Java Code Examples for org.nd4j.shade.jackson.databind.node.ArrayNode#get()

The following examples show how to use org.nd4j.shade.jackson.databind.node.ArrayNode#get() . 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: 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 2
Source File: DataAnalysis.java    From DataVec 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 3
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 4
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;

}