mirror of
https://github.com/kuhyx/praca_magisterska.git
synced 2026-07-04 13:43:05 +02:00
621 lines
25 KiB
Python
621 lines
25 KiB
Python
#!/usr/bin/env python3
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"""
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Generate diagrams for PYTANIE 31: Interaktywne wspomaganie decyzji w warunkach ryzyka.
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Diagrams:
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1. Payoff matrix + all criteria results comparison (bar chart)
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2. Regret matrix construction step-by-step
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3. Hurwicz α interpolation between maximax and maximin
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4. Decision criteria mnemonic map
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5. Expected value criterion with probability-weighted bars
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6. Decision conditions spectrum (pewność → ryzyko → niepewność)
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All: A4-compatible, B&W, 300 DPI, laser-printer-friendly.
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"""
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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from matplotlib.patches import FancyBboxPatch
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import numpy as np
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import os
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DPI = 300
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BG = 'white'
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LN = 'black'
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FS = 8
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FS_TITLE = 11
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OUTPUT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'img')
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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GRAY1 = '#E8E8E8'
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GRAY2 = '#D0D0D0'
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GRAY3 = '#B8B8B8'
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GRAY4 = '#F5F5F5'
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GRAY5 = '#C0C0C0'
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def draw_box(ax, x, y, w, h, text, fill='white', lw=1.2, fontsize=FS,
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fontweight='normal', ha='center', va='center', rounded=True):
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if rounded:
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rect = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.05",
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lw=lw, edgecolor=LN, facecolor=fill)
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else:
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rect = mpatches.Rectangle((x, y), w, h, lw=lw, edgecolor=LN, facecolor=fill)
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ax.add_patch(rect)
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ax.text(x + w/2, y + h/2, text, ha=ha, va=va, fontsize=fontsize,
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fontweight=fontweight, wrap=True)
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def draw_arrow(ax, x1, y1, x2, y2, lw=1.2, style='->', color=LN):
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ax.annotate("", xy=(x2, y2), xytext=(x1, y1),
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arrowprops=dict(arrowstyle=style, color=color, lw=lw))
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# ============================================================
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# 1. PAYOFF MATRIX + ALL CRITERIA BAR CHART
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# ============================================================
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def draw_criteria_comparison():
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fig, axes = plt.subplots(1, 2, figsize=(8.27, 4.5), gridspec_kw={'width_ratios': [1.2, 1]})
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# -- Left: Payoff matrix as styled table --
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ax = axes[0]
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ax.axis('off')
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ax.set_xlim(0, 6)
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ax.set_ylim(0, 6)
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ax.set_title('Macierz wypłat (tys. zł)', fontsize=FS_TITLE, fontweight='bold', pad=8)
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# Headers
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headers_col = ['', 'S₁\n(dobra)', 'S₂\n(średnia)', 'S₃\n(zła)']
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rows = [
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['A₁ (fabryka)', '200', '50', '−100'],
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['A₂ (sklep)', '80', '70', '40'],
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['A₃ (obligacje)', '30', '30', '30'],
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]
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col_w = [1.8, 1.2, 1.2, 1.2]
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row_h = 0.7
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start_y = 4.5
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start_x = 0.2
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# Draw header row
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x = start_x
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for j, h in enumerate(headers_col):
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fill = GRAY2 if j > 0 else GRAY3
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rect = mpatches.Rectangle((x, start_y), col_w[j], row_h, lw=1, edgecolor=LN, facecolor=fill)
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ax.add_patch(rect)
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ax.text(x + col_w[j]/2, start_y + row_h/2, h, ha='center', va='center',
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fontsize=FS, fontweight='bold')
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x += col_w[j]
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# Draw data rows
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for i, row in enumerate(rows):
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x = start_x
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y = start_y - (i+1) * row_h
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for j, val in enumerate(row):
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fill = GRAY4 if j == 0 else ('white' if i % 2 == 0 else GRAY1)
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# Highlight negative
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if val.startswith('−'):
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fill = '#D8D8D8'
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rect = mpatches.Rectangle((x, y), col_w[j], row_h, lw=1, edgecolor=LN, facecolor=fill)
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ax.add_patch(rect)
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fw = 'bold' if j == 0 else 'normal'
