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- #!/usr/bin/env python3
- import csv
- import math
- from collections import defaultdict
- # include X
- include_x = False
- # normalize q and Y
- normalize = True
- # closeness coefficient
- closeness = 2
- # step size within RGB color space
- step = 4
- data = []
- with open("database.csv") as infile:
- for name, *nums in csv.reader(infile, delimiter=",", quotechar="'"):
- data.append((name, *[float(n) for n in nums]))
- counts = defaultdict(int)
- results = {}
- try:
- with open("best.csv") as infile:
- for r, g, b, name, score in csv.reader(infile, delimiter=",", quotechar="'"):
- results[(int(r), int(g), int(b))] = (name, float(score))
- except:
- pass # file not found, assume no prior results
- for r in range(0, 256, step):
- for g in range(0, 256, step):
- for b in range(0, 256, step):
- if (known := results.get((r, g, b), None)) is not None:
- counts[known[0]] += 1
- continue
- norm_color = math.sqrt(r * r + g * g + b * b)
- if norm_color == 0:
- continue
- best_score = None
- best_name = None
- for name, x, yr, yg, yb in data:
- norm = (norm_color * math.sqrt(yr * yr + yg * yg + yb * yb)) if normalize else 1
- score = (x if include_x else 0) - (closeness * (r * yr + g * yg + b * yb) / norm)
- if best_score is None or score < best_score:
- best_score = score
- best_name = name
- results[(r, g, b)] = (best_name, best_score)
- counts[best_name] += 1
- with open("best.csv", "w") as outfile:
- csv.writer(outfile, delimiter=",", quotechar="'").writerows((*k, *v) for k, v in results.items())
- with open("counts.csv", "w") as outfile:
- csv.writer(outfile, delimiter=",", quotechar="'").writerows(counts.items())
- print(f"Top ten most hit:")
- for k in sorted(list(counts), key=counts.get, reverse=True)[:10]:
- print(f"{k} - {counts[k]}")
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