org.nd4j.shade.jackson.databind.JsonNode Java Examples

The following examples show how to use org.nd4j.shade.jackson.databind.JsonNode. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: IntegerDistributionDeserializer.java    From deeplearning4j with Apache License 2.0 6 votes vote down vote up
@Override
public IntegerDistribution deserialize(JsonParser p, DeserializationContext ctxt) throws IOException {
    JsonNode node = p.getCodec().readTree(p);
    String simpleName = node.get("distribution").asText();

    switch (simpleName) {
        case "BinomialDistribution":
            return new BinomialDistribution(node.get("trials").asInt(), node.get("p").asDouble());
        case "GeometricDistribution":
            return new GeometricDistribution(node.get("p").asDouble());
        case "HypergeometricDistribution":
            return new HypergeometricDistribution(node.get("populationSize").asInt(),
                            node.get("numberOfSuccesses").asInt(), node.get("sampleSize").asInt());
        case "PascalDistribution":
            return new PascalDistribution(node.get("r").asInt(), node.get("p").asDouble());
        case "PoissonDistribution":
            return new PoissonDistribution(node.get("p").asDouble());
        case "UniformIntegerDistribution":
            return new UniformIntegerDistribution(node.get("lower").asInt(), node.get("upper").asInt());
        case "ZipfDistribution":
            return new ZipfDistribution(node.get("numElements").asInt(), node.get("exponent").asDouble());
        default:
            throw new RuntimeException("Unknown or not supported distribution: " + simpleName);
    }
}
 
Example #2
Source File: DataJsonDeserializer.java    From konduit-serving with Apache License 2.0 6 votes vote down vote up
protected Point deserializePoint(JsonNode n2){
    String label = null;
    Double prob = null;
    if(n2.has("label") ){
        label = n2.get("label").textValue();
    } else if(n2.has("@label")){
        label = n2.get("@label").textValue();
    }

    if(n2.has("probability")){
        prob = n2.get("probability").doubleValue();
    } else if(n2.has("@probability")){
        prob = n2.get("@probability").doubleValue();
    }

    ArrayNode n3 = (ArrayNode) n2.get(Data.RESERVED_KEY_POINT_COORDS);
    double[] coords = new double[n3.size()];
    for (int i = 0; i < n3.size(); i++) {
        coords[i] = n3.get(i).asDouble();
    }
    return Point.create(coords, label, prob);
}
 
Example #3
Source File: DataFormatDeserializer.java    From deeplearning4j with Apache License 2.0 6 votes vote down vote up
@Override
public DataFormat deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
    JsonNode node = jp.getCodec().readTree(jp);
    String text = node.textValue();
    switch (text){
        case "NCHW":
            return CNN2DFormat.NCHW;
        case "NHWC":
            return CNN2DFormat.NHWC;
        case "NCW":
            return RNNFormat.NCW;
        case "NWC":
            return RNNFormat.NWC;
        default:
            return null;
    }
}
 
Example #4
Source File: LegacyIntArrayDeserializer.java    From deeplearning4j with Apache License 2.0 6 votes vote down vote up
@Override
public int[] deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
    JsonNode n = jp.getCodec().readTree(jp);
    if(n.isArray()){
        ArrayNode an = (ArrayNode)n;
        int size = an.size();
        int[] out = new int[size];
        for( int i=0; i<size; i++ ){
            out[i] = an.get(i).asInt();
        }
        return out;
    } else if(n.isNumber()){
        int v = n.asInt();
        return new int[]{v,v};
    } else {
        throw new IllegalStateException("Could not deserialize value: " + n);
    }
}
 
Example #5
Source File: DateTimeFieldTypeDeserializer.java    From DataVec with Apache License 2.0 5 votes vote down vote up
@Override
public DateTimeFieldType deserialize(JsonParser jsonParser, DeserializationContext deserializationContext)
                throws IOException, JsonProcessingException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    String value = node.get("fieldType").textValue();
    return map.get(value);
}
 
Example #6
Source File: RowVectorDeserializer.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@Override
public INDArray deserialize(JsonParser jsonParser, DeserializationContext deserializationContext)
                throws IOException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    if (node == null)
        return null;
    int size = node.size();
    double[] d = new double[size];
    for (int i = 0; i < size; i++) {
        d[i] = node.get(i).asDouble();
    }

    return Nd4j.create(d);
}
 
Example #7
Source File: ConfusionMatrixDeserializer.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@Override
public ConfusionMatrix<Integer> deserialize(JsonParser jp, DeserializationContext ctxt)
                throws IOException, JsonProcessingException {
    JsonNode n = jp.getCodec().readTree(jp);

