Example usage for com.google.gson JsonObject add

List of usage examples for com.google.gson JsonObject add

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Introduction

In this page you can find the example usage for com.google.gson JsonObject add.

Prototype

public void add(String property, JsonElement value)

Source Link

Document

Adds a member, which is a name-value pair, to self.

Usage

From source file:be.iminds.iot.dianne.builder.DianneLearner.java

License:Open Source License

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    response.setContentType("application/json");
    String id = request.getParameter("id");
    if (id == null) {
        System.out.println("No neural network instance specified");
    }/*ww w . j a  v a 2 s .c om*/
    UUID nnId = UUID.fromString(id);
    NeuralNetworkInstanceDTO nni = platform.getNeuralNetworkInstance(nnId);
    if (nni == null) {
        System.out.println("Neural network instance " + id + " not deployed");
        return;
    }

    String action = request.getParameter("action");
    if (action.equals("stop")) {
        learner.stop();
        return;
    }

    String target = request.getParameter("target");
    // this list consists of all ids of modules that are needed for trainer:
    // the input, output, trainable and preprocessor modules
    String configJsonString = request.getParameter("config");

    JsonObject configJson = parser.parse(configJsonString).getAsJsonObject();

    JsonObject learnerConfig = (JsonObject) configJson.get(target);

    for (Entry<String, JsonElement> configs : configJson.entrySet()) {
        JsonObject datasetConfig = (JsonObject) configs.getValue();
        if (datasetConfig.get("category").getAsString().equals("Dataset")
                && datasetConfig.get("input") != null) {
            String dataset = datasetConfig.get("dataset").getAsString();
            if (action.equals("learn")) {
                int start = 0;
                int end = datasetConfig.get("train").getAsInt();

                int batch = learnerConfig.get("batch").getAsInt();
                float learningRate = learnerConfig.get("learningRate").getAsFloat();
                float momentum = learnerConfig.get("momentum").getAsFloat();
                float regularization = learnerConfig.get("regularization").getAsFloat();
                String criterion = learnerConfig.get("loss").getAsString();
                String method = learnerConfig.get("method").getAsString();

                boolean clean = learnerConfig.get("clean").getAsBoolean();

                Map<String, String> config = new HashMap<>();
                config.put("range", "" + start + "," + end);
                config.put("batchSize", "" + batch);
                config.put("learningRate", "" + learningRate);
                config.put("momentum", "" + momentum);
                config.put("regularization", "" + regularization);
                config.put("criterion", criterion);
                config.put("method", method);
                config.put("syncInterval", "" + interval);
                config.put("clean", clean ? "true" : "false");
                config.put("trace", "true");

                try {
                    Dataset d = datasets.getDataset(dataset);
                    if (d != null) {
                        JsonObject labels = new JsonObject();
                        labels.add(target, new JsonPrimitive(Arrays.toString(d.getLabels())));
                        response.getWriter().write(labels.toString());
                        response.getWriter().flush();
                    }

                    learner.learn(dataset, config, nni);

                } catch (Exception e) {
                    e.printStackTrace();
                }

            } else if (action.equals("evaluate")) {
                int start = datasetConfig.get("train").getAsInt();
                int end = start + datasetConfig.get("test").getAsInt();

                Map<String, String> config = new HashMap<>();
                config.put("range", "" + start + "," + end);

                try {
                    Evaluation result = evaluator.eval(dataset, config, nni);

                    JsonObject eval = new JsonObject();

                    eval.add("metric", new JsonPrimitive(result.metric()));
                    eval.add("time", new JsonPrimitive(result.time()));

                    if (result instanceof ErrorEvaluation) {
                        ErrorEvaluation ee = (ErrorEvaluation) result;
                        eval.add("error", new JsonPrimitive(ee.error()));
                    }

                    if (result instanceof ClassificationEvaluation) {
                        ClassificationEvaluation ce = (ClassificationEvaluation) result;
                        eval.add("accuracy", new JsonPrimitive(ce.accuracy() * 100));

                        Tensor confusionMatrix = ce.confusionMatrix();
                        JsonArray data = new JsonArray();
                        for (int i = 0; i < confusionMatrix.size(0); i++) {
                            for (int j = 0; j < confusionMatrix.size(1); j++) {
                                JsonArray element = new JsonArray();
                                element.add(new JsonPrimitive(i));
                                element.add(new JsonPrimitive(j));
                                element.add(new JsonPrimitive(confusionMatrix.get(i, j)));
                                data.add(element);
                            }
                        }
                        eval.add("confusionMatrix", data);
                    }

                    response.getWriter().write(eval.toString());
                    response.getWriter().flush();
                } catch (Exception e) {
                    e.printStackTrace();
                    JsonObject eval = new JsonObject();
                    eval.add("error", new JsonPrimitive(e.getCause().getMessage()));
                    response.getWriter().write(eval.toString());
                    response.getWriter().flush();
                }
            }
            break;
        }
    }
}

