C++ Tensor::At方法代码示例

void Tensor::WeightedAdd(Tensor& t1, Tensor& t2, double weight_t1, double weight_t2)
{
	assert(t1.NumElements() == t2.NumElements());

	for (int i = 0; i < t1.NumElements(); ++i)
	{
		t1.Set(i, weight_t1 * t1.At(i) + weight_t2 * t2.At(i));
	}
}

// add two tensors, store result in result_tensor which is assumed 
// to be uninitialized
void Tensor::Add(Tensor& t1, Tensor& t2)
{
	assert(t1.NumElements() == t2.NumElements());

	for (int i = 0; i < t1.NumElements(); ++i)
	{
		t1.Set(i, t1.At(i) + t2.At(i));
	}
}

// inner product among two tensors of the same size
double Tensor::InnerProduct(Tensor& t1, Tensor& t2)
{
	assert(t1.NumElements() == t2.NumElements());
	double val = 0;
	for (int i = 0; i < t1.NumElements(); ++i)
	{
		val += t1.At(i) * t2.At(i);
	}
	return val;
}

double Tensor::Diff(Tensor& A, Tensor& B)
{
	assert(A.NumElements() == B.NumElements());

	double diff = 0;
	for (int i = 0; i < A.NumElements(); ++i)
	{
		diff += abs(A.At(i) - B.At(i));
	}
	return diff;
}

bool Tensor::Equals(Tensor& A, Tensor& B)
{
	assert(A.NumElements() == B.NumElements());

	for (int i = 0; A.NumElements(); ++i)
	{
		if (A.At(i) != B.At(i))
			return false;
	}
	return true;
}

void Tensor::Divide(Tensor& result_tensor, Tensor& t1, double val)
{
	result_tensor.Initialize(t1.Dims());

	for (int i = 0; i < t1.NumElements(); ++i)
	{
		result_tensor.Set(i, t1.At(i) / val);
	}
}

void Tensor::Rearrange(Tensor& result_tensor, Tensor& orig_tensor, vector<int>& old_to_new_modes)
{
	vector<int> new_dims;
	VectorPlus::Rearrange(new_dims, orig_tensor.Dims(), old_to_new_modes);

	result_tensor.Initialize(new_dims);

	int i = 0;
	FastIndexer indexer(orig_tensor.Dims());
	while (indexer.HasNext())
	//for (int i = 0; i < orig_tensor.NumElements(); ++i)
	{
		vector<int>& orig_indices = indexer.GetNext();
//		vector<int> orig_indices;
//		orig_tensor.ComputeIndexArray(orig_indices, i);
		vector<int> new_indices;
		VectorPlus::Rearrange(new_indices, orig_indices, old_to_new_modes);

		result_tensor.Set(new_indices, orig_tensor.At(i++));
	}
}

	double At(int index) {return prob_tensor->At(index); }

// multiply two tensors, store result in result_tensor which is assumed to be uninitialized
void Tensor::Multiply(Tensor& result_tensor, Tensor& t1, Tensor& t2, vector<int>& mult_modes1, vector<int>& mult_modes2)
{
	assert(mult_modes1.size() == mult_modes2.size());

	if (t1.Order() == mult_modes1.size() && t2.Order() == mult_modes2.size())
	{
		double val = InnerProduct(t1, t2);
		vector<int> fake_dims;
		result_tensor.Initialize(fake_dims);
		result_tensor.Set(0, val);
		return;
	}

	int numMultElements = 1;	
	vector<int> mult_dims(mult_modes1.size(), 0);

	for (int i = 0; i < mult_modes1.size(); ++i) 
	{
		assert(t1.Dim(mult_modes1[i]) == t2.Dim(mult_modes2[i]));
		mult_dims[i] = t1.Dim(mult_modes1[i]);
		numMultElements = numMultElements * mult_dims[i];
	}
	vector<int> mult_offsets;
	ComputeOffsets(mult_offsets, mult_dims);
	int result_order = t1.Order() + t2.Order() - mult_modes1.size() - mult_modes2.size(); 

	if (result_order == 0)
		assert(0);

	vector<int> result_dims;

	vector<int> free_modes1;
	vector<int> free_modes2;