    //Get class names/labels
    ArrayNode classesNode = (ArrayNode) n.get("classes");
    List<Integer> classes = new ArrayList<>();
    for (JsonNode cn : classesNode) {
        classes.add(cn.asInt());
    }

    ConfusionMatrix<Integer> cm = new ConfusionMatrix<>(classes);

    ObjectNode matrix = (ObjectNode) n.get("matrix");
    Iterator<Map.Entry<String, JsonNode>> matrixIter = matrix.fields();
    while (matrixIter.hasNext()) {
        Map.Entry<String, JsonNode> e = matrixIter.next();

        int actualClass = Integer.parseInt(e.getKey());
        ArrayNode an = (ArrayNode) e.getValue();

        ArrayNode innerMultiSetKey = (ArrayNode) an.get(0);
        ArrayNode innerMultiSetCount = (ArrayNode) an.get(1);

        Iterator<JsonNode> iterKey = innerMultiSetKey.iterator();
        Iterator<JsonNode> iterCnt = innerMultiSetCount.iterator();
        while (iterKey.hasNext()) {
            int predictedClass = iterKey.next().asInt();
            int count = iterCnt.next().asInt();

            cm.add(actualClass, predictedClass, count);
        }
    }

    return cm;
}
 
Example #8
Source File: DateTimeFieldTypeDeserializer.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@Override
public DateTimeFieldType deserialize(JsonParser jsonParser, DeserializationContext deserializationContext)
                throws IOException, JsonProcessingException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    String value = node.get("fieldType").textValue();
    return map.get(value);
}
 
Example #9
Source File: BoundingBoxesDeserializer.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@Override
public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
    JsonNode node = jp.getCodec().readTree(jp);
    if(node.has("dataBuffer")){
        //Must be legacy format serialization
        JsonNode arr = node.get("dataBuffer");
        int rank = node.get("rankField").asInt();
        int numElements = node.get("numElements").asInt();
        int offset = node.get("offsetField").asInt();
        JsonNode shape = node.get("shapeField");
        JsonNode stride = node.get("strideField");
        int[] shapeArr = new int[rank];
        int[] strideArr = new int[rank];
        DataBuffer buff = Nd4j.createBuffer(numElements);
        for (int i = 0; i < numElements; i++) {
            buff.put(i, arr.get(i).asDouble());
        }

        String ordering = node.get("orderingField").asText();
        for (int i = 0; i < rank; i++) {
            shapeArr[i] = shape.get(i).asInt();
            strideArr[i] = stride.get(i).asInt();
        }

        return Nd4j.create(buff, shapeArr, strideArr, offset, ordering.charAt(0));
    }
    //Standard/new format
    return new NDArrayTextDeSerializer().deserialize(node);
}
 
Example #10
Source File: TDigestDeserializer.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@Override
public TDigest deserialize(JsonParser jp, DeserializationContext d) throws IOException, JsonProcessingException {
    JsonNode node = (JsonNode)jp.getCodec().readTree(jp);
    String field = node.get("digest").asText();
    Base64 b = new Base64();
    byte[] bytes = b.decode(field);
    try(ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(bytes))){
        return (TDigest) ois.readObject();
    } catch (Exception e){
        throw new RuntimeException("Error deserializing TDigest object from JSON", e);
    }
}
 
Example #11
Source File: NDArrayDeSerializer.java    From nd4j with Apache License 2.0 5 votes vote down vote up
@Override
public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException {
    JsonNode node = jp.getCodec().readTree(jp);
    String field = node.get("array").asText();
    INDArray ret = Nd4jBase64.fromBase64(field.toString());
    return ret;