From source file:be.iminds.iot.dianne.builder.DianneOutput.java

License:Open Source License

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    response.setContentType("application/json");

    String action = request.getParameter("action");
    if ("available-outputs".equals(action)) {
        JsonArray availableOutputs = new JsonArray();
        synchronized (outputs) {
            for (DianneOutputs o : outputs) {
                for (OutputDescription output : o.getAvailableOutputs()) {
                    JsonObject ob = new JsonObject();
                    ob.add("name", new JsonPrimitive(output.getName()));
                    ob.add("type", new JsonPrimitive(output.getType()));
                    availableOutputs.add(ob);
                }/*from w  ww .  j av a 2 s  . c  o  m*/
            }
        }
        response.getWriter().write(availableOutputs.toString());
        response.getWriter().flush();
    } else if ("setoutput".equals(action)) {
        String nnId = request.getParameter("nnId");
        String outputId = request.getParameter("outputId");
        String output = request.getParameter("output");
        // TODO only forward to applicable outputmgr?
        synchronized (outputs) {
            for (DianneOutputs o : outputs) {
                o.setOutput(UUID.fromString(nnId), UUID.fromString(outputId), output);
            }
        }
    } else if ("unsetoutput".equals(action)) {
        String nnId = request.getParameter("nnId");
        String outputId = request.getParameter("outputId");
        String output = request.getParameter("output");
        // TODO only forward to applicable outputmgr?
        synchronized (outputs) {
            for (DianneOutputs o : outputs) {
                o.unsetOutput(UUID.fromString(nnId), UUID.fromString(outputId), output);
            }
        }
    }
}

From source file:be.iminds.iot.dianne.builder.DianneRunner.java

License:Open Source License

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    response.setContentType("application/json");
    String id = request.getParameter("id");
    if (id == null) {
        System.out.println("No neural network instance specified");
        return;//from w  w w . j av a 2 s .c o m
    }
    UUID nnId = UUID.fromString(id);
    NeuralNetworkInstanceDTO nni = platform.getNeuralNetworkInstance(nnId);
    if (nni == null) {
        System.out.println("Neural network instance " + id + " not deployed");
        return;
    }

    NeuralNetwork nn = null;
    try {
        nn = dianne.getNeuralNetwork(nni).getValue();
    } catch (Exception e) {
    }
    if (nn == null) {
        System.out.println("Neural network instance " + id + " not available");
        return;
    }

    if (request.getParameter("forward") != null) {
        String inputId = request.getParameter("input");

        JsonObject sample = parser.parse(request.getParameter("forward")).getAsJsonObject();
        Tensor t = jsonConverter.fromJson(sample);

        start = System.currentTimeMillis();
        nn.forward(UUID.fromString(inputId), null, t, "ui");

    } else if (request.getParameter("url") != null) {
        String url = request.getParameter("url");
        String inputId = request.getParameter("input");

        Tensor t = null;
        try {
            URL u = new URL(url);
            BufferedImage img = ImageIO.read(u);
            t = imageConverter.fromImage(img);
        } catch (Exception e) {
            System.out.println("Failed to read image from url " + url);
            return;
        }

        start = System.currentTimeMillis();
        nn.forward(UUID.fromString(inputId), null, t, "ui");

    } else if (request.getParameter("mode") != null) {
        String mode = request.getParameter("mode");
        String targetId = request.getParameter("target");

        Module m = nn.getModules().get(UUID.fromString(targetId));
        if (m != null) {
            m.setMode(EnumSet.of(Mode.valueOf(mode)));
        }
    } else if (request.getParameter("dataset") != null) {
        String dataset = request.getParameter("dataset");
        if (datasets == null) {
            System.out.println("Datasets service not available");
            return;
        }
        Dataset d = datasets.getDataset(dataset);
        if (d == null) {
            System.out.println("Dataset " + dataset + " not available");
            return;
        }

        Sample s = d.getSample(rand.nextInt(d.size()));

        String inputId = request.getParameter("input");

        if (inputId != null) {
            start = System.currentTimeMillis();
            nn.forward(UUID.fromString(inputId), null, s.input, "ui");
        }

