Tell HN: ChatGPT-4 vs. GPT-4 API Code Capabilities Are Drastically Different
----------<p>ChatGPT-4 created a function as follows:<p>```
def calculate_df(beta, t):
    # the variables are discrete and uniformly spread between 0 and 1
    variables = np.linspace(0, 1, v_res)<p><pre><code>    # create a grid for customers
    theta, x = np.meshgrid(np.linspace(0, 1, c_res), np.linspace(0, 1, c_res))
    
    # extend customer grid to include all combinations of variables
    theta, x, ξ1, ξ2, p1, p2, q1, q2 = np.meshgrid(theta, x, variables, variables, variables, variables, variables, variables)

    # calculate utilities for all combinations
    u1 = -p1 + theta*q1 - t*(1-ξ1)*np.abs(x - 0.5)
    u2 = -p2 + theta*q2 - t*(1-ξ2)*np.abs(x - 0.5)

    # determine purchase decisions for all combinations
    decisions = np.where(u1 &gt; u2, 1, np.where(u1 &lt; u2, 2, 0.5))

    # count the number of customers for each firm for all combinations
    firm1_customers = np.count_nonzero(decisions == 1, axis=(0, 1)) + 0.5*np.count_nonzero(decisions == 0.5, axis=(0, 1))
    firm2_customers = np.count_nonzero(decisions == 2, axis=(0, 1)) + 0.5*np.count_nonzero(decisions == 0.5, axis=(0, 1))
    d1 = firm1_customers &#x2F; (c_res * c_res)
    d2 = firm2_customers &#x2F; (c_res * c_res)

    # calculate firm profits for all combinations
    pi1 = p1*d1 - beta*q1**2
    pi2 = p2*d2 - beta*q2**2

    # reshape results into 1D arrays and zip into a list
    data = list(zip(ξ1.ravel(), ξ2.ravel(), p1.ravel(), p2.ravel(), q1.ravel(), q2.ravel(), pi1.ravel(), pi2.ravel(), d1.ravel(), d2.ravel(), [decisions[:,:,i,j] for i,j in np.ndindex(decisions.shape[2:])]))

    # convert data to dataframe
    df = pd.DataFrame(
        data, columns=[&#x27;ξ1&#x27;, &#x27;ξ2&#x27;, &#x27;p1&#x27;, &#x27;p2&#x27;, &#x27;q1&#x27;, &#x27;q2&#x27;, &#x27;pi1&#x27;, &#x27;pi2&#x27;, &#x27;d1&#x27;, &#x27;d2&#x27;, &#x27;decisions&#x27;])
    return df</code></pre>
```<p>When running this via `calculate_df(1,1)`, I got the following error:<p>```
ValueError                                Traceback (most recent call last)
Input In [140], in ()
     84     df = pd.DataFrame(
     85         data, columns=[&#x27;ξ1&#x27;, &#x27;ξ2&#x27;, &#x27;p1&#x27;, &#x27;p2&#x27;, &#x27;q1&#x27;, &#x27;q2&#x27;, &#x27;pi1&#x27;, &#x27;pi2&#x27;, &#x27;d1&#x27;, &#x27;d2&#x27;, &#x27;decisions&#x27;])
     86     return df
---&gt; 88 calculate_df(1, 1)<p>Input In [140], in calculate_df(beta, t)
     75 pi2 = p2<i>d2 - beta</i>q2*2
     77 # reshape results into 1D arrays and zip into a list
     78 data = list(zip(
     79     ξ1.ravel(), ξ2.ravel(), p1.ravel(), p2.ravel(), q1.ravel(),
     80     q2.ravel(), pi1.ravel(), pi2.ravel(), d1.ravel(), d2.ravel(),
---&gt; 81     [decisions[:, :, i, j] for i, j in np.ndindex(decisions.shape[2:])]))
     83 # convert data to dataframe
     84 df = pd.DataFrame(
     85     data, columns=[&#x27;ξ1&#x27;, &#x27;ξ2&#x27;, &#x27;p1&#x27;, &#x27;p2&#x27;, &#x27;q1&#x27;, &#x27;q2&#x27;, &#x27;pi1&#x27;, &#x27;pi2&#x27;, &#x27;d1&#x27;, &#x27;d2&#x27;, &#x27;decisions&#x27;])<p>Input In [140], in (.0)
     75 pi2 = p2<i>d2 - beta</i>q2*2
     77 # reshape results into 1D arrays and zip into a list
     78 data = list(zip(
     79     ξ1.ravel(), ξ2.ravel(), p1.ravel(), p2.ravel(), q1.ravel(),
     80     q2.ravel(), pi1.ravel(), pi2.ravel(), d1.ravel(), d2.ravel(),
---&gt; 81     [decisions[:, :, i, j] for i, j in np.ndindex(decisions.shape[2:])]))
     83 # convert data to dataframe
     84 df = pd.DataFrame(
     85     data, columns=[&#x27;ξ1&#x27;, &#x27;ξ2&#x27;, &#x27;p1&#x27;, &#x27;p2&#x27;, &#x27;q1&#x27;, &#x27;q2&#x27;, &#x27;pi1&#x27;, &#x27;pi2&#x27;, &#x27;d1&#x27;, &#x27;d2&#x27;, &#x27;decisions&#x27;])<p>ValueError: too many values to unpack (expected 2)
```<p>Here&#x27;s where things became interesting:<p>1. ChatGPT-4&#x27;s attempt at correcting&#x2F;debugging the code:<p>&quot;&quot;&quot;
I apologize for the confusion. It appears I made an error in creating the indices for decisions. It should be 4 dimensions instead of 2 because we have ξ1, ξ2, p1, p2, q1, q2 which are six variables and we used np.meshgrid() function for these six variables, so the decision should have dimensions matching them.<p>Please try this code:<p>```
def calculate_df(beta, t):
    # the variables are discrete and uniformly spread between 0 and 1
    variables = np.linspace(0, 1, v_res)<p><pre><code>    # create a grid for customers
    theta, x = np.meshgrid(np.linspace(0, 1, c_res), np.linspace(0, 1, c_res))
    
