fm.py 文件源码

def __init__(self, n_features=None, n_dim=8, lossfun=F.mean_squared_error,
                 lambda0=5e-3, lambda1=5e-3, lambda2=5e-3, init_bias=0.0,
                 intx_term=True, total_nobs=1):
        self.n_dim = n_dim
        self.n_features = n_features
        self.lossfun = lossfun
        self.lambda0 = lambda0
        self.lambda1 = lambda1
        self.lambda2 = lambda2
        self.intx_term = intx_term
        self.total_nobs = total_nobs

        # These are all the learned weights corresponding
        # to the overall bias, slope per feature, and latent
        # interaction vector per feature
        super(FM, self).__init__(bias=L.Bias(shape=(1,)),
                                 slope=L.EmbedID(n_features, 1),
                                 latent=L.EmbedID(n_features, n_dim))

        # Xavier initialize weights
        c = np.sqrt(n_features * n_dim)
        self.latent.W.data[...] = np.random.randn(n_features, n_dim) / c
        d = np.sqrt(n_features)
        self.slope.W.data[...] = np.random.randn(n_features, 1) / d
        self.bias.b.data[...] *= 0.0
        self.bias.b.data[...] += init_bias