C# Instance.valueSparse方法代码示例

		/// <summary> Merges this instance with the given instance and returns
		/// the result. Dataset is set to null.
		/// 
		/// </summary>
		/// <param name="inst">the instance to be merged with this one
		/// </param>
		/// <returns> the merged instances
		/// </returns>
		public override Instance mergeInstance(Instance inst)
		{
			
			double[] values = new double[numValues() + inst.numValues()];
			int[] indices = new int[numValues() + inst.numValues()];
			
			int m = 0;
			for (int j = 0; j < numValues(); j++, m++)
			{
				values[m] = valueSparse(j);
				indices[m] = index(j);
			}
			for (int j = 0; j < inst.numValues(); j++, m++)
			{
				values[m] = inst.valueSparse(j);
				indices[m] = numAttributes() + inst.index(j);
			}
			
			return new SparseInstance(1.0, values, indices, numAttributes() + inst.numAttributes());
		}

		/// <summary> Convert a single instance over. The converted instance is 
		/// added to the end of the output queue.
		/// 
		/// </summary>
		/// <param name="instance">the instance to convert
		/// </param>
		private void  convertInstance(Instance instance)
		{
			
			Instance inst = null;
			if (instance is SparseInstance)
			{
				double[] vals = new double[instance.numValues()];
				int[] indices = new int[instance.numValues()];
				int num = 0;
				for (int j = 0; j < instance.numValues(); j++)
				{
					if (instance.isMissingSparse(j) && (getInputFormat().classIndex() != instance.index(j)) && (instance.attributeSparse(j).Nominal || instance.attributeSparse(j).Numeric))
					{
						if (m_ModesAndMeans[instance.index(j)] != 0.0)
						{
							vals[num] = m_ModesAndMeans[instance.index(j)];
							indices[num] = instance.index(j);
							num++;
						}
					}
					else
					{
						vals[num] = instance.valueSparse(j);
						indices[num] = instance.index(j);
						num++;
					}
				}
				if (num == instance.numValues())
				{
					inst = new SparseInstance(instance.weight(), vals, indices, instance.numAttributes());
				}
				else
				{
					double[] tempVals = new double[num];
					int[] tempInd = new int[num];
					Array.Copy(vals, 0, tempVals, 0, num);
					Array.Copy(indices, 0, tempInd, 0, num);
					inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes());
				}
			}
			else
			{
				double[] vals = new double[getInputFormat().numAttributes()];
				for (int j = 0; j < instance.numAttributes(); j++)
				{
					if (instance.isMissing(j) && (getInputFormat().classIndex() != j) && (getInputFormat().attribute(j).Nominal || getInputFormat().attribute(j).Numeric))
					{
						vals[j] = m_ModesAndMeans[j];
					}
					else
					{
						vals[j] = instance.value_Renamed(j);
					}
				}
				inst = new Instance(instance.weight(), vals);
			}
			inst.Dataset = instance.dataset();
			push(inst);
		}

		/// <summary> Computes the inner product of two instances</summary>
		private double innerProduct(Instance i1, Instance i2)
		{
			
			// we can do a fast dot product
			double result = 0;
			int n1 = i1.numValues(); int n2 = i2.numValues();
			int classIndex = m_Train.classIndex();
			for (int p1 = 0, p2 = 0; p1 < n1 && p2 < n2; )
			{
				int ind1 = i1.index(p1);
				int ind2 = i2.index(p2);
				if (ind1 == ind2)
				{
					if (ind1 != classIndex)
					{
						result += i1.valueSparse(p1) * i2.valueSparse(p2);
					}
					p1++; p2++;
				}
				else if (ind1 > ind2)
				{
					p2++;
				}
				else
				{
					p1++;
				}
			}
			result += 1.0;
			
			if (m_Exponent != 1)
			{
				return System.Math.Pow(result, m_Exponent);
			}
			else
			{
				return result;
			}
		}