        JsonObject sample = jsonConverter.toJson(s.input);
        String[] labels = d.getLabels();
        if (labels != null) {
            sample.add("target", new JsonPrimitive(labels[TensorOps.argmax(s.target)]));
        } else {
            if (s.target.size() < 10) {
                JsonObject target = jsonConverter.toJson(s.target);
                sample.add("target", target.get("data").getAsJsonArray());
            }
        }

        response.getWriter().println(sample.toString());
        response.getWriter().flush();
    }
}

From source file:be.iminds.iot.dianne.builder.DianneRunner.java

License:Open Source License

private String outputSSEMessage(UUID outputId, String[] outputLabels, Tensor output, long time,
        String... tags) {//from  w w  w .j  av  a 2  s  .  com
    JsonObject data = jsonConverter.toJson(output);

    float max = TensorOps.max(output);
    if (outputLabels != null || isProbability(output)) {
        // format output as [['label', val],['label2',val2],...] for in highcharts
        String[] labels;
        float[] values;
        if (output.size() > 10) {
            // if more than 10 outputs, only send top-10 results
            Integer[] indices = new Integer[output.size()];
            for (int i = 0; i < output.size(); i++) {
                indices[i] = i;
            }
            Arrays.sort(indices, new Comparator<Integer>() {
                @Override
                public int compare(Integer o1, Integer o2) {
                    float v1 = output.get(o1);
                    float v2 = output.get(o2);
                    // inverse order to have large->small order
                    return v1 > v2 ? -1 : (v1 < v2 ? 1 : 0);
                }
            });
            labels = new String[10];
            values = new float[10];
            for (int i = 0; i < 10; i++) {
                labels[i] = outputLabels != null ? outputLabels[indices[i]] : "" + indices[i];
                values[i] = output.get(indices[i]);
            }
        } else {
            labels = outputLabels;
            if (labels == null) {
                labels = new String[output.size()];
                for (int i = 0; i < labels.length; i++) {
                    labels[i] = "" + i;
                }
            }
            values = output.get();
        }

        JsonArray probabilities = new JsonArray();
        for (int i = 0; i < values.length; i++) {
            // if negative values for classification - assume log probabilities
            // take exp to return probability
            if (max < 0) {
                values[i] = (float) Math.exp(values[i]);
            }
            probabilities.add(new JsonPrimitive(values[i]));
        }
        data.add("probabilities", probabilities);

        JsonArray l = new JsonArray();
        for (int i = 0; i < labels.length; i++) {
            l.add(new JsonPrimitive(labels[i]));
        }
        data.add("labels", l);
    }

    if (time > 0) {
        data.add("time", new JsonPrimitive(time));
    }

    if (tags != null) {
        JsonArray ta = new JsonArray();
        for (String tt : tags) {
            if (tt.equals("ui") || tt.startsWith("_")) // ignore the ui tag
                continue;

            ta.add(new JsonPrimitive(tt));
        }
        data.add("tags", ta);
    }

    data.add("id", new JsonPrimitive(outputId.toString()));

    StringBuilder builder = new StringBuilder();
    builder.append("data: ").append(data.toString()).append("\n\n");
    return builder.toString();
}

From source file:be.iminds.iot.dianne.builder.DianneRunner.java

License:Open Source License

private String errorSSEMessage(ModuleException exception) {
    JsonObject data = new JsonObject();

    data.add("id", new JsonPrimitive(exception.moduleId.toString()));
    data.add("error", new JsonPrimitive(exception.getMessage()));

    StringBuilder builder = new StringBuilder();
    builder.append("data: ").append(data.toString()).append("\n\n");
    return builder.toString();
}

From source file:be.iminds.iot.dianne.builder.DianneVAE.java

License:Open Source License

@Override
protected void doPost(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException {
    response.setContentType("application/json");
    String d = request.getParameter("dataset");
    if (d == null) {
        System.out.println("No dataset provided");
        return;//from   w w  w.j  a v  a  2s  .c om
    }
    Dataset dataset = datasets.getDataset(d);
    if (dataset == null) {
        System.out.println("No such dataset found: " + d);
        return;
    }

    String enc = request.getParameter("encoder");
    if (enc == null) {
        System.out.println("No encoder provided");
        return;
    }

