d493caa1278c158b78aa02c8f23d4f56c311f975,pymc3/variational/callbacks.py,CheckLossConvergence1,__call__,#CheckLossConvergence1#Any#Any#Any#,33

Before Change

    def __call__(self, approx, hist, i):
        if hist is None or i < self.window_size or i % self.every:
            return
        diff = ((hist[-self.window_size:] - hist[-self.window_size-1:-1])
                / hist[-self.window_size-1:-1])
        mean = diff.mean()
        // unbiased std of mean
        std = diff.std() / (self.window_size - 1)**.5
        t = abs(mean / std)
        p = stats.t.cdf(t, df=self.window_size) - .5
        // 1 - confidence is lower allowed p
        if p < self.critical:
            raise StopIteration

After Change

        current = self.flatten_shared(approx.params)
        delta = (current - self.prev)/self.prev
        self.prev = current
        delta[np.isnan(delta)] = 0
        norm = delta.dot(delta)**.5
        if norm < self.tolerance:
            raise StopIteration

Instances