* or implied. See the License for the specific language governing permissions and limitations under
 * the License.
 */

/*
 * SipHash-c-d was designed by Jean-Philippe Aumasson and Daniel J. Bernstein and is described in
 * "SipHash: a fast short-input PRF" (available at https://131002.net/siphash/siphash.pdf).
 */

package com.google.common.hash;

    author_link: https://newrelic.com
    link: https://newrelic.com/instant-observability/fastapi/e559ec64-f765-4470-a15f-1901fcebb468
    title: How to monitor FastAPI application performance using Python agent
  - author: Jean-Baptiste Rocher
    author_link: https://hashnode.com/@jibrocher
    link: https://dev.indooroutdoor.io/series/fastapi-react-poll-app
    title: Building the Poll App From Django Tutorial With FastAPI And React

*   `tf.sparse.segment_sum` `tf.sparse.segment_mean` `tf.sparse.segment_sqrt_n` `SparseSegmentSum/Mean/SqrtN[WithNumSegments]`

    *   Added `sparse_gradient` option (default=false) that makes the gradient of these functions/ops sparse (`IndexedSlices`) instead of dense (`Tensor`), using new `SparseSegmentSum/Mean/SqrtNGradV2` ops.

*   `tf.nn.embedding_lookup_sparse`

// Copyright 2013 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#include <process.h>
#include "_cgo_export.h"

__stdcall
static unsigned int
addThread(void *p)
{
	int i, max;
	
	max = *(int*)p;
	for(i=0; i<max; i++)
		Add(i);
	return 0;
}

void

        for (int i = 1; i < values.length; i++) {
          mean = mean + (values[i] - mean) / (i + 1);
        }
        return mean;
      }
    };

    abstract double mean(double[] values);
  }

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

          + (-22.0 - INTEGER_MANY_VALUES_MEAN) * (-22.0 - INTEGER_MANY_VALUES_MEAN)
          + (3333.0 - INTEGER_MANY_VALUES_MEAN) * (3333.0 - INTEGER_MANY_VALUES_MEAN)
          + (-4444.0 - INTEGER_MANY_VALUES_MEAN) * (-4444.0 - INTEGER_MANY_VALUES_MEAN)
          + (555555.0 - INTEGER_MANY_VALUES_MEAN) * (555555.0 - INTEGER_MANY_VALUES_MEAN);
  static final double INTEGER_MANY_VALUES_MAX = 555555.0;

   */
  @Deprecated
  public static double mean(long... values) {
    checkArgument(values.length > 0, "Cannot take mean of 0 values");
    long count = 1;
    double mean = values[0];
    for (int index = 1; index < values.length; ++index) {
      count++;
      // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15)
      mean += (values[index] - mean) / count;
    }
    return mean;
  }

  /**

      DoubleMath.mean(Double.POSITIVE_INFINITY);
      fail("Expected IllegalArgumentException");
    } catch (IllegalArgumentException expected) {
    }
  }

  @GwtIncompatible // DoubleMath.mean
  public void testMean_intVarargs() {
    assertEquals(-13.75, DoubleMath.mean(11, -22, 44, -88), 1.0e-10);
    assertEquals(11.0, DoubleMath.mean(11), 1.0e-10);
  }

  @GwtIncompatible // DoubleMath.mean

        for (int i = 1; i < values.length; i++) {
          mean = mean + (values[i] - mean) / (i + 1);
        }
        return mean;
      }
    };

    abstract double mean(double[] values);
  }

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

      // in NaN naturally.
      sumOfProductsOfDeltas +=
          values.sumOfProductsOfDeltas()
              + (values.xStats().mean() - xStats.mean())
                  * (values.yStats().mean() - yStats.mean())
                  * values.count();
    }
    yStats.addAll(values.yStats());
  }

  /** Returns an immutable snapshot of the current statistics. */