Dec-01-2019, 04:39 PM

I'm studying this dataset: https://archive.ics.uci.edu/ml/datasets/...ower+Plant

These are the features:

- AT: Ambiant Temperature

- V: Exhaust Vacuum

- AP: Ambient Pressure

- RH: Relative Humidity

- PE: Energy Output (the label to predict)

We have many parameters such as:

- m: number of observations in the training set

- n: number of features (without the y offset)

- w: the vector of weights of your model (w0, w1, …., wn)

- x: an observation vector (features and offset x0=1 ) . Dimension : (n+1, 1)

- X: matrix of observations. Dimension : (m, n+1)

- y: label (‘answers’) vector . Dimension : (m, 1)

- cost: the cost function J

- delta: the stop condition

- iterations: max number of iterations of the gradient descent (default value = 1000)

- alpha: learning rate of the gradient descent (default value = 0.03)

So I initialize all the vectors first. However, how will I add values inside these vectors? How to do matrix multiplications and the transposed matrix in Python?

These are the features:

- AT: Ambiant Temperature

- V: Exhaust Vacuum

- AP: Ambient Pressure

- RH: Relative Humidity

- PE: Energy Output (the label to predict)

We have many parameters such as:

- m: number of observations in the training set

- n: number of features (without the y offset)

- w: the vector of weights of your model (w0, w1, …., wn)

- x: an observation vector (features and offset x0=1 ) . Dimension : (n+1, 1)

- X: matrix of observations. Dimension : (m, n+1)

- y: label (‘answers’) vector . Dimension : (m, 1)

- cost: the cost function J

- delta: the stop condition

- iterations: max number of iterations of the gradient descent (default value = 1000)

- alpha: learning rate of the gradient descent (default value = 0.03)

So I initialize all the vectors first. However, how will I add values inside these vectors? How to do matrix multiplications and the transposed matrix in Python?

w = array([]) x = array([]) X = array([]) y = array([])I don't really know what to do next.