Build the first working two-cluster segmenter

Build only what the claim requires: k=2 assignments are stable for the fixture and cluster centers are printable. Prefer boring, deterministic code over frameworks you cannot yet explain. Run the path twice; identical output on this fixture is a feature, not a lack of creativity.

from math import dist
pts=[(0,.0),(.08,.03),(.15,.06),(.77,.83),(.85,.91),(1,1)]
centers=[pts[0],pts[-1]]
for _ in range(10):
 groups=[[p for p in pts if min(range(2),key=lambda j:dist(p,centers[j]))==i] for i in range(2)]
 new=[tuple(sum(v)/len(g) for v in zip(*g)) for g in groups]
 if new==centers: break
 centers=new
print([len(g) for g in groups], centers)

Expected evidence: (3, 3) with centers near the two visible groups. Read each printed intermediate as part of the argument that the path works—not as decoration.

Narrate the journey from raw input to result for a single example from small 2D customer matrix. If you cannot name an intermediate, the implementation is still too opaque for this lab. Only after this path is solid should you generalize data sources or UI.

While implementing the two-cluster segmenter, print or log at least three intermediates that map to the claim (k=2 assignments are stable for the fixture and cluster centers are printable). Good intermediates are values a teammate could recompute with a calculator or diff. Bad intermediates are framework traces you cannot explain.

Re-run with small 2D customer matrix twice. If the second run differs, either the path is nondeterministic (document the seed) or you have hidden global state—both are lab bugs until named.

Stop adding features once the path supports group customers by spend/visits without pretending clusters are ground-truth labels. Extra UI, extra tools, or extra models belong in later chapters. The mastery bar for this page is simply: a deterministic end-to-end path with intermediates.

At the implementation stage for clustering-basics, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about small 2D customer matrix that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: random two-group assignment inertia for comparison.

For this page specifically, success looks like a deterministic path with printed intermediates while still centering the user decision to group customers by spend/visits without pretending clusters are ground-truth labels. If you cannot point to a file, command, or assertion that proves that for the two-cluster segmenter, stay on this page instead of advancing.

Glossary: clustering · Cheatsheet: ML Python starter

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