}
 
Example #12
Source File: VectorDeSerializer.java    From nd4j with Apache License 2.0 5 votes vote down vote up
@Override
public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException {
    JsonNode node = jp.getCodec().readTree(jp);
    JsonNode arr = node.get("dataBuffer");
    int rank = node.get("rankField").asInt();
    int numElements = node.get("numElements").asInt();
    int offset = node.get("offsetField").asInt();
    JsonNode shape = node.get("shapeField");
    JsonNode stride = node.get("strideField");
    String type = node.get("typeField").asText();
    int[] realShape = new int[rank];
    int[] realStride = new int[rank];
    DataBuffer buff = Nd4j.createBuffer(numElements);
    for (int i = 0; i < numElements; i++) {
        buff.put(i, arr.get(i).asDouble());
    }

    String ordering = node.get("orderingField").asText();
    for (int i = 0; i < rank; i++) {
        realShape[i] = shape.get(i).asInt();
        realStride[i] = stride.get(i).asInt();
    }

    INDArray ret = type.equals("real") ? Nd4j.create(buff, realShape, realStride, offset, ordering.charAt(0))
                    : Nd4j.createComplex(buff, realShape, realStride, offset, ordering.charAt(0));
    return ret;
}
 
Example #13
Source File: RowVectorDeserializer.java    From nd4j with Apache License 2.0 5 votes vote down vote up
@Override
public INDArray deserialize(JsonParser jsonParser, DeserializationContext deserializationContext)
                throws IOException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    if (node == null)
        return null;
    int size = node.size();
    double[] d = new double[size];
    for (int i = 0; i < size; i++) {
        d[i] = node.get(i).asDouble();
    }

    return Nd4j.create(d);
}
 
Example #14
Source File: TDigestDeserializer.java    From DataVec with Apache License 2.0 5 votes vote down vote up
@Override
public TDigest deserialize(JsonParser jp, DeserializationContext d) throws IOException, JsonProcessingException {
    JsonNode node = (JsonNode)jp.getCodec().readTree(jp);
    String field = node.get("digest").asText();
    Base64 b = new Base64();
    byte[] bytes = b.decode(field);
    try(ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(bytes))){
        return (TDigest) ois.readObject();
    } catch (Exception e){
        throw new RuntimeException("Error deserializing TDigest object from JSON", e);
    }
}
 
Example #15
Source File: PointDeserializer.java    From konduit-serving with Apache License 2.0 5 votes vote down vote up
@Override
public Point deserialize(JsonParser jp, DeserializationContext dc) throws IOException, JsonProcessingException {
    JsonNode n = jp.getCodec().readTree(jp);
    String lbl = n.has("label") ? n.get("label").textValue() : null;
    Double prob = n.has("probability") ? n.get("probability").doubleValue() : null;
    ArrayNode cn = (ArrayNode)n.get("coords");
    double[] pts = new double[cn.size()];
    for( int i=0; i<pts.length; i++ ){
        pts[i] = cn.get(i).doubleValue();
    }
    return new NDPoint(pts, lbl, prob);
}
 
Example #16
Source File: DataJsonDeserializer.java    From konduit-serving with Apache License 2.0 5 votes vote down vote up
protected ValueType nodeType(JsonNode n){
    if (n.isTextual()) {                     //String
        return ValueType.STRING;
    } else if (n.isDouble()) {               //Double
        return ValueType.DOUBLE;
    } else if (n.isInt() || n.isLong()) {   //Long
        return ValueType.INT64;
    } else if (n.isBoolean()) {              //Boolean
        return ValueType.BOOLEAN;
    } else if (n.isArray()){
        return ValueType.LIST;
    } else if (n.isObject()) {
        //Could be: Bytes, image, NDArray, BoundingBox, Point or Data
        if (n.has(Data.RESERVED_KEY_BYTES_BASE64)) {
            return ValueType.BYTES;
        } else if (n.has(Data.RESERVED_KEY_BYTES_ARRAY)) {
            return ValueType.BYTES;
        } else if (n.has(Data.RESERVED_KEY_NDARRAY_TYPE)) {
            //NDArray
            return ValueType.NDARRAY;
        } else if (n.has(Data.RESERVED_KEY_IMAGE_DATA)) {
            //Image
            return ValueType.IMAGE;
        } else if(n.has(Data.RESERVED_KEY_BB_CX) || n.has(Data.RESERVED_KEY_BB_X1)){
            return ValueType.BOUNDING_BOX;
        } else if(n.has(Data.RESERVED_KEY_POINT_COORDS)){
            return ValueType.POINT;
        } else {
            //Must be data
            return ValueType.DATA;
        }
    } else {
        throw new UnsupportedOperationException("Type not yet implemented");
    }
}
 
