def calc_diff_map(dist_mat, dims=2, factor=2): sigma = numpy.median(dist_mat)/factor diff_kernal = numpy.exp(-(dist_mat ** 2) / (2 * sigma ** 2)) row_sum = numpy.sum(diff_kernal, axis=1) normlized_kernal = numpy.dot(numpy.diag(1 / row_sum), diff_kernal) U, S, V = numpy.linalg.svd(normlized_kernal) return U[:, 1:dims+1].T转matlab

在 MATLAB 中，你可以使用以下代码将给定的 Python 函数 `calc_diff_map` 转换为 MATLAB 语法：

function U = calc_diff_map(dist_mat, dims, factor)
    sigma = median(dist_mat(:)) / factor;
    diff_kernel = exp(-(dist_mat .^ 2) / (2 * sigma ^ 2));
    row_sum = sum(diff_kernel, 2);
    normalized_kernel = (1 ./ row_sum) .* diff_kernel;
    [U, ~, ~] = svd(normalized_kernel);
    U = U(:, 1:dims)';
end

在 MATLAB 中，函数定义使用 `function` 关键字，并且在函数名称后面跟上输入参数。在这个例子中，输入参数包括 `dist_mat`（距离矩阵）、`dims`（维度）和 `factor`（因子）。使用 `:` 运算符将 `dist_mat` 转换为向量，并使用 `median` 函数计算中位数。使用 `exp` 函数计算指数，使用 `sum` 函数计算行和。通过逐元素除法和点乘运算来计算归一化的核矩阵。函数 `svd` 返回的第一个输出参数是特征向量矩阵 `U`，我们选择其中的前 `dims` 列，并将其转置以匹配 Python 版本的输出格式。最后，返回结果矩阵 `U`。

相关问题

转成matlab： def _apply_linesearch_optimzation(self, update_embedding_with, grad, calc_loss, loss, **kwargs): self.eta = self.eta_max if kwargs.get('first_iter',False) and not self.linesearch_first: self.eta = kwargs.get('eta_first',1) loss_diff = 1 while loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) if self.eta <= self.eta_min and loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) loss_diff = -1 self.eta *= 2 update_embedding_with(new_embedding=temp_embedding) return delta def _linesearch_once(self, update_embedding_with, grad, calc_loss, loss, **kwargs): delta = self._calc_delta(grad) temp_embedding = update_embedding_with(delta=delta,copy=True) loss_diff = calc_loss(temp_embedding) - loss self.eta /= 2 return loss_diff, temp_embedding, delta

function delta = _apply_linesearch_optimzation(self, update_embedding_with, grad, calc_loss, loss, varargin)
eta = self.eta_max;
if nargin > 4 && varargin{1} && ~self.linesearch_first
eta = varargin{2};
end
loss_diff = 1;
while loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with, grad, calc_loss, loss, varargin{:});
if eta <= self.eta_min && loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with, grad, calc_loss, loss, varargin{:});
loss_diff = -1;
end
end
eta = eta * 2;
update_embedding_with('new_embedding', temp_embedding);
end

function [loss_diff, temp_embedding, delta] = _linesearch_once(self, update_embedding_with, grad, calc_loss, loss, varargin)
delta = self._calc_delta(grad);
temp_embedding = update_embedding_with('delta', delta, 'copy', true);
loss_diff = calc_loss(temp_embedding) - loss;
self.eta = self.eta / 2;
end

转成matlab：self.eta = self.eta_max if kwargs.get('first_iter',False) and not self.linesearch_first: self.eta = kwargs.get('eta_first',1) loss_diff = 1 while loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) if self.eta <= self.eta_min and loss_diff > 0: loss_diff, temp_embedding, delta = self._linesearch_once( update_embedding_with,grad,calc_loss,loss,**kwargs) loss_diff = -1 self.eta *= 2 update_embedding_with(new_embedding=temp_embedding) return delta

self.eta = self.eta_max;
if isfield(kwargs,'first_iter') && kwargs.first_iter && ~self.linesearch_first
self.eta = kwargs.eta_first;
end
loss_diff = 1;
while loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with,grad,calc_loss,loss,kwargs);
if self.eta <= self.eta_min && loss_diff > 0
[loss_diff, temp_embedding, delta] = self._linesearch_once(update_embedding_with,grad,calc_loss,loss,kwargs);
loss_diff = -1;
end
end
self.eta = self.eta * 2;
update_embedding_with('new_embedding',temp_embedding);
end_return = delta;