cnnregressionmodel.java

化学图形处理软件

        }
        if (key.equals("Wts")) {
            if (!(obj instanceof Double[])) {
                throw new QSARModelException("The class of the 'Wts' object must be Double[]");
            }
        }
        if (key.equals("mask")) {
            if (!(obj instanceof Boolean[])) {
                throw new QSARModelException("The class of the 'mask' object must be Boolean[]");
            }
        }
        if (key.equals("linout") ||
                key.equals("entropy") ||
                key.equals("softmax") ||
                key.equals("censored") ||
                key.equals("skip") ||
                key.equals("Hess") ||
                key.equals("trace")) {
            if (!(obj instanceof Boolean)) {
                throw new QSARModelException("The class of the 'trace|skip|Hess|linout|entropy|softmax|censored' object must be Boolean");
            }
        }
        if (key.equals("rang") ||
                key.equals("decay") ||
                key.equals("abstol") ||
                key.equals("reltol")) {
            if (!(obj instanceof Double)) {
                throw new QSARModelException("The class of the 'reltol|abstol|decay|rang' object must be Double");
            }
        }
        if (key.equals("maxit") ||
                key.equals("MaxNWts")) {
            if (!(obj instanceof Integer)) {
                throw new QSARModelException("The class of the 'maxit|MaxNWts' object must be Integer");
            }
        }

        if (key.equals("newdata")) {
            if (!(obj instanceof Double[][])) {
                throw new QSARModelException("The class of the 'newdata' object must be Double[][]");
            }
        }
        params.put(key, obj);
    }

    /**
     * Fits a CNN regression model.
     * <p/>
     * This method calls the R function to fit a CNN regression model
     * to the specified dependent and independent variables. If an error
     * occurs in the R session, an exception is thrown.
     * <p/>
     * Note that, this method should be called prior to calling the various get
     * methods to obtain information regarding the fit.
     */
    public void build() throws QSARModelException {
        Double[][] x;
        Double[][] y;
        x = (Double[][]) this.params.get("x");
        y = (Double[][]) this.params.get("y");
        if (x.length != y.length)
            throw new QSARModelException("Number of observations does not match number of rows in the design matrix");
        if (nvar == 0) nvar = x[0].length;

        // lets build the model
        String paramVarName = loadParametersIntoRSession();
        String cmd = "buildCNN(\"" + getModelName() + "\", " + paramVarName + ")";
        REXP ret = rengine.eval(cmd);
        if (ret == null) {
            CNNRegressionModel.logger.debug("Error in buildCNN");
            throw new QSARModelException("Error in buildCNN");
        }

        // remove the parameter list
        rengine.eval("rm(" + paramVarName + ")");

        // save the model object on the Java side
        modelObject = ret.asList();
    }

    /**
     * Uses a fitted model to predict the response for new observations.
     * <p/>
     * This function uses a previously fitted model to obtain predicted values
     * for a new set of observations. If the model has not been fitted prior to this
     * call an exception will be thrown. Use <code>setParameters</code>
     * to set the values of the independent variable for the new observations and the
     * interval type.
     *
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model has not been built prior to a call
     *          to this method. Also if the number of independent variables specified for prediction
     *          is not the same as specified during model building
     */
    public void predict() throws QSARModelException {

        if (modelObject == null)
            throw new QSARModelException("Before calling predict() you must fit the model using build()");

        Double[][] newx = (Double[][]) params.get("newdata");
        if (newx[0].length != nvar) {
            throw new QSARModelException("Number of independent variables used for prediction must match those used for fitting");
        }

        String pn = loadParametersIntoRSession();
        REXP ret = rengine.eval("predicCNN(\"" + getModelName() + "\", " + pn + ")");
        if (ret == null) throw new QSARModelException("Error occured in prediction");

        // remove the parameter list
        rengine.eval("rm(" + pn + ")");

        modelPredict = ret.asDoubleMatrix();
    }

    /**
     * Get the matrix of predicted values obtained from <code>predict.nnet<code>.
     *
     * @return The result of the prediction.
     */
    public double[][] getPredictions() {
        return modelPredict;
    }

    /**
     * Returns an <code>RList</code> object summarizing the nnet regression model.
     * <p/>
     * The return object can be queried via the <code>RList</code> methods to extract the
     * required components.
     *
     * @return A summary for the nnet regression model
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model has not been built prior to a call
     *          to this method
     */
    public RList summary() throws QSARModelException {
        if (modelObject == null)
            throw new QSARModelException("Before calling summary() you must fit the model using build()");