Example #17
Source File: StorageLevelDeserializer.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up
@Override
public StorageLevel deserialize(JsonParser jsonParser, DeserializationContext deserializationContext)
                throws IOException, JsonProcessingException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    String value = node.textValue();
    if (value == null || "null".equals(value)) {
        return null;
    }
    return StorageLevel.fromString(value);
}
 
Example #18
Source File: DataJsonDeserializer.java    From konduit-serving with Apache License 2.0 5 votes vote down vote up
public static BoundingBox deserializeBB(JsonNode n2){
    String label = null;
    Double prob = null;
    if(n2.has("label") ){
        label = n2.get("label").textValue();
    } else if(n2.has("@label")){
        label = n2.get("@label").textValue();
    }

    if(n2.has("probability")){
        prob = n2.get("probability").doubleValue();
    } else if(n2.has("@probability")){
        prob = n2.get("@probability").doubleValue();
    }


    if(n2.has(Data.RESERVED_KEY_BB_CX)){
        double cx = n2.get(Data.RESERVED_KEY_BB_CX).doubleValue();
        double cy = n2.get(Data.RESERVED_KEY_BB_CY).doubleValue();
        double h = n2.get(Data.RESERVED_KEY_BB_H).doubleValue();
        double w = n2.get(Data.RESERVED_KEY_BB_W).doubleValue();
        return BoundingBox.create(cx, cy, h, w, label, prob);
    } else {
        double x1 = n2.get(Data.RESERVED_KEY_BB_X1).doubleValue();
        double x2 = n2.get(Data.RESERVED_KEY_BB_X2).doubleValue();
        double y1 = n2.get(Data.RESERVED_KEY_BB_Y1).doubleValue();
        double y2 = n2.get(Data.RESERVED_KEY_BB_Y2).doubleValue();
        return BoundingBox.createXY(x1, x2, y1, y2, label, prob);
    }
}
 
Example #19
Source File: DataJsonDeserializer.java    From konduit-serving with Apache License 2.0 5 votes vote down vote up
protected Image deserializeImage(JsonNode n2){
    String format = n2.get(Data.RESERVED_KEY_IMAGE_FORMAT).textValue();
    if(!"PNG".equalsIgnoreCase(format)){
        throw new UnsupportedOperationException("Deserialization of formats other than PNG not yet implemented");
    }
    String base64Data = n2.get(Data.RESERVED_KEY_IMAGE_DATA).textValue();
    byte[] bytes = Base64.getDecoder().decode(base64Data);
    Png png = new Png(bytes);
    return Image.create(png);
}
 
Example #20
Source File: DataJsonDeserializer.java    From konduit-serving with Apache License 2.0 5 votes vote down vote up
protected NDArray deserializeNDArray(JsonNode n){
    NDArrayType type = NDArrayType.valueOf(n.get(Data.RESERVED_KEY_NDARRAY_TYPE).textValue());
    ArrayNode shapeNode = (ArrayNode) n.get(Data.RESERVED_KEY_NDARRAY_SHAPE);
    long[] shape = new long[shapeNode.size()];
    for (int i = 0; i < shape.length; i++)
        shape[i] = shapeNode.get(i).asLong();
    String base64 = n.get(Data.RESERVED_KEY_NDARRAY_DATA_BASE64).textValue();
    byte[] bytes = Base64.getDecoder().decode(base64);
    ByteBuffer bb = ByteBuffer.wrap(bytes);
    SerializedNDArray ndArray = new SerializedNDArray(type, shape, bb);
    return NDArray.create(ndArray);
}
 
Example #21
Source File: RealDistributionDeserializer.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
@Override
public RealDistribution deserialize(JsonParser p, DeserializationContext ctxt)
                throws IOException, JsonProcessingException {
    JsonNode node = p.getCodec().readTree(p);
    String simpleName = node.get("distribution").asText();

