def make_dataset(num_batches):
m = num_batches*batch_size
X = rng.randn(m, num_features)
y = rng.randn(m, num_features)
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
return rval
def make_dataset(num_batches):
m = num_batches * batch_size
X = rng.randn(m, num_features)
y = rng.randn(m, num_features)
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
return rval
def make_dataset(num_batches):
disturb_mem.disturb_mem()
m = num_batches*batch_size
X = rng.randn(m, num_features)
y = np.zeros((m,1))
y[:,0] = np.dot(X, w) > 0.
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
X = rval.get_batch_design(batch_size)
assert X.shape == (batch_size, num_features)
return rval
def make_dataset(num_batches):
disturb_mem.disturb_mem()
m = num_batches * batch_size
X = rng.randn(m, num_features)
y = np.zeros((m, 1))
y[:, 0] = np.dot(X, w) > 0.
rval = DenseDesignMatrix(X=X, y=y)
rval.yaml_src = "" # suppress no yaml_src warning
X = rval.get_batch_design(batch_size)
assert X.shape == (batch_size, num_features)
return rval