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ax.text(x + col_w[j]/2, y + row_h/2, val, ha='center', va='center',
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fontsize=FS, fontweight=fw)
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x += col_w[j]
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# Probability row for EV
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x = start_x
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y = start_y - 4 * row_h
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probs = ['p (dla E[X]):', '0.5', '0.3', '0.2']
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for j, val in enumerate(probs):
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fill = GRAY5 if j > 0 else GRAY3
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rect = mpatches.Rectangle((x, y), col_w[j], row_h * 0.7, lw=1, edgecolor=LN, facecolor=fill)
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ax.add_patch(rect)
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ax.text(x + col_w[j]/2, y + row_h*0.35, val, ha='center', va='center',
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fontsize=7, fontweight='bold', style='italic')
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x += col_w[j]
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# -- Right: Bar chart comparing criteria results --
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ax2 = axes[1]
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criteria = ['E[X]', 'Laplace', 'Maximax', 'Maximin', 'Hurwicz\nα=0.6', 'Savage']
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# Recalculate with probabilities 0.5, 0.3, 0.2
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# E[X]: A1=200*0.5+50*0.3+(-100)*0.2=100+15-20=95
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# A2=80*0.5+70*0.3+40*0.2=40+21+8=69
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# A3=30*0.5+30*0.3+30*0.2=15+9+6=30
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ev = [95, 69, 30]
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laplace = [50, 63.3, 30]
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maximax = [200, 80, 30]
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maximin = [-100, 40, 30]
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hurwicz = [80, 64, 30] # α=0.6
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savage_maxregret = [140, 120, 170] # lower = better
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# Which alternative wins for each criterion?
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winners = [0, 1, 0, 1, 0, 1] # index of winning alternative
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winner_vals = [95, 63.3, 200, 40, 80, 120]
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# Display as grouped bar chart - each criterion shows the 3 alternatives
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x_pos = np.arange(len(criteria))
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width = 0.22
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hatches = ['///', '...', 'xxx']
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labels = ['A₁ (fabryka)', 'A₂ (sklep)', 'A₃ (obligacje)']
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all_vals = [
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[ev[0], laplace[0], maximax[0], maximin[0], hurwicz[0], savage_maxregret[0]],
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[ev[1], laplace[1], maximax[1], maximin[1], hurwicz[1], savage_maxregret[1]],
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[ev[2], laplace[2], maximax[2], maximin[2], hurwicz[2], savage_maxregret[2]],
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]
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for i in range(3):
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bars = ax2.bar(x_pos + (i - 1) * width, all_vals[i], width,
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label=labels[i], color='white', edgecolor=LN, hatch=hatches[i], lw=0.8)
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# Mark winners with star
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for c_idx in range(len(criteria)):
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w = winners[c_idx]
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val = all_vals[w][c_idx]
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ax2.text(x_pos[c_idx] + (w - 1) * width, val + 5, '★',
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ha='center', va='bottom', fontsize=10, fontweight='bold')
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ax2.set_xticks(x_pos)
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ax2.set_xticklabels(criteria, fontsize=7)
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ax2.set_ylabel('Wartość kryterium', fontsize=8)
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ax2.set_title('Porównanie kryteriów', fontsize=FS_TITLE, fontweight='bold', pad=8)
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ax2.legend(fontsize=7, loc='upper right')
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ax2.axhline(y=0, color=LN, lw=0.5, ls='-')
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ax2.spines['top'].set_visible(False)
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ax2.spines['right'].set_visible(False)
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ax2.tick_params(labelsize=7)
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# Note about Savage
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ax2.text(5, -30, '(Savage: niżej\n= lepiej)', fontsize=6, ha='center',
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va='top', style='italic')
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plt.tight_layout()
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outpath = os.path.join(OUTPUT_DIR, 'q31_criteria_comparison.png')
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fig.savefig(outpath, dpi=DPI, bbox_inches='tight', facecolor=BG)
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plt.close(fig)
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print(f" Saved: {outpath}")
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# ============================================================
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# 2. REGRET MATRIX CONSTRUCTION
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# ============================================================
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def draw_regret_matrix():
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fig, ax = plt.subplots(1, 1, figsize=(8.27, 5))
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ax.axis('off')
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ax.set_xlim(0, 10)
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ax.set_ylim(0, 7)
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ax.set_title('Kryterium Savage\'a — budowa macierzy żalu',
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fontsize=FS_TITLE + 1, fontweight='bold', pad=10)
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# --- Step 1: Original payoff matrix (left) ---
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ax.text(2.2, 6.3, 'Krok 1: Macierz wypłat', fontsize=9, fontweight='bold',
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ha='center', va='center')
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col_w = 1.0
