Package org.apache.commons.math.linear

Examples of org.apache.commons.math.linear.RealVector$Entry

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  }

  @Override
  protected void doDelete(HttpServletRequest req, HttpServletResponse resp)
      throws ServletException, IOException {
    Entry e = entry(req);
    S3Object remove = map.remove(e);
    if (remove == null) {
      resp.sendError(404, "Not found " + e);
    } else {
      resp.sendError(HttpURLConnection.HTTP_NO_CONTENT, "Deleted");
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    }

  }

  private Entry entry(HttpServletRequest req) {
    return new Entry(key(uri(req)));
  }
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        log("doGet " + uri);
    if ("/".equals(uri.getPath())) {
      list(req, resp);
    } else {
      String key = uri.getPath().substring(1);
      Entry e = new Entry(key);
      S3Object obj = map.get(e);
        if (debug)
          log("map.get(" + key + ") = " + obj);
      if (obj == null) {
        resp.sendError(404, "Not here: " + e);
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    if (maxKeysStr != null)
      maxKeys = Integer.parseInt(maxKeysStr);
    Writer w = new Writer();
    SortedMap<Entry, S3Object> submap = new TreeMap<Entry, S3Object>(map);
    if (prefix != null)
      submap = submap.tailMap(new Entry(prefix));
    int keyCount = 0;
    boolean truncated = false;
    String nextMarker = null;
    for (Entry e : submap.keySet()) {
      if (++keyCount > maxKeys) {
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    log("doPut " + uri);
    if ("/".equals(uri.getPath())) {
      log("create bucket");
      bucket = true;
    } else {
      Entry e = new Entry(key(uri));
      e.setLastModified(new Date());
      e.setSize(req.getContentLength());
      e.setOwner(new Owner("id", "name"));
      ByteArrayOutputStream os = new ByteArrayOutputStream();
      ServletInputStream is = req.getInputStream();
      byte b[] = new byte[128];
      while (true) {
        int len = is.read(b);
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    Vector2d direction = rm.getNewDirectionVector();
    direction.normalize();
    RealMatrix coefficients = new Array2DRowRealMatrix(new double[][] { { direction.getX(), -(sideVectX) },
        { direction.getY(), -(sideVectY) } }, false);
    DecompositionSolver solver = new LUDecompositionImpl(coefficients).getSolver();
    RealVector constants = new ArrayRealVector(new double[] { sideX - roboter1.getX(), sideY - roboter1.getY() }, false);
    RealVector solution = solver.solve(constants);

    double r = solution.getEntry(0);
    // double s = solution.getEntry(1);

    double res1 = roboter1.getX() + direction.getX() * r;
    double res2 = roboter1.getY() + direction.getY() * r;

 
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    /**
     * {@inheritDoc}
     */
    public double[] estimateRegressionParameters() {
        RealVector b = calculateBeta();
        return b.getData();
    }
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    /**
     * {@inheritDoc}
     */
    public double[] estimateResiduals() {
        RealVector b = calculateBeta();
        RealVector e = Y.subtract(X.operate(b));
        return e.getData();
    }
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     *
     * @return error variance estimate
     * @since 2.2
     */
    protected double calculateErrorVariance() {
        RealVector residuals = calculateResiduals();
        return residuals.dotProduct(residuals) /
               (X.getRowDimension() - X.getColumnDimension());
    }
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     * </pre>
     *
     * @return The residuals [n,1] matrix
     */
    protected RealVector calculateResiduals() {
        RealVector b = calculateBeta();
        return Y.subtract(X.operate(b));
    }
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