    switch (simpleName) {
        case "BetaDistribution":
            return new BetaDistribution(node.get("alpha").asDouble(), node.get("beta").asDouble());
        case "CauchyDistribution":
            return new CauchyDistribution(node.get("median").asDouble(), node.get("scale").asDouble());
        case "ChiSquaredDistribution":
            return new ChiSquaredDistribution(node.get("dof").asDouble());
        case "ExponentialDistribution":
            return new ExponentialDistribution(node.get("mean").asDouble());
        case "FDistribution":
            return new FDistribution(node.get("numeratorDof").asDouble(), node.get("denominatorDof").asDouble());
        case "GammaDistribution":
            return new GammaDistribution(node.get("shape").asDouble(), node.get("scale").asDouble());
        case "LevyDistribution":
            return new LevyDistribution(node.get("mu").asDouble(), node.get("c").asDouble());
        case "LogNormalDistribution":
            return new LogNormalDistribution(node.get("scale").asDouble(), node.get("shape").asDouble());
        case "NormalDistribution":
            return new NormalDistribution(node.get("mean").asDouble(), node.get("stdev").asDouble());
        case "ParetoDistribution":
            return new ParetoDistribution(node.get("scale").asDouble(), node.get("shape").asDouble());
        case "TDistribution":
            return new TDistribution(node.get("dof").asDouble());
        case "TriangularDistribution":
            return new TriangularDistribution(node.get("a").asDouble(), node.get("b").asDouble(),
                            node.get("c").asDouble());
        case "UniformRealDistribution":
            return new UniformRealDistribution(node.get("lower").asDouble(), node.get("upper").asDouble());
        case "WeibullDistribution":
            return new WeibullDistribution(node.get("alpha").asDouble(), node.get("beta").asDouble());
        case "LogUniformDistribution":
            return new LogUniformDistribution(node.get("min").asDouble(), node.get("max").asDouble());
        default:
            throw new RuntimeException("Unknown or not supported distribution: " + simpleName);
    }


}
 
Example #22
Source File: ComputationGraphConfiguration.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
/**
 * Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied from handling of {@link Activation}
 * above.
 * @return True if all is well and layer iteration shall continue. False else-wise.
 */
private static void handleLegacyWeightInitFromJson(String json, Layer layer, ObjectMapper mapper, JsonNode vertices) {
    if (layer instanceof BaseLayer && ((BaseLayer) layer).getWeightInitFn() == null) {
        String layerName = layer.getLayerName();

        try {
            if (vertices == null) {
                JsonNode jsonNode = mapper.readTree(json);
                vertices = jsonNode.get("vertices");
            }

            JsonNode vertexNode = vertices.get(layerName);
            JsonNode layerVertexNode = vertexNode.get("LayerVertex");
            if (layerVertexNode == null || !layerVertexNode.has("layerConf")
                    || !layerVertexNode.get("layerConf").has("layer")) {
                return;
            }
            JsonNode layerWrapperNode = layerVertexNode.get("layerConf").get("layer");

            if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
                return;
            }

            JsonNode layerNode = layerWrapperNode.elements().next();
            JsonNode weightInit = layerNode.get("weightInit"); //Should only have 1 element: "dense", "output", etc
            JsonNode distribution = layerNode.get("dist");

            Distribution dist = null;
            if(distribution != null) {
                dist = mapper.treeToValue(distribution, Distribution.class);
            }

            if (weightInit != null) {
                final IWeightInit wi = WeightInit.valueOf(weightInit.asText()).getWeightInitFunction(dist);
                ((BaseLayer) layer).setWeightInitFn(wi);
            }

        } catch (IOException e) {
            log.warn("Layer with null ActivationFn field or pre-0.7.2 activation function detected: could not parse JSON",
                    e);
        }
    }
}
 
Example #23
Source File: MultiLayerConfiguration.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
/**
 * Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied from handling of {@link Activation}
 * above.
 * @return True if all is well and layer iteration shall continue. False else-wise.
 */
private static boolean handleLegacyWeightInitFromJson(String json, Layer l, ObjectMapper mapper, JsonNode confs, int layerCount) {
    if ((l instanceof BaseLayer) && ((BaseLayer) l).getWeightInitFn() == null) {
        try {
            JsonNode jsonNode = mapper.readTree(json);
            if (confs == null) {
                confs = jsonNode.get("confs");
            }
            if (confs instanceof ArrayNode) {
                ArrayNode layerConfs = (ArrayNode) confs;
                JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
                if (outputLayerNNCNode == null)
                    return false; //Should never happen...
                JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");

                if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
                    return true;
                }

                JsonNode layerNode = layerWrapperNode.elements().next();
                JsonNode weightInit = layerNode.get("weightInit"); //Should only have 1 element: "dense", "output", etc
                JsonNode distribution = layerNode.get("dist");