	// find free indices from t1
	for (int i = 0; i < t1.Order(); ++i)
	{
		if (!VectorPlus::Contains(mult_modes1, i))
		{
			result_dims.push_back(t1.Dim(i));
			free_modes1.push_back(i);
		}
	}

	// find free indices from t2
	for (int i = 0; i < t2.Order(); ++i)
	{
		if (!VectorPlus::Contains(mult_modes2, i))
		{
			result_dims.push_back(t2.Dim(i));
			free_modes2.push_back(i);
		}
	}

	// initialize result_tensor
	result_tensor.Initialize(result_dims);

	// fill in elements from result tensor

	FastIndexer result_indexer(result_dims);

	for (int n = 0; n < result_tensor.NumElements(); ++n)
	{
		vector<int>& indices = result_indexer.GetNext();
		vector<int> free_indices1;
		vector<int> free_indices2;
	//	result_tensor.ComputeIndexArray(indices, n);

		for (int i = 0; i < result_tensor.Order(); ++i)
		{
			if (i < free_modes1.size())
				free_indices1.push_back(indices[i]);
			else
				free_indices2.push_back(indices[i]);
		}

		// sum over elementwise products of mult-mode elements
		double temp_sum = 0;
		FastIndexer mult_indexer(mult_dims);
		for (int k = 0; k < numMultElements; ++k)
		{
			vector<int>& mult_indices = mult_indexer.GetNext();
		//	ComputeIndexArray(mult_indices, mult_offsets, k);

			vector<int> indices1; 
			vector<int> indices2;

			MergeIndices(indices1, mult_modes1, free_modes1, mult_indices, free_indices1);
			MergeIndices(indices2, mult_modes2, free_modes2, mult_indices, free_indices2);

			temp_sum += t1.At(indices1) * t2.At(indices2);
		}

		result_tensor.Set(n, temp_sum);
	}
}

void Tensor::ElementwiseMultiply(Tensor& result_tensor, Tensor& t1, Tensor& t2, vector<int>& mult_modes1, vector<int>& mult_modes2)
{

	assert(mult_modes1.size() == mult_modes2.size());


	int numMultElements = 1;	
	vector<int> mult_dims(mult_modes1.size(), 0);

	for (int i = 0; i < mult_modes1.size(); ++i) 
	{
		assert(t1.Dim(mult_modes1[i]) == t2.Dim(mult_modes2[i]));
		mult_dims[i] = t1.Dim(mult_modes1[i]);
		numMultElements = numMultElements * mult_dims[i];
	}
	vector<int> mult_offsets;
	ComputeOffsets(mult_offsets, mult_dims);
	int result_order = t1.Order() + t2.Order() - mult_modes2.size(); 

	if (result_order == 0)
		assert(0);

	vector<int> result_dims;

	vector<int> free_modes1;
	vector<int> free_modes2;


	// find free indices from t1
	for (int i = 0; i < t1.Order(); ++i)
	{
		if (!VectorPlus::Contains(mult_modes1, i))
		{
			free_modes1.push_back(i);
		}
	}

	// find free indices from t2
	for (int i = 0; i < t2.Order(); ++i)
	{
		if (!VectorPlus::Contains(mult_modes2, i))
		{
			free_modes2.push_back(i);
		}
	}

	int curr_index = 0; 
	for (int i = 0; i < result_order; ++i)
	{
		if (i < t1.Order())
		{
			result_dims.push_back(t1.Dim(i));
		}
		else
		{
			result_dims.push_back(t2.Dim(free_modes2[curr_index++]));
		}
	}



	// initialize result_tensor
	result_tensor.Initialize(result_dims);

