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[mccall_model] Replace np.sum(a * b) with a @ b #471

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Merged
mmcky merged 1 commit into from
Jul 21, 2025
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[mccall_model] Replace np.sum(a * b) with a @ b #471

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8 changes: 4 additions & 4 deletions lectures/mccall_model.md
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Original file line number Diff line number Diff line change
Expand Up @@ -404,7 +404,7 @@ class McCallModel:
# Evaluate value for each state-action pair
# Consider action = accept or reject the current offer
accept = w[i] / (1 - β)
reject = c + β * np.sum(v * q)
reject = c + β * (v @ q)
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@HumphreyYang HumphreyYang Jul 2, 2025

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Many thanks @UdohNakamura and @mmcky,

When building on my side I got

<string>:3: NumbaPerformanceWarning: '@' is faster on contiguous arrays, called on (Array(float64, 1, 'C', False, aligned=True), Array(float64, 1, 'A', False, aligned=True))

Upon checking, I found we annotate the type

    ('w', float64[:]),   # array of wage values, w[i] = wage at state i
    ('q', float64[:])    # array of probabilities

The following type will help (see more here:

    ('w', float64[::1]),   # array of wage values, w[i] = wage at state i
    ('q', float64[::1])    # array of probabilities

Please let me know what you think.

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@oyamad oyamad Jul 4, 2025

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@HumphreyYang Thanks, I didn't know that. Maybe write float64[::1] in place of float64[:] and explain that it is needed to have @ work inside a jitclass?

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@mmcky mmcky Jul 21, 2025

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thanks @HumphreyYang and @oyamad

@HumphreyYang I will merge these great PRs and then you can do a pass to fix this.


return np.array([accept, reject])
```
Expand Down Expand Up @@ -488,7 +488,7 @@ def compute_reservation_wage(mcm,

# == Now compute the reservation wage == #

return (1 - β) * (c + β * np.sum(v * q))
return (1 - β) * (c + β * (v @ q))
```

The next line computes the reservation wage at default parameters
Expand Down Expand Up @@ -622,13 +622,13 @@ def compute_reservation_wage_two(mcm,

# == First compute h == #

h = np.sum(w * q) / (1 - β)
h = (w @ q) / (1 - β)
i = 0
error = tol + 1
while i < max_iter and error > tol:

s = np.maximum(w / (1 - β), h)
h_next = c + β * np.sum(s * q)
h_next = c + β * (s @ q)

error = np.abs(h_next - h)
i += 1
Expand Down
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