Java Code Examples for org.nd4j.shade.jackson.databind.node.ArrayNode#get()
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org.nd4j.shade.jackson.databind.node.ArrayNode#get() .
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Example 1
Source File: ConfusionMatrixDeserializer.java From deeplearning4j with Apache License 2.0 | 5 votes |
@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 |
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 |
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 |
/** * 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; }