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row_h = 0.55
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headers = ['', 'S₁', 'S₂', 'S₃']
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data = [
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['A₁', '200', '50', '−100'],
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['A₂', '80', '70', '40'],
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['A₃', '30', '30', '30'],
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]
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start_x = 0.3
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start_y = 5.5
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for j, h in enumerate(headers):
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w = 0.7 if j == 0 else col_w
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x = start_x + (0 if j == 0 else 0.7 + (j-1)*col_w)
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rect = mpatches.Rectangle((x, start_y), w, row_h, lw=1, edgecolor=LN, facecolor=GRAY2)
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ax.add_patch(rect)
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ax.text(x + w/2, start_y + row_h/2, h, ha='center', va='center',
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fontsize=FS, fontweight='bold')
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for i, row in enumerate(data):
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y = start_y - (i+1) * row_h
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for j, val in enumerate(row):
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w = 0.7 if j == 0 else col_w
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x = start_x + (0 if j == 0 else 0.7 + (j-1)*col_w)
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fill = GRAY4 if j == 0 else 'white'
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rect = mpatches.Rectangle((x, y), w, row_h, lw=1, edgecolor=LN, facecolor=fill)
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ax.add_patch(rect)
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ax.text(x + w/2, y + row_h/2, val, ha='center', va='center', fontsize=FS)
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# Max per column annotation
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max_y = start_y - 3 * row_h - 0.1
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ax.text(start_x + 0.7 + 0.5*col_w, max_y, 'max=200', fontsize=7,
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ha='center', va='top', fontweight='bold', color='#333')
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ax.text(start_x + 0.7 + 1.5*col_w, max_y, 'max=70', fontsize=7,
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ha='center', va='top', fontweight='bold', color='#333')
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ax.text(start_x + 0.7 + 2.5*col_w, max_y, 'max=40', fontsize=7,
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ha='center', va='top', fontweight='bold', color='#333')
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# Arrow
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ax.annotate("", xy=(5.0, 4.8), xytext=(4.2, 4.8),
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arrowprops=dict(arrowstyle='->', color=LN, lw=2))
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ax.text(4.6, 5.0, 'rᵢⱼ = max − aᵢⱼ', fontsize=8, ha='center', va='bottom',
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fontweight='bold')
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# --- Step 2: Regret matrix (right) ---
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ax.text(7.5, 6.3, 'Krok 2: Macierz żalu', fontsize=9, fontweight='bold',
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ha='center', va='center')
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regret_data = [
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['A₁', '0', '20', '140'],
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['A₂', '120', '0', '0'],
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['A₃', '170', '40', '10'],
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]
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headers2 = ['', 'S₁', 'S₂', 'S₃', 'max rᵢ']
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start_x2 = 5.3
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for j, h in enumerate(headers2):
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w = 0.7 if j == 0 else (0.9 if j < 4 else 1.0)
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x = start_x2
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if j == 0:
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x = start_x2
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elif j <= 3:
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x = start_x2 + 0.7 + (j-1)*0.9
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else:
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x = start_x2 + 0.7 + 3*0.9
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rect = mpatches.Rectangle((x, start_y), w, row_h, lw=1, edgecolor=LN,
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facecolor=GRAY2 if j < 4 else GRAY3)
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ax.add_patch(rect)
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ax.text(x + w/2, start_y + row_h/2, h, ha='center', va='center',
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fontsize=FS, fontweight='bold')
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max_regrets = [140, 120, 170]
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for i, row in enumerate(regret_data):
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y = start_y - (i+1) * row_h
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for j, val in enumerate(row):
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w = 0.7 if j == 0 else 0.9
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x = start_x2 + (0 if j == 0 else 0.7 + (j-1)*0.9)
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fill = GRAY4 if j == 0 else 'white'
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# Highlight the max regret cell
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if j > 0 and int(val) == max_regrets[i]:
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fill = GRAY2
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rect = mpatches.Rectangle((x, y), w, row_h, lw=1, edgecolor=LN, facecolor=fill)
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ax.add_patch(rect)
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fw = 'bold' if (j > 0 and int(val) == max_regrets[i]) else 'normal'
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ax.text(x + w/2, y + row_h/2, val, ha='center', va='center',
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fontsize=FS, fontweight=fw)
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# Max regret column
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x = start_x2 + 0.7 + 3*0.9
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w = 1.0
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fill = '#C8C8C8' if max_regrets[i] == min(max_regrets) else GRAY1
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rect = mpatches.Rectangle((x, y), w, row_h, lw=1.5 if max_regrets[i]==min(max_regrets) else 1,
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edgecolor