org.nd4j.shade.jackson.databind.JsonNode Java Examples
The following examples show how to use
org.nd4j.shade.jackson.databind.JsonNode.
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Example #1
Source File: IntegerDistributionDeserializer.java From deeplearning4j with Apache License 2.0 | 6 votes |
@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 |
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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
@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 |
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 |
@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 |
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 |
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 |
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 |
@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 |
/** * 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 |
/** * 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 |
@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 |
@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 |
@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 |
@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 |
@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 |
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 |
@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); }