feat(matplotlib): implement contour-decision-boundary (#2977)

## Implementation: `contour-decision-boundary` - matplotlib

Implements the **matplotlib** version of `contour-decision-boundary`.

**File:**
`plots/contour-decision-boundary/implementations/matplotlib.py`

**Parent Issue:** #2921

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612898107)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/contour-decision-boundary/implementations/matplotlib.py

@@ -0,0 +1,112 @@
+""" pyplots.ai
+contour-decision-boundary: Decision Boundary Classifier Visualization
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.datasets import make_moons
+from sklearn.svm import SVC
+
+
+# Data - generate synthetic two-moon classification data
+np.random.seed(42)
+X, y = make_moons(n_samples=200, noise=0.25, random_state=42)
+
+# Train a classifier (SVM with RBF kernel)
+classifier = SVC(kernel="rbf", C=1.0, gamma="scale")
+classifier.fit(X, y)
+
+# Create mesh grid for decision boundary
+x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
+y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
+xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))
+
+# Predict class for each point in mesh
+Z = classifier.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot decision regions with contourf
+ax.contourf(xx, yy, Z, alpha=0.4, cmap="coolwarm", levels=[-0.5, 0.5, 1.5])
+
+# Add decision boundary line
+ax.contour(xx, yy, Z, colors="white", linewidths=2, levels=[0.5])
+
+# Identify correctly and incorrectly classified points
+predictions = classifier.predict(X)
+correct = predictions == y
+incorrect = ~correct
+
+# Plot training points - correctly classified
+class_0_correct = (y == 0) & correct
+class_1_correct = (y == 1) & correct
+ax.scatter(
+    X[class_0_correct, 0],
+    X[class_0_correct, 1],
+    c="#306998",
+    s=150,
+    alpha=0.9,
+    edgecolors="white",
+    linewidths=2,
+    marker="o",
+    label="Class A (correct)",
+    zorder=3,
+)
+ax.scatter(
+    X[class_1_correct, 0],
+    X[class_1_correct, 1],
+    c="#FFD43B",
+    s=150,
+    alpha=0.9,
+    edgecolors="black",
+    linewidths=2,
+    marker="o",
+    label="Class B (correct)",
+    zorder=3,
+)
+
+# Plot incorrectly classified points with X marker
+if np.any(incorrect):
+    class_0_incorrect = (y == 0) & incorrect
+    class_1_incorrect = (y == 1) & incorrect
+    if np.any(class_0_incorrect):
+        ax.scatter(
+            X[class_0_incorrect, 0],
+            X[class_0_incorrect, 1],
+            c="#306998",
+            s=200,
+            alpha=0.9,
+            edgecolors="red",
+            linewidths=3,
+            marker="X",
+            label="Class A (misclassified)",
+            zorder=4,
+        )
+    if np.any(class_1_incorrect):
+        ax.scatter(
+            X[class_1_incorrect, 0],
+            X[class_1_incorrect, 1],
+            c="#FFD43B",
+            s=200,
+            alpha=0.9,
+            edgecolors="red",
+            linewidths=3,
+            marker="X",
+            label="Class B (misclassified)",
+            zorder=4,
+        )
+
+# Labels and styling
+ax.set_xlabel("Feature X1", fontsize=20)
+ax.set_ylabel("Feature X2", fontsize=20)
+ax.set_title("contour-decision-boundary · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.legend(fontsize=14, loc="upper left", framealpha=0.9)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/contour-decision-boundary/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: contour-decision-boundary
+created: '2025-12-31T05:42:26Z'
+updated: '2025-12-31T05:56:04Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612898107
+issue: 2921
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/contour-decision-boundary/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of decision boundary visualization meeting all specification
+    requirements
+  - Clear visual distinction between correctly classified and misclassified points
+    using different marker styles (circles vs X)
+  - Proper use of contourf for class regions with appropriate transparency (alpha=0.4)
+  - White decision boundary line provides clear separation between regions
+  - Good marker sizing (s=150/200) and edge colors (white/black/red) for visibility
+  - Reproducible with fixed random seeds
+  weaknesses:
+  - Grid is barely visible despite being enabled - could use slightly higher alpha
+    or different styling
+  - Color palette uses coolwarm which is not optimal for colorblind accessibility
+    - consider using a perceptually uniform colormap
+  - Could leverage SVM decision_function to show prediction confidence as gradient
+    rather than hard boundary regions