Esempio n. 1
0
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;

}
Esempio n. 2
0
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;

}
Esempio n. 3
0
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;
}