NUMPY RANDOM PERMUTE VERIFICATION

randint ( 0, 10 )) perm_indices = random. randint ( 10, 1000 ) for i in range ( N ): X. shape num_of_samples2 = num_of_samples // 2 # integer division # x, y: x = z y = zeros (( num_of_samples2, dim )) # preallocation for m in range ( len ( ds )): idx = range ( cum_ds, cum_ds + ds ) y = z return x, yĭef test_split_data ( self ): X, y =, N = random. shape : raise Exception ( 'sum(ds) must be equal to z.shape in other ' + 'words the subspace dimensions do not sum to the' + ' total dimension!' ) # 0,d_1,d_1+d_2.,d_1+.+d_ starting indices of the subspaces: cum_ds = cumsum ( hstack (( 0, ds ))) num_of_samples, dim = z.

NUMPY RANDOM PERMUTE VERIFICATION

''' # verification (sum(ds) = z.shape): if sum ( ds ) != z. y : (number of samplesy, dimension)-ndarray Sample from the product of the marginals it is independent of x. Returns - x : (number of samplesx, dimension)-ndarray Samples from the joint. ds : int vector Dimension of the individual subspaces in z ds = i^th subspace dimension.

Parameters - z : (number of samples, dimension)-ndarray Sample points. Sigma return deltaĭef joint_and_product_of_the_marginals_split ( z, ds ): ''' Split to samples from the joint and the product of the marginals. lamL / a, c - b / a ) - c delta += u delta += u U += u * vary. Psi, U )) if i = j : u = - b / a delta += u U += u * vary. T ): if i > j : # seems ok since we look for upper triangular indices in active set continue if i = j : a = vary. Sigma ) for _ in range ( max_iter ): for i, j in rng. astype ( int ) train_imgs, train_masks, train_index = train_imgs, train_masks, train_index return train_imgs, train_masks, train_indexĭef lambda_newton_direction ( self, active, fixed, vary, max_iter = 1 ): # TODO we should be able to do a warm start. values )) final_ordering = final_ordering. iloc # shuffle users in batch final_ordering = np. loc = i, 'initial_order' ] # indexes of i'th image for each user after shuffling idx = idx. T ) #take all first images per subject and so on final_ordering = np. droplevel ( 0 ) #create new column with new subject image order ordering = 0 ordering = ordering. sort_index ( inplace = True, level = 'subject' ) #shuffle intra subject ordering = ordering. from_arrays (, train_index ], names = )) ordering. arange ( len ( train_index )), columns =, index = pd. Def train_ordering ( train_imgs, train_masks, train_index ): ordering = pd.

Numpy random permute