Research on Partial Least Squares Method Based on Deep Confidence Network in Traditional Chinese Medicine
Algorithm 1
Partial least squares algorithm based on DBN.
Input: original sample data set Dataset (D);
Output: DBN-PLS equation.
Step 1 preprocesses the data to obtain
Step 2 deep belief nets (DBN)
Initialize model parameters
Layersize = 1
hidden_layers_sizes = [4, 4, 4]
While Whether the number of RBM layer size reaches the precision condition
while Whether the number layersize of each layer of neurons reaches the accuracy condition
for z in layersize
Calculating the probability that hidden layer neurons are activated
Take Gibbs sampling to extract a sample:
Reconstruct the visual layer with is used to calculate the probability of activated hidden layer neurons
Take Gibbs sampling to extract a sample:
is used to calculate the probability of activated hidden layer neurons
Update weight
Step 3 The characteristics extracted by independent variables and dependent variables are calculated by the DBN model, respectively. ,, put the PLS outer model into multiple linear regression, and find the DBN-PLS equation.
Denormalized reduction of coefficients Y multiple linear regression equations for X