    NeuralNetwork encoder = nns.get(enc);
    if (encoder == null) {
        try {
            encoder = dianne.getNeuralNetwork(platform.deployNeuralNetwork(enc, new String[] { "vae" }))
                    .getValue();
            nns.put(enc, encoder);
        } catch (Exception e) {
            e.printStackTrace();
            return;
        }
    }

    NeuralNetwork decoder = null;
    String dec = request.getParameter("decoder");
    if (dec != null) {
        decoder = nns.get(dec);
        if (decoder == null) {
            try {
                decoder = dianne.getNeuralNetwork(platform.deployNeuralNetwork(dec, new String[] { "vae" }))
                        .getValue();
                nns.put(dec, decoder);
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
    }

    int size = this.size;
    String s = request.getParameter("size");
    if (s != null) {
        size = Integer.parseInt(s);
    }

    JsonArray result = new JsonArray();

    String sample = request.getParameter("sample");
    String l = request.getParameter("latent");
    if (sample == null && l == null) {
        // fetch random samples from dataset
        for (int i = 0; i < size; i++) {
            JsonObject o = new JsonObject();
            int index = random.nextInt(dataset.size());
            Tensor data = dataset.getSample(index).input;
            Tensor latent = encoder.forward(data);

            o.add("index", new JsonPrimitive(index));
            o.add("data", converter.toJson(data));
            o.add("latent", converter.toJson(latent));

            result.add(o);
        }

    } else {
        Tensor latent = null;
        if (sample != null) {
            int index = Integer.parseInt(sample);
            Tensor base = dataset.getSample(index).input;
            latent = encoder.forward(base).clone();
        } else {
            Tensor lmeans = converter.fromString(l);
            latent = new Tensor(lmeans.size() * 2);
            latent.fill(0.0f);
            lmeans.copyInto(latent.narrow(0, lmeans.size()));
        }
        if (decoder != null) {
            // generate samples using the decoder
            Tensor mean = latent.narrow(0, latent.size() / 2);
            Tensor stdev = latent.narrow(latent.size() / 2, latent.size() / 2);
            Tensor rand = new Tensor(latent.size() / 2);
            Tensor rand2 = null;

            for (int i = 0; i < size; i++) {
                rand.randn();
                TensorOps.cmul(rand, rand, stdev);
                TensorOps.add(rand, rand, mean);

                Tensor reconstruction = decoder.forward(rand);
                Tensor rmean = reconstruction.narrow(0, reconstruction.size() / 2);
                Tensor rstdev = reconstruction.narrow(reconstruction.size() / 2, reconstruction.size() / 2);

                if (rand2 == null) {
                    rand2 = new Tensor(rmean.dims());
                }
                rand2.randn();
                TensorOps.cmul(rand2, rand2, rstdev);
                TensorOps.add(rand2, rand2, rmean);

                JsonObject o = new JsonObject();
                o.add("index", new JsonPrimitive(-1));
                o.add("data", converter.toJson(rand2));
                o.add("latent", converter.toJson(latent));
                result.add(o);
            }

        } else {
            // search for samples close to this sample in the dataset
            final Tensor baseline = latent;
            SortedSet<LatentSample> set = new TreeSet<>(new Comparator<LatentSample>() {
                @Override
                public int compare(LatentSample o1, LatentSample o2) {
                    Tensor diff = TensorOps.sub(null, o1.latent.narrow(0, baseline.size() / 2),
                            baseline.narrow(0, baseline.size() / 2));
                    Float d1 = TensorOps.dot(diff, diff);
                    diff = TensorOps.sub(null, o2.latent.narrow(0, baseline.size() / 2),
                            baseline.narrow(0, baseline.size() / 2));
                    Float d2 = TensorOps.dot(diff, diff);
                    return d1.compareTo(d2);
                }
            });

            for (int i = 0; i < tries; i++) {
                LatentSample ls = new LatentSample();
                ls.index = random.nextInt(dataset.size());
                ls.data = dataset.getSample(ls.index).input;
                ls.latent = encoder.forward(ls.data).clone();
                set.add(ls);

                if (set.size() > size) {
                    set.remove(set.last());
                }
            }

            for (LatentSample ls : set) {
                JsonObject o = new JsonObject();
                o.add("index", new JsonPrimitive(ls.index));
                o.add("data", converter.toJson(ls.data));
                o.add("latent", converter.toJson(ls.latent));
                result.add(o);
            }
        }
    }

    response.getWriter().println(result);
    response.getWriter().flush();
}

From source file:be.iminds.iot.dianne.jsonrpc.DianneSSEServlet.java

License:Open Source License

@Override
public void handleEvent(Event event) {
    // construct server sent event
    JsonObject data = new JsonObject();

