擅长:python、mysql、java
<p>在<code>gradiantDescent</code>函数中尝试以下操作:</p>
<pre class="lang-py prettyprint-override"><code>for _ in range(num_itr):
theta = theta - (alpha / m) * np.dot(X.T, (np.dot(X, theta) - y))
J_history.append(costFunction(theta, X, y))
return theta, J_history
</code></pre>
<p>您会得到一个<code>nan</code>值,因为某些计算出错</p>