def test_explicit_init():
import nums
import nums.core.application_manager as am
nums.init()
assert am.is_initialized()
am.destroy()
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())
import ray
import nums
import nums.numpy as nps
# Initialize ray and connect it to the cluster.
ray.init(address="auto")
# Initialize nums with the cluster shape. Here we set it to use all the nodes in the ray cluster.
nums.init(cluster_shape=(len(ray.nodes()), 1))
def main():
X = nps.random.rand(10**4)
Y = nps.random.rand(10**4)
Z = nps.add(X, Y)
print("X + Y = ", Z.get())
if __name__ == "__main__":
main()
