void TestSetDistributionSoftMax()
{
BayesNet *net = SimpleSoftMaxModel();
if (net->GetGaussianMean("node0")[0].FltValue() != 0.1f)
{
PNL_THROW(pnl::CAlgorithmicException, "node0 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianMean("node1")[0].FltValue() != 0.2f)
{
PNL_THROW(pnl::CAlgorithmicException, "node1 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianMean("node2")[0].FltValue() != 0.3f)
{
PNL_THROW(pnl::CAlgorithmicException, "node2 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node0")[0].FltValue() != 0.9f)
{
PNL_THROW(pnl::CAlgorithmicException, "node0 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node1")[0].FltValue() != 0.8f)
{
PNL_THROW(pnl::CAlgorithmicException, "node1 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node2")[0].FltValue() != 0.7f)
{
PNL_THROW(pnl::CAlgorithmicException, "node2 : Setting or getting gaussian parameters is wrong");
}
if ((net->GetSoftMaxOffset("node5")[0].FltValue(0).fl != 0.1f)||
(net->GetSoftMaxOffset("node5")[0].FltValue(1).fl != 0.1f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
};
TokArr node5= net->GetSoftMaxWeights("node5");
float val0 = node5[0].FltValue(0).fl;
float val1 = node5[0].FltValue(1).fl;
float val2 = node5[0].FltValue(2).fl;
float val3 = node5[0].FltValue(3).fl;
float val4 = node5[0].FltValue(4).fl;
float val5 = node5[0].FltValue(5).fl;
if ((node5[0].FltValue(0).fl != 0.3f)||
(node5[0].FltValue(1).fl != 0.4f)||
(node5[0].FltValue(2).fl != 0.5f)||
(node5[0].FltValue(3).fl != 0.6f)||
(node5[0].FltValue(4).fl != 0.7f)||
(node5[0].FltValue(5).fl != 0.8f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
};
delete net;
std::cout << "TestSetDistributionSoftMax is completed successfully" << std::endl;
}
void TestsPnlHigh::TestGetGaussianMeanCovarWeights()
{
printf("TestGetGaussianMeanCovarWeights\n");
BayesNet *net = SimpleCGM1();
net->SetPGaussian("Cont1", "0.0", "2.5", "1.0 3.0", "Tab0^State0");
net->SetPGaussian("Cont1", "-1.5", "0.75", "0.5 2.5", "Tab0^State1");
if (net->GetGaussianMean("Cont1", "Tab0^State0")[0].FltValue() != 0.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "GetGaussianMean works incorrectly");
};
if (net->GetGaussianMean("Cont1", "Tab0^State1")[0].FltValue() != -1.5f)
{
PNL_THROW(pnl::CAlgorithmicException, "GetGaussianMean works incorrectly");
};
if (net->GetGaussianCovar("Cont1", "Tab0^State0")[0].FltValue() != 2.5f)
{
PNL_THROW(pnl::CAlgorithmicException, "GetGaussianCovar works incorrectly");
};
if (net->GetGaussianCovar("Cont1", "Tab0^State1")[0].FltValue() != 0.75f)
{
PNL_THROW(pnl::CAlgorithmicException, "GetGaussianCovar works incorrectly");
};
if (String(net->GetGaussianWeights("Cont1", "Cont0", "Tab0^State0")[0]) != "1.000000^3.000000")
{
PNL_THROW(pnl::CAlgorithmicException, "GetGaussianCovar works incorrectly");
};
if (String(net->GetGaussianWeights("Cont1", "Cont0", "Tab0^State1")[0]) != "0.500000^2.500000")
{
PNL_THROW(pnl::CAlgorithmicException, "GetGaussianCovar works incorrectly");
};
delete net;
};
void TestSetDistributionSevenNodesModel()
{
BayesNet *net = SevenNodesModel();
if (net->GetGaussianMean("node0")[0].FltValue() != 0.5f)
{
PNL_THROW(pnl::CAlgorithmicException, "node0 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianMean("node1")[0].FltValue() != 0.5f)
{
PNL_THROW(pnl::CAlgorithmicException, "node1 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node0")[0].FltValue() != 1.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "node0 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node1")[0].FltValue() != 1.0f)
