power, an array, X_train, and one column array, y_train.

The y_train contains the value 0 when the relation between wind and power is an anomaly and is marked as 1 using an additional variable:

X_train=[]y_train=[]# regular dataX_train.extend( [[x ,wind_turbine_model(x)] for x in range(0,30)] )y_train.extend( [1 for x in range(0,30)] )# anomaly datafor x in range(15,30): X_train.extend([[x, 50 + x*random.random()]]) y_train.extend([0])

To generated the regular data, we used the wind_turbine_model() function. To generate anomaly ...