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diff --git a/hw3/2.py b/hw3/2.py
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+++ b/hw3/2.py
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+from numpy.random import normal
+import numpy as np
+from pickle import load
+from random import shuffle
+
+
+def init_vectors(K, sigma=0.01):
+    U = normal(0, sigma, size=(n+1, K))
+    V = normal(0, sigma, size=(m+1, K))
+    return U, V
+
+
+def init_vectors_bis(K, sigma=0.01):
+    U = normal(0, sigma, size=(n+1, K))
+    V = normal(0, sigma, size=(m+1, K))
+    a = np.ones(n+1)
+    b = np.ones(m+1)
+    g = 1
+    return U, V, a, b, g
+
+
+def sgd_step(U, V, lamb=0.05, sigmas=1.0):
+    keys = train.keys()
+    shuffle(keys)
+    for (i, j) in keys:
+        r = train[(i, j)]
+        ui = np.copy(U[i])
+        vj = np.copy(V[j])
+        a = (lamb / sigmas) * (float(r) - np.inner(ui, vj))
+        U[i] += a * vj
+        V[j] += a * ui
+
+
+def sgd_step_bis(U, V, a, b, g, lamb=0.05, sigmas=1.0):
+    keys = train.keys()
+    shuffle(keys)
+    for (i, j) in keys:
+        r = train[(i, j)]
+        ui = np.copy(U[i])
+        vj = np.copy(V[j])
+        e = (lamb / sigmas) * (float(r) - np.inner(ui, vj) - a[i] - b[j] - g)
+        U[i] += e * vj
+        V[j] += e * ui
+        a[i] += e
+        b[j] += e
+        g += e
+    return g
+
+
+def mse(U, V, d):
+    serror = sum((float(r) - np.inner(U[i], V[j])) ** 2
+                 for (i, j), r in d.iteritems())
+    return serror / len(d)
+
+
+def mse_bis(U, V, a, b, g, d):
+    serror = sum((float(r) - np.inner(U[i], V[j]) - a[i] - b[j] - g) ** 2
+                 for (i, j), r in d.iteritems())
+    return serror / len(d)
+
+
+def part_c():
+    for K in xrange(1, 11):
+        U, V = init_vectors(K)
+        for t in xrange(10):
+            sgd_step(U, V)
+        print K, mse(U, V, train), mse(U, V, test)
+
+
+def part_d():
+    U, V, a, b, g = init_vectors_bis(2)
+    for t in xrange(10):
+        print t
+        g = sgd_step_bis(U, V, a, b, g)
+    print g
+    i = range(len(b))
+    mi = min(i, key=lambda x: b[x])
+    ma = max(i, key=lambda x: b[x])
+    print mi, b[mi]
+    print ma, b[ma]
+    print mse_bis(U, V, a, b, g, train), mse_bis(U, V, a, b, g, test)
+
+
+if __name__ == "__main__":
+    n, m, train, test = load(open("data.pickle", "rb"))
+    part_d()