    # extend customer grid to include all combinations of variables
    theta, x, ξ1, ξ2, p1, p2, q1, q2 = np.meshgrid(theta, x, variables, variables, variables, variables, variables, variables)

    # calculate utilities for all combinations
    u1 = -p1 + theta*q1 - t*(1-ξ1)*np.abs(x - 0.5)
    u2 = -p2 + theta*q2 - t*(1-ξ2)*np.abs(x - 0.5)

    # determine purchase decisions for all combinations
    decisions = np.where(u1 &gt; u2, 1, np.where(u1 &lt; u2, 2, 0.5))

    # count the number of customers for each firm for all combinations
    firm1_customers = np.count_nonzero(decisions == 1, axis=(0, 1)) + 0.5*np.count_nonzero(decisions == 0.5, axis=(0, 1))
    firm2_customers = np.count_nonzero(decisions == 2, axis=(0, 1)) + 0.5*np.count_nonzero(decisions == 0.5, axis=(0, 1))
    d1 = firm1_customers &#x2F; (c_res * c_res)
    d2 = firm2_customers &#x2F; (c_res * c_res)

    # calculate firm profits for all combinations
    pi1 = p1*d1 - beta*q1**2
    pi2 = p2*d2 - beta*q2**2

    # reshape results into 1D arrays and zip into a list
    data = list(zip(ξ1.ravel(), ξ2.ravel(), p1.ravel(), p2.ravel(), q1.ravel(), q2.ravel(), pi1.ravel(), pi2.ravel(), d1.ravel(), d2.ravel(), [decisions[:,:,:,i,j,k,l,m,n] for i,j,k,l,m,n in np.ndindex(decisions.shape[2:])]))

    # convert data to dataframe
    df = pd.DataFrame(data, columns=[&#x27;ξ1&#x27;, &#x27;ξ2&#x27;, &#x27;p1&#x27;, &#x27;p2&#x27;, &#x27;q1&#x27;, &#x27;q2&#x27;, &#x27;pi1&#x27;, &#x27;pi2&#x27;, &#x27;d1&#x27;, &#x27;d2&#x27;, &#x27;decisions&#x27;])
    return df</code></pre>
```
&quot;&quot;&quot;<p>This code didn&#x27;t solve the issue because ChatGPT-4 didn&#x27;t actually fix the indices problem.<p>2. GPT-4&#x27;s solution (via phind.com)<p>&quot;&quot;&quot;
The error you are encountering, `ValueError: too many values to unpack (expected 2)`, occurs when you are trying to unpack more values than expected. In your case, the error is raised at this line:<p>```python
[decisions[:, :, i, j] for i, j in np.ndindex(decisions.shape[2:])]))
```<p>The issue is that the `decisions` array has more than 2 dimensions, and you are trying to unpack these dimensions using only two variables, `i` and `j`. To fix this error, you need to unpack the dimensions correctly.<p>First, let&#x27;s understand the dimensions of the `decisions` array. It has the shape `(c_res, c_res, v_res, v_res, v_res, v_res, v_res, v_res)`. From the line causing the error, you are trying to loop only through the dimensions starting from the third dimension.<p>To fix the error, you should unpack all the dimensions from the third dimension onwards using an appropriate number of variables. In this case, you need six variables instead of two. Modify the line as follows:<p>```python
[decisions[:, :, i, j, k, l, m, n] for i, j, k, l, m, n in np.ndindex(decisions.shape[2:])]))
```<p>Now, the code should work without raising the `ValueError`. Here&#x27;s the modified version of the `calculate_df` function:<p>```python
def calculate_df(beta, t):
    ...
    # determine purchase decisions for all combinations
    decisions = np.where(u1 &gt; u2, 1, np.where(u1 &lt; u2, 2, 0.5))<p><pre><code>    # count the number of customers for each firm for all combinations
    ...
    
    # reshape results into 1D arrays and zip into a list
    data = list(zip(
        ξ1.ravel(), ξ2.ravel(), p1.ravel(), p2.ravel(), q1.ravel(),
        q2.ravel(), pi1.ravel(), pi2.ravel(), d1.ravel(), d2.ravel(),
        [decisions[:, :, i, j, k, l, m, n] for i, j, k, l, m, n in np.ndindex(decisions.shape[2:])]))

    # convert data to dataframe
    df = pd.DataFrame(
        data, columns=[&#x27;ξ1&#x27;, &#x27;ξ2&#x27;, &#x27;p1&#x27;, &#x27;p2&#x27;, &#x27;q1&#x27;, &#x27;q2&#x27;, &#x27;pi1&#x27;, &#x27;pi2&#x27;, &#x27;d1&#x27;, &#x27;d2&#x27;, &#x27;decisions&#x27;])
    return df</code></pre>
```<p>Now, when you run `calculate_df(1, 1)`, it should work without any errors.
&quot;&quot;&quot;
This code ran without any problems on the first try.<p>----------<p>I also missed the mature vibe of GPT-4&#x27;s answer. The way it walked me through the problem and wrote the correct answer just made me miss the good old days when this was the norm on ChatGPT-4. Now it&#x27;s only accessible via API.