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Updating README code example to match implementation #1

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14 changes: 8 additions & 6 deletions README.md
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Expand Up @@ -31,6 +31,8 @@ and an (optional) list of missing entries.
Consider a data table A that is approximately rank k, where the first n1 columns
contain Boolean data, and the next n2 columns contain numerical data.

from numpy.random import randn
from numpy import sign
m, n1, n2, k = 50, 25, 25, 5
eta = 0.1 # noise
A = randn(m,k).dot(randn(k,n1+n2)) + eta*randn(m,n1+n2)
Expand Down Expand Up @@ -63,9 +65,9 @@ For example, if a 4x4 block of data is missing from the center of A,
this corresponds to rows 24-27 and columns 49-50 of submatrix 1,
and rows 24-27 and columns 1-2 of submatrix 2. (Python is 0-indexed.)

missing1 = [(23, 48), (23, 49), (24, 48), (24, 49), \
(25, 48), (25, 49), (26, 48), (26, 49)]
missing2 = [(23, 0), (23, 1), (24, 0), (24, 1), \
missing1 = [(23, 23), (23, 24), (24, 23), (24, 24),
(25, 23), (25, 24), (26, 23), (26, 24)]
missing2 = [(23, 0), (23, 1), (24, 0), (24, 1),
(25, 0), (25, 1), (26, 0), (26, 1)]
missing_list = [missing1, missing2]

Expand All @@ -76,12 +78,12 @@ that no entries are missing.
of the alternating minimization algorithm, create a Convergence object to pass
to the model. The default parameter values are shown below.

from glrm.util import Convergence
from glrm.convergence import Convergence
c = Convergence(TOL = 1e-3, max_iters = 1000)

All that remains is to initialize the GLRM model and call fit().

model = GLRM(A_list, loss_list, regX, regY, k, missing = missing_list, converge = c)
model = GLRM(A_list, loss_list, regX, regY, k, missing_list = missing_list, converge = c)
model.fit()

To extract the factors X, Y and impute missing values,
Expand Down Expand Up @@ -109,7 +111,7 @@ To view convergence history,
- LinearReg
- QuadraticReg

## Developing loss functions and regularizers (not guaranteed to work yet)
## Developing loss functions and regularizers (not implemented yet)

- FractionalLoss
- NonnegativeReg
Expand Down