def test_basic_creation(nps_app_inst):
import nums.numpy as nps
assert nps_app_inst is not None
ops = "empty", "zeros", "ones"
shape = (2, 3, 4)
for op in ops:
ba: BlockArray = nps.__getattribute__(op)(shape=shape)
if "zeros" in op:
assert nps.allclose(nps.zeros(shape), ba)
if "ones" in op:
assert nps.allclose(nps.ones(shape), ba)
ba2: BlockArray = nps.__getattribute__(op + "_like")(ba)
assert ba.shape == ba2.shape
assert ba.dtype == ba2.dtype
assert ba.block_shape == ba2.block_shape
def sample(app: ArrayApplication, sample_size):
X_train = nps.concatenate([
nps.random.randn(sample_size // 2, 2),
nps.random.randn(sample_size // 2, 2) + 2.0
],
axis=0)
y_train = nps.concatenate([
nps.zeros(shape=(sample_size // 2, ), dtype=nps.int),
nps.ones(shape=(sample_size // 2, ), dtype=nps.int)
],
axis=0)
# We augment X with 1s for intercept term.
X_train = app.concatenate([
X_train,
app.ones(shape=(X_train.shape[0], 1),
block_shape=(X_train.block_shape[0], 1),
dtype=X_train.dtype)
],
axis=1,
axis_block_size=X_train.block_shape[1] + 1)
return X_train, y_train
import nums
import nums.numpy as nps
from nums.models.glms import LogisticRegression
nums.init()
# Make dataset.
X1 = nps.random.randn(500, 1) + 5.0
y1 = nps.zeros(shape=(500, ), dtype=bool)
X2 = nps.random.randn(500, 1) + 10.0
y2 = nps.ones(shape=(500, ), dtype=bool)
X = nps.concatenate([X1, X2], axis=0)
y = nps.concatenate([y1, y2], axis=0)
# Train Logistic Regression Model.
model = LogisticRegression(solver="newton", tol=1e-8, max_iter=1)
model.fit(X, y)
y_pred = model.predict(X)
print("accuracy", (nps.sum(y == y_pred) / X.shape[0]).get())