        REXP ret = rengine.eval("summary(" + getModelName() + ")");
        if (ret == null) {
            logger.debug("Error in summary()");
            throw new QSARModelException("Error in summary()");
        }
        return ret.asList();
    }


    /**
     * Loads a <code>'nnet'</code> object from disk in to the current session.
     *
     * @param fileName The disk file containing the model
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model being loaded is not a <code>'nnet'</code> model
     *          object  or the file does not exist
     */
    public void loadModel(String fileName) throws QSARModelException {
        File f = new File(fileName);
        if (!f.exists()) throw new QSARModelException(fileName + " does not exist");

        rengine.assign("tmpFileName", fileName);
        REXP ret = rengine.eval("loadModel(tmpFileName)");
        if (ret == null) throw new QSARModelException("Model could not be loaded");

        String name = ret.asList().at("name").asString();
        if (!isOfClass(name, "nnet")) {
            removeObject(name);
            throw new QSARModelException("Loaded object was not of class \'nnet\'");
        }

        modelObject = ret.asList().at("model").asList();
        setModelName(name);
        nvar = (int) getN()[0];
        noutput = (int) getN()[2];
    }

    /**
     * Loads a  <code>'nnet'</code> object from a serialized string into the current session.
     *
     * @param serializedModel A String containing the serialized version of the model
     * @param modelName       A String indicating the name of the model in the R session
     * @throws org.openscience.cdk.qsar.model.QSARModelException
     *          if the model being loaded is not a <code>'nnet'</code> model
     *          object
     */
    public void loadModel(String serializedModel, String modelName) throws QSARModelException {
        rengine.assign("tmpSerializedModel", serializedModel);
        rengine.assign("tmpModelName", modelName);
        REXP ret = rengine.eval("unserializeModel(tmpSerializedModel, tmpModelName)");

        if (ret == null) throw new QSARModelException("Model could not be unserialized");

        String name = ret.asList().at("name").asString();
        if (!isOfClass(name, "nnet")) {
            removeObject(name);
            throw new QSARModelException("Loaded object was not of class \'nnet\'");
        }

        modelObject = ret.asList().at("model").asList();
        setModelName(name);
        nvar = (int) getN()[0];
        noutput = (int) getN()[2];
    }

// Autogenerated code: assumes that 'modelObject' is
// a RList object


    /**
     * Gets the <code>censored</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the censored field
     */
    public RBool getCensored() {
        return modelObject.at("censored").asBool();
    }

    /**
     * Gets the <code>conn</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the conn field
     */
    public double[] getConn() {
        return modelObject.at("conn").asDoubleArray();
    }

    /**
     * Gets the <code>decay</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the decay field
     */
    public double getDecay() {
        return modelObject.at("decay").asDouble();
    }

    /**
     * Gets the <code>entropy</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the entropy field
     */
    public RBool getEntropy() {
        return modelObject.at("entropy").asBool();
    }

    /**
     * Gets the <code>fitted.values</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the fitted.values field
     */
    public double[][] getFittedValues() {
        return modelObject.at("fitted.values").asDoubleMatrix();
    }

    /**
     * Gets the <code>n</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the n field
     */
    public double[] getN() {
        return modelObject.at("n").asDoubleArray();
    }

    /**
     * Gets the <code>nconn</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the nconn field
     */
    public double[] getNconn() {
        return modelObject.at("nconn").asDoubleArray();
    }

    /**
     * Gets the <code>nsunits</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the nsunits field
     */
    public double getNsunits() {
        return modelObject.at("nsunits").asDouble();
    }

    /**
     * Gets the <code>nunits</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the nunits field
     */
    public double getNunits() {
        return modelObject.at("nunits").asDouble();
    }

    /**
     * Gets the <code>residuals</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the residuals field
     */
    public double[][] getResiduals() {
        return modelObject.at("residuals").asDoubleMatrix();
    }

    /**
     * Gets the <code>softmax</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the softmax field
     */
    public RBool getSoftmax() {
        return modelObject.at("softmax").asBool();
    }

    /**
     * Gets the <code>value</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the value field
     */
    public double getValue() {
        return modelObject.at("value").asDouble();
    }

    /**
     * Gets the <code>wts</code> field of an <code>'nnet'</code> object.
     *
     * @return The value of the wts field
     */
    public double[] getWts() {
        return modelObject.at("wts").asDoubleArray();
    }


}