                Distribution dist = null;
                if(distribution != null) {
                    dist = mapper.treeToValue(distribution, Distribution.class);
                }

                if (weightInit != null) {
                    final IWeightInit wi = WeightInit.valueOf(weightInit.asText()).getWeightInitFunction(dist);
                    ((BaseLayer) l).setWeightInitFn(wi);
                }
            }

        } catch (IOException e) {
            log.warn("Layer with null WeightInit detected: " + l.getLayerName() + ", could not parse JSON",
                    e);
        }
    }
    return true;

}
 
Example #24
Source File: NDArrayDeSerializer.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
@Override
public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException {
    JsonNode node = jp.getCodec().readTree(jp);
    String field = node.get("array").asText();
    return Nd4jBase64.fromBase64(field);
}
 
Example #25
Source File: BoundingBoxDeserializer.java    From konduit-serving with Apache License 2.0 4 votes vote down vote up
@Override
public BoundingBox deserialize(JsonParser jp, DeserializationContext dc) throws IOException, JsonProcessingException {
    JsonNode n = jp.getCodec().readTree(jp);
    return DataJsonDeserializer.deserializeBB(n);
}
 
Example #26
Source File: NDArrayTextDeSerializer.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
@Override
public INDArray deserialize(JsonParser jp, DeserializationContext deserializationContext) throws IOException {
    JsonNode n = jp.getCodec().readTree(jp);
    return deserialize(n);
}
 
Example #27
Source File: JsonDeserializerAtomicBoolean.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
@Override
public AtomicBoolean deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    boolean value = node.asBoolean();
    return new AtomicBoolean(value);
}
 
Example #28
Source File: JsonDeserializerAtomicDouble.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
@Override
public AtomicDouble deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    double value = node.asDouble();
    return new AtomicDouble(value);
}
 
Example #29
Source File: DataAnalysis.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up
private static DataAnalysis fromMapper(ObjectMapper om, String json) {

        List<ColumnMetaData> meta = new ArrayList<>();
        List<ColumnAnalysis> analysis = new ArrayList<>();
        try {
            JsonNode node = om.readTree(json);
            Iterator<String> fieldNames = node.fieldNames();
            boolean hasDataAnalysis = false;
            while (fieldNames.hasNext()) {
                if ("DataAnalysis".equals(fieldNames.next())) {
                    hasDataAnalysis = true;
                    break;
                }
            }
            if (!hasDataAnalysis) {
                throw new RuntimeException();
            }

            ArrayNode arrayNode = (ArrayNode) node.get("DataAnalysis");
            for (int i = 0; i < arrayNode.size(); i++) {
                JsonNode analysisNode = arrayNode.get(i);
                String name = analysisNode.get(COL_NAME).asText();
                int idx = analysisNode.get(COL_IDX).asInt();
                ColumnType type = ColumnType.valueOf(analysisNode.get(COL_TYPE).asText());

                JsonNode daNode = analysisNode.get(ANALYSIS);
                ColumnAnalysis dataAnalysis = om.treeToValue(daNode, ColumnAnalysis.class);

                if (type == ColumnType.Categorical) {
                    ArrayNode an = (ArrayNode) analysisNode.get(CATEGORICAL_STATE_NAMES);
                    List<String> stateNames = new ArrayList<>(an.size());
                    Iterator<JsonNode> iter = an.elements();
                    while (iter.hasNext()) {
                        stateNames.add(iter.next().asText());
                    }
                    meta.add(new CategoricalMetaData(name, stateNames));
                } else {
                    meta.add(type.newColumnMetaData(name));
                }

                analysis.add(dataAnalysis);
            }
        } catch (Exception e) {
            throw new RuntimeException(e);
        }

        Schema schema = new Schema(meta);
        return new DataAnalysis(schema, analysis);
    }
 
Example #30
Source File: JsonDeserializerAtomicBoolean.java    From nd4j with Apache License 2.0 4 votes vote down vote up
@Override
public AtomicBoolean deserialize(JsonParser jsonParser, DeserializationContext deserializationContext) throws IOException, JsonProcessingException {
    JsonNode node = jsonParser.getCodec().readTree(jsonParser);
    boolean value = node.asBoolean();
    return new AtomicBoolean(value);
}