{
PNL_THROW(pnl::CAlgorithmicException, "node1 : Setting or getting gaussian parameters is wrong");
}
float val12 = net->GetPTabular("node2")[0].FltValue();
float val22 = net->GetPTabular("node2")[1].FltValue();
if ((net->GetPTabular("node2")[0].FltValue() != 0.7f)||
(net->GetPTabular("node2")[1].FltValue() != 0.3f))
{
PNL_THROW(pnl::CAlgorithmicException, "node2 : Setting or getting tabular parameters is wrong");
};
TokArr off5True = net->GetSoftMaxOffset("node3", "node2^True");
if ((off5True[0].FltValue(0).fl != 0.3f)||
(off5True[0].FltValue(1).fl != 0.5f))
{
PNL_THROW(pnl::CAlgorithmicException, "node3 : Setting or getting softmax parameters is wrong");
};
TokArr off5False = net->GetSoftMaxOffset("node3", "node2^False");
float val1off = off5False[0].FltValue(0).fl;
float val2off = off5False[0].FltValue(1).fl;
if ((off5False[0].FltValue(0).fl != 0.3f)||
(off5False[0].FltValue(1).fl != 0.5f))
{
PNL_THROW(pnl::CAlgorithmicException, "node3 : Setting or getting softmax parameters is wrong");
};
TokArr node5True = net->GetSoftMaxWeights("node3", "node2^True");
if ((node5True[0].FltValue(0).fl != 0.5f)||
(node5True[0].FltValue(1).fl != 0.1f)||
(node5True[0].FltValue(2).fl != 0.5f)||
(node5True[0].FltValue(3).fl != 0.7f))
{
PNL_THROW(pnl::CAlgorithmicException, "node3 : Setting or getting softmax parameters is wrong");
};
TokArr node5False = net->GetSoftMaxWeights("node3", "node2^False");
float val0 = node5False[0].FltValue(0).fl;
float val1 = node5False[0].FltValue(1).fl;
float val2 = node5False[0].FltValue(2).fl;
float val3 = node5False[0].FltValue(3).fl;
if ((node5False[0].FltValue(0).fl != 0.5f)||
(node5False[0].FltValue(1).fl != 0.4f)||
(node5False[0].FltValue(2).fl != 0.5f)||
(node5False[0].FltValue(3).fl != 0.7f))
{
PNL_THROW(pnl::CAlgorithmicException, "node3 : Setting or getting softmax parameters is wrong");
};
float val40 = net->GetPTabular("node4", "node3^False")[0].FltValue();
float val41 = net->GetPTabular("node4", "node3^False")[1].FltValue();
float val42 = net->GetPTabular("node4", "node3^True")[0].FltValue() ;
float val43 = net->GetPTabular("node4", "node3^True")[1].FltValue() ;
if ((net->GetPTabular("node4", "node3^False")[0].FltValue() != 0.7f)||
(net->GetPTabular("node4", "node3^False")[1].FltValue() != 0.3f)||
(net->GetPTabular("node4", "node3^True")[0].FltValue() != 0.2f)||
(net->GetPTabular("node4", "node3^True")[1].FltValue() != 0.8f))
{
PNL_THROW(pnl::CAlgorithmicException, "node4 : Setting or getting tabular parameters is wrong");
};
if ((net->GetGaussianMean("node5", "node3^True")[0].FltValue() != 0.5f)||
(net->GetGaussianMean("node5", "node3^False")[0].FltValue() != 1.0f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
}
if ((net->GetGaussianCovar("node5", "node3^True")[0].FltValue() != 0.5f)||
(net->GetGaussianCovar("node5", "node3^False")[0].FltValue() != 1.0f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
}
TokArr off6True = net->GetSoftMaxOffset("node6", "node4^True");
if ((off6True[0].FltValue(0).fl != 0.1f)||
(off6True[0].FltValue(1).fl != 0.9f))
{
PNL_THROW(pnl::CAlgorithmicException, "node6 : Setting or getting softmax parameters is wrong");
};
TokArr off6False = net->GetSoftMaxOffset("node6", "node4^False");
if ((off6False[0].FltValue(0).fl != 0.7f)||
(off6False[0].FltValue(1).fl != 0.3f))
{
PNL_THROW(pnl::CAlgorithmicException, "node6 : Setting or getting softmax parameters is wrong");
};
TokArr node6True = net->GetSoftMaxWeights("node6", "node4^True");
