Question

Need help completing code. (In python)

Problem 2. Prepare the data for the lab In this problem you'll split the dataset into a training dataset and a test dataset and store them into separate arrays to work with: (a) Create a training set dataTR consisting of the first 500 and last 500 rows of the original dataset and a set dataTS with the remaining (middle) 372 rows. (b) Create 2D numpy arrays X-tr with rows [1.zi(1), Zi(2) , Zi(3) , Zi(4)] with İ = 0, 1, . . . , 999( m,r = 1000 ) and X-t s with rows [1, zXI), zr2)丙(3)刑41 with j = 0, 1, . . . , 371 ( Tnts = 372). c) Create corresponding 1D arrays y tr and y ts with the labels y and y of the data samples. dataTRnp.zeros ( (1000, 5), 'double) print dataTR.shape, n' dataTr(0:500,:) = data(0:500,:] dataTR[500:1000,:data [872:1372,: print dataTR[497:503,:dataTR[497:503,'n print data[499:501,:1data[499:501,:, n print 'dataTR[997:1000,dataTR[997:1000,:, 'n dataTS# np . zeros((372, # keep going 5), 'double')

Answer 1

Assuming your original data is given in the form (initial columns have the values for inputs(x) and the last columns have the output(y) ).

Which is data[:,:-1] = Your x values

and

data[:,-1:] = Your y values

Solution in python.

#Part a of the answer

#Making dataTR and dataTS
dataTR = np.zeros((1000,5),'double')
dataTS = np.zeros((372,5),'double')

#Assigning Respective Values
dataTR[0:500,:] = data[0:500,:]
dataTR[500:1000,:] = data[872:1372,:]
dataTS[0:372,:] = data[500:872,:]

#Part b and c of the answer
#The questions wants you to split your newly created DataTR and DataTS into
#x_tr and x_ts.

#Part b
#Making X_tr and X_ts
X_tr = np.zeros((1000,5),'double')
X_ts = np.zeros((372,5),'double')

#Initiallizing X_tr and X_ts

#You want the first column to be a ones and rest input x.
#You will notice the last column is not taken from the dataTR because
# this column contains output values y.
X_tr[:,1:] = dataTR[:,:-1]
X_tr[:,0] = numpy.ones(1000,'double')

X_ts[:,1:] = dataTS[:,:-1]
X_ts[:,0] = numpy.ones(372,'double')

#Part c
#Making y_tr and y_ts
#This has 1-d array because the output has a single column.
y_tr = np.zeros((1000),'double')
y_ts = np.zeros((372),'double')

#Initiallizing y_tr and y_ts
#Here, the last column is taken into account because it has outputs.
#Using notation of -1.
y_tr[:] = dataTR[:,-1]
y_ts[:] = dataTS[:,-1]

Screenshot of Solution:

Outputs:

Confirming X_ts is in correct shape.I hope this satisfies you.Thanks for using Chegg.