Restricted Boltzmann machine confusing details Hi I'm new to RBM and machine learning/computer vision in general, and I'm using the code from: http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/ while reading on https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine for some theories. But right now i'm totally confused. In the code there is a part: 

# Calculate the activations of the hidden units.
      hidden_activations = np.dot(visible, self.weights)      
      # Calculate the probabilities of turning the hidden units on.
      hidden_probs = self._logistic(hidden_activations)


I assume the equivalence in wikipedia would be the conditional probability, but when I look that up, nothing makes sense! I mean, what are v and h, are they supposed to be vectors or values? (I assume they are in fact just values, as wikipedia only talks about each seperate samples while the code groups all of them into a matrix, but even them it's still hazy :( ). I'm new to this so my question may be a little dumb but please help me. Thank you very much!
P/s: Sorry I am unable to post the equations :(
The equation from the wikipedia implemented with this code is the one in picture bellow. Sigma function is logistic sigmoid (self._logistic()). The dot product is the sum of products bellow. bj is offset and might be included in the sum (some visible neuron is always 1) or it might be omitted completely for simplicity or something else.