if ((node6True[0].FltValue(0).fl != 0.8f)||
(node6True[0].FltValue(1).fl != 0.2f))
{
PNL_THROW(pnl::CAlgorithmicException, "node6 : Setting or getting softmax parameters is wrong");
};
TokArr node6False = net->GetSoftMaxWeights("node6", "node4^False");
float val06 = node5False[0].FltValue(0).fl;
float val16 = node5False[0].FltValue(1).fl;
if ((node6False[0].FltValue(0).fl != 0.5f)||
(node6False[0].FltValue(1).fl != 0.9f))
{
PNL_THROW(pnl::CAlgorithmicException, "node6 : Setting or getting softmax parameters is wrong");
};
delete net;
std::cout << "TestSetDistributionSevenNodesModel is completed successfully" << std::endl;
}
void TestSetDistributionCondSoftMax()
{
BayesNet *net = SimpleCondSoftMaxModel();
if (net->GetGaussianMean("node0")[0].FltValue() != 0.1f)
{
PNL_THROW(pnl::CAlgorithmicException, "node0 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianMean("node1")[0].FltValue() != 0.2f)
{
PNL_THROW(pnl::CAlgorithmicException, "node1 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianMean("node2")[0].FltValue() != 0.3f)
{
PNL_THROW(pnl::CAlgorithmicException, "node2 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node0")[0].FltValue() != 0.9f)
{
PNL_THROW(pnl::CAlgorithmicException, "node0 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node1")[0].FltValue() != 0.8f)
{
PNL_THROW(pnl::CAlgorithmicException, "node1 : Setting or getting gaussian parameters is wrong");
}
if (net->GetGaussianCovar("node2")[0].FltValue() != 0.7f)
{
PNL_THROW(pnl::CAlgorithmicException, "node2 : Setting or getting gaussian parameters is wrong");
}
if ((net->GetPTabular("node6")[0].FltValue() != 0.3f)||
(net->GetPTabular("node6")[1].FltValue() != 0.7f)||
(net->GetPTabular("node6")[2].FltValue() != 0.5f)||
(net->GetPTabular("node6")[3].FltValue() != 0.5f))
{
PNL_THROW(pnl::CAlgorithmicException, "node6 : Setting or getting gaussian parameters is wrong");
};
TokArr off5True = net->GetSoftMaxOffset("node5", "node3^True");
if ((off5True[0].FltValue(0).fl != 0.1f)||
(off5True[0].FltValue(1).fl != 0.1f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
};
TokArr off5False = net->GetSoftMaxOffset("node5", "node3^False");
float val1off = off5False[0].FltValue(0).fl;
float val2off = off5False[0].FltValue(1).fl;
if ((off5False[0].FltValue(0).fl != 0.21f)||
(off5False[0].FltValue(1).fl != 0.21f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
};
TokArr node5True = net->GetSoftMaxWeights("node5", "node3^True");
if ((node5True[0].FltValue(0).fl != 0.3f)||
(node5True[0].FltValue(1).fl != 0.4f)||
(node5True[0].FltValue(2).fl != 0.5f)||
(node5True[0].FltValue(3).fl != 0.6f)||
(node5True[0].FltValue(4).fl != 0.7f)||
(node5True[0].FltValue(5).fl != 0.8f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
};
TokArr node5False = net->GetSoftMaxWeights("node5", "node3^False");
float val0 = node5False[0].FltValue(0).fl;
float val1 = node5False[0].FltValue(1).fl;
float val2 = node5False[0].FltValue(2).fl;
float val3 = node5False[0].FltValue(3).fl;
float val4 = node5False[0].FltValue(4).fl;
float val5 = node5False[0].FltValue(5).fl;
if ((node5False[0].FltValue(0).fl != 0.23f)||
(node5False[0].FltValue(1).fl != 0.24f)||
(node5False[0].FltValue(2).fl != 0.25f)||
(node5False[0].FltValue(3).fl != 0.26f)||
(node5False[0].FltValue(4).fl != 0.27f)||
(node5False[0].FltValue(5).fl != 0.28f))
{
PNL_THROW(pnl::CAlgorithmicException, "node5 : Setting or getting gaussian parameters is wrong");
};
delete net;
std::cout << "TestSetDistributionCondSoftMax is completed successfully" << std::endl;
}
