def test_NaiveGraphNet_classification( n, p, num_obs_class1 = 100, num_obs_class2 = 100 ):
"""
Test the naive graphnet on a binary classification problem
"""
# classification data
class_data = classification_dataset( n, p, num_obs_class1, num_obs_class2, SNR = 1, corr1 = 0.9, corr2 = None )
X = class_data[1]
y = class_data[0]
#Prepare adjacency matrix from "mask" of X (usually we'd get it from a mask file, see mask.py)
Adj = prepare_adj(1+0.*X,0,1,1,0)
1/0
l1 = 50.
l2 = 5.
l3 = 30.
delta = 100.
#Run optimization
l = cwpath.CoordWise((X, y, Adj), graphnet.GraphNet ) #, initial_coefs=np.random.normal(0,1,81))
l.problem.assign_penalty(l1=l1,l2=l2,l3=l3)#,delta=delta)
l.fit()
beta =np.round(1000*l.problem.coefficients)/1000
print beta
n = 25
#Generate toy data: 3x3x3x3 data on n trials
X_orig = np.random.normal(0,1,n*3**4).reshape((n,3,3,3,3))
#Reshape X to 2 dimensions
p = np.prod(X_orig.shape[1:])
X = np.zeros((n,p))
for i in range(n):
X[i,:] = X_orig[i].reshape((1,p))
beta = np.append(5*np.ones(10),np.zeros(71))
Y = np.dot(X,beta)
#Prepare adjacency matrix from "mask" of X (usually we'd get it from a mask file, see mask.py)
Adj = prepare_adj(1+0.*X_orig[0],0,1,1,1)
l1 = 50.
l2 = 5.
l3 = 30.
delta = 100.
#Run optimization
l = cwpath.CoordWise((X, Y, Adj), graphnet.GraphNet, initial_coefs=np.random.normal(0,1,81))
l.problem.assign_penalty(l1=l1,l2=l2,l3=l3)#,delta=delta)
l.fit()
beta =np.round(1000*l.problem.coefficients)/1000
print beta
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