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example.py
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example.py
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import numpy as np
from nn_dax import NNDAX
if __name__=="__main__":
np.random.seed(0)
#Name of your input measures in power bi
input_features=["[x1]","[x2]","[x3]"]
#Define weights,biases and activation functions of pre-trained neural network
#List of dicts
n_hidden=4
layers=[]
layers.append({"W":np.random.randn(len(input_features),n_hidden), "b":np.random.randn(1,n_hidden),"activation":"tanh"})
layers.append({"W":np.random.randn(n_hidden,1), "b":np.random.randn(1,1),"activation":"sigmoid"})
#Create NNDAX object
nnd=NNDAX(input_features,layers)
#Generate dax code
print(nnd.generate_dax())
#Run a input sample through the network.
#Can double check this value with calculated values from PowerBI to ensure everything works correctly.
print( "-----------")
print( "TEST")
print( "input=[1,2,3]:")
print( "output=" + str(nnd.calculate(np.array([[1,2,3]]))))