	// fill in elements from result tensor
	FastIndexer indexer(result_dims);
	int n = 0;
	while (indexer.HasNext())
	{
		vector<int>& indices = indexer.GetNext();
		vector<int> indices1(t1.Order(), 0);
		vector<int> free_indices2;
	//	result_tensor.ComputeIndexArray(indices, n);

		for (int i = 0; i < result_tensor.Order(); ++i)
		{
			if (i < t1.Order())
				indices1[i] = indices[i];
			else
				free_indices2.push_back(indices[i]);
		}

		
		vector<int> mult_indices;
		VectorPlus::Subset(mult_indices, indices1, mult_modes1);


		vector<int> indices2;
		MergeIndices(indices2, mult_modes2, free_modes2, mult_indices, free_indices2);

		double val = 0;
		double val1 = t1.At(indices1);
		if (val1 != 0)
		{
			val = val1 * t2.At(indices2);
		}
		result_tensor.Set(n++, val);
	}
}

void Tensor::CreateLinearSystem(vector<double>& B_vec, Matrix& A_matrix, 
						Tensor& X, Tensor& A, Tensor& B,
						vector<int>& mult_modesX, vector<int>& mult_modesA)
{
	// fake multiply x and A together to create B, creating the linear system in the process

	assert(mult_modesX.size() == mult_modesA.size());

	if (X.Order() == mult_modesX.size() && A.Order() == mult_modesA.size())
	{
		assert(0);
	}

	int numMultElements = 1;	
	vector<int> mult_dims(mult_modesX.size(), 0);

	for (int i = 0; i < mult_modesX.size(); ++i) 
	{
		assert(X.Dim(mult_modesX[i]) == A.Dim(mult_modesA[i]));
		mult_dims[i] = X.Dim(mult_modesX[i]);
		numMultElements = numMultElements * mult_dims[i];
	}
	vector<int> mult_offsets;
	ComputeOffsets(mult_offsets, mult_dims);
	int result_order = X.Order() + A.Order() - mult_modesX.size() - mult_modesA.size(); 

	if (result_order == 0)
		assert(0);

	vector<int> result_dims;

	vector<int> free_modesX;
	vector<int> free_modesA;


	// find free indices from X
	for (int i = 0; i < X.Order(); ++i)
	{
		if (!VectorPlus::Contains(mult_modesX, i))
		{
			free_modesX.push_back(i);
		}
	}

	// find free indices from A
	for (int i = 0; i < A.Order(); ++i)
	{
		if (!VectorPlus::Contains(mult_modesA, i))
		{
			free_modesA.push_back(i);
		}
	}
	vector<int> a_mat_dims = VectorPlus::CreatePair(B.NumElements(), X.NumElements());
	A_matrix.Initialize(a_mat_dims);
	B_vec.reserve(B.NumElements());
	// fill in elements from result tensor

	FastIndexer B_indexer(B.Dims());

	for (int n = 0; n < B.NumElements(); ++n)
	{
		B_vec.push_back(B.At(n));

		vector<int>& indices = B_indexer.GetNext();
		vector<int> free_indicesX;
		vector<int> free_indicesA;
	//	B.ComputeIndexArray(indices, n);

		for (int i = 0; i < B.Order(); ++i)
		{
			if (!VectorPlus::Contains(mult_modesX, i))
				free_indicesX.push_back(indices[i]);
			else
				free_indicesA.push_back(indices[i]);
		}

		// sum over elementwise products of mult-mode elements
		double temp_sum = 0;
		FastIndexer mult_indexer(mult_dims);
		for (int k = 0; k < numMultElements; ++k)
		{
			vector<int>& mult_indices = mult_indexer.GetNext();
		//	ComputeIndexArray(mult_indices, mult_offsets, k);

			vector<int> indicesX; 
			vector<int> indicesA;

			MergeIndices(indicesX, mult_modesX, free_modesX, mult_indices, free_indicesX);
			MergeIndices(indicesA, mult_modesA, free_modesA, mult_indices, free_indicesA);

			
			A_matrix.Set(n, X.ComputeIndex(indicesX), A.At(indicesA));
		}
	}
}