Which one would YOU pick? I would pick: double function(const vector<double> &args); Or if it must be from your examples, then: double function(double * args); The reason being that I don't believe unrolling the arguments like that adds any noticeable speedup, but it does make the code look ugly....

ruby-on-rails,ruby,matrix,gradient-descent

Ruby actually has a built in matrix library. You can use it by calling require 'matrix' You create a vector object and take the inner product of it. For example, let's create two vectors a = Vector[1,3,-5] b = Vector[4,-2,-1] a.inner_product(b) => 3 ...

r,function,statistics,regression,gradient-descent

OK, by the numbers: In your call to gr.ascent(...) you pass a function, MMSE as the first argument. Inside gr.ascent(...) you refer to this function as df(...). The function MMSE(...) has 2 arguments, b0 and b1 for which there are no defaults - so these must be specified or there...

machine-learning,gradient-descent

Mathematically, we are trying here to minimise error function Error(θ) = Σ(yi - h(xi))^2 summation over i. To minimise error, we do d(Error(θ))/dθi = Zero putting h(xi) = Σ(θi*xi) summation over i and derive the above formula. The rest of the formulation can be reasoned as Gradient descent uses the...

You used the stopping criterion old parameters - new parameters <= 0.0000001 First of all I think there's an abs() missing if you want to use this criterion (though my ignorance of R may be at fault). But even if you use abs(old parameters - new parameters) <= 0.0000001 this...

neural-network,backpropagation,gradient-descent

If you have 5 hidden layers (assuming with 100 nodes each) you have 5 * 100^2 weights (assuming the bias node is included in the 100 nodes), not 100^5 (because there are 100^2 weights between two consecutive layers). If you use gradient descent, you'll have to calculate the contribution of...

regression,matlab,gradient-descent

Not sure I'm following your logic, but it's quite obvious that 'e' (the error) should not be squared. Let's see what you should be using. theta is a column vector of unknowns, y is a column vector of measurements and X is the model matrix where each row is an...

python-2.7,exception-handling,args,kwargs,gradient-descent

It might help you to understand if we replace the variable names step by step: target_fn = safe(target_fn) means that f in safe is target_fn: def safe(target_fn): def safe_f(*args, **kwargs): try: return target_fn(*args, **kwargs) except: return float('inf') return safe_f and: target_fn = safe_f i.e. we replace the function originally bound...