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🌐 [translation-sync] [scipy.md] Update np.random → Generator API #120

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🌐 [translation-sync] [scipy.md] Update np.random → Generator API #120

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8b15475
Update translation: lectures/scipy.md
mmcky May 8, 2026
c861490
Update translation: .translate/state/scipy.md.yml
mmcky May 8, 2026
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10 changes: 5 additions & 5 deletions .translate/state/scipy.md.yml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
source-sha: cfedc1a06bbefe20d23924d26aef437a968ab2da
synced-at: "2026-03-20"
model: unknown
mode: RESYNC
source-sha: fb75a07e49cbec97c2c3e698a81ee008203b1fab
synced-at: "2026-05-08"
model: claude-sonnet-4-6
mode: UPDATE
section-count: 8
tool-version: 0.13.1
tool-version: 0.14.1
14 changes: 8 additions & 6 deletions lectures/scipy.md
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Expand Up @@ -124,10 +124,11 @@ a = np.identity(3)

### متغیرهای تصادفی و توزیع‌ها

به یاد بیاورید که `numpy.random` توابعی را برای تولید متغیرهای تصادفی فراهم می‌کند
به یاد بیاورید که `numpy.random` ابزارهایی را برای تولید متغیرهای تصادفی فراهم می‌کند

```{code-cell} python3
np.random.beta(5, 5, size=3)
rng = np.random.default_rng()
rng.beta(5, 5, size=3)
```

این یک نمونه از توزیع با تابع چگالی زیر را وقتی `a, b = 5, 5` تولید می‌کند
Expand Down Expand Up @@ -208,8 +209,8 @@ plt.show()
```{code-cell} python3
from scipy.stats import linregress

Comment on lines 209 to 211
x = np.random.randn(200)
y = 2 * x + 0.1 * np.random.randn(200)
x = rng.standard_normal(200)
y = 2 * x + 0.1 * rng.standard_normal(200)
gradient, intercept, r_value, p_value, std_err = linregress(x, y)
gradient, intercept
```
Expand Down Expand Up @@ -592,8 +593,9 @@ $$ \mathbb E \max\{ S_n - K, 0 \}
در اینجا یک راه‌حل آورده شده است:

```{code-cell} ipython3
rng = np.random.default_rng()
M = 10_000_000
S = np.exp(μ + σ * np.random.randn(M))
S = np.exp(μ + σ * rng.standard_normal(M))
return_draws = np.maximum(S - K, 0)
P = β**n * np.mean(return_draws)
print(f"The Monte Carlo option price is {P:3f}")
Expand Down Expand Up @@ -648,4 +650,4 @@ bisect(f, 0, 1)
```

```{solution-end}
```
```
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