    if (event.getTopic().contains("progress")) {
        // progress
        data.add("type", new JsonPrimitive("progress"));
        data.add("jobId", new JsonPrimitive(event.getProperty("jobId").toString()));

        if (event.containsProperty("iteration")) {
            data.add("iteration", new JsonPrimitive((Long) event.getProperty("iteration")));
        }/*  w w  w .j a  va  2s  . c om*/

        if (event.containsProperty("minibatchLoss")) {
            data.add("minibatchLoss", new JsonPrimitive((Float) event.getProperty("minibatchLoss")));
        }

        if (event.containsProperty("validationLoss")) {
            data.add("validationLoss", new JsonPrimitive((Float) event.getProperty("validationLoss")));
        }

        if (event.containsProperty("q")) {
            data.add("q", new JsonPrimitive((Float) event.getProperty("q")));
        }

        if (event.containsProperty("reward")) {
            data.add("reward", new JsonPrimitive((Float) event.getProperty("reward")));
        }

        if (event.containsProperty("sequence")) {
            data.add("sequence", new JsonPrimitive((Long) event.getProperty("sequence")));
        }
    } else {
        // notification
        if (event.containsProperty("jobId"))
            data.add("jobId", new JsonPrimitive(event.getProperty("jobId").toString()));
        data.add("type", new JsonPrimitive("notification"));
        data.add("message", new JsonPrimitive((String) event.getProperty("message")));
        data.add("level", new JsonPrimitive(event.getProperty("level").toString()));
        long timestamp = (Long) event.getProperty("timestamp");
        data.add("timestamp", new JsonPrimitive(timestamp));
    }

    StringBuilder builder = new StringBuilder();
    builder.append("data: ").append(data.toString()).append("\n\n");
    String sse = builder.toString();

    // send to all clients
    Iterator<Entry<String, AsyncContext>> it = clients.entrySet().iterator();
    while (it.hasNext()) {
        AsyncContext client = it.next().getValue();
        try {
            PrintWriter writer = client.getResponse().getWriter();
            writer.write(sse);
            writer.flush();
        } catch (Exception e) {
            it.remove();
        }
    }
}

From source file:be.iminds.iot.dianne.nn.util.DianneJSONConverter.java

License:Open Source License

public static JsonObject toJson(NeuralNetworkDTO dto) {
    JsonObject nn = new JsonObject();

    JsonObject modules = new JsonObject();
    for (ModuleDTO m : dto.modules.values()) {
        JsonObject module = toJson(m);//  www .  j  av  a2  s  .co  m
        modules.add(m.id.toString(), module);
    }

    String name = dto.name == null ? "unnamed" : dto.name;
    nn.add("name", new JsonPrimitive(name));
    nn.add("modules", modules);
    return nn;
}

From source file:be.iminds.iot.dianne.nn.util.DianneJSONConverter.java

License:Open Source License

public static JsonObject toJson(ModuleDTO dto) {
    JsonObject module = new JsonObject();

    module.add("id", new JsonPrimitive(dto.id.toString()));
    module.add("type", new JsonPrimitive(dto.type));

    if (dto.next != null) {
        JsonArray next = new JsonArray();
        for (UUID n : dto.next) {
            next.add(new JsonPrimitive(n.toString()));
        }/*  www .  java  2 s  .  c o  m*/
        module.add("next", next);
    }

    if (dto.prev != null) {
        JsonArray prev = new JsonArray();
        for (UUID p : dto.prev) {
            prev.add(new JsonPrimitive(p.toString()));
        }
        module.add("prev", prev);
    }

    if (dto.properties != null) {
        for (String k : dto.properties.keySet()) {
            module.add(k, new JsonPrimitive(dto.properties.get(k)));
        }
    }

    return module;
}

From source file:be.iminds.iot.dianne.nn.util.DianneJSONRPCRequestFactory.java

License:Open Source License

public static JsonObject createLearnRequest(int id, NeuralNetworkDTO nn, String dataset,
        Map<String, String> properties) {
    JsonObject request = new JsonObject();
    request.add("jsonrpc", new JsonPrimitive("2.0"));
    request.add("method", new JsonPrimitive("learn"));
    request.add("id", new JsonPrimitive(id));

    JsonArray params = new JsonArray();
    params.add(DianneJSONConverter.toJson(nn));
    params.add(new JsonPrimitive(dataset));
    params.add(createJsonFromMap(properties));
    request.add("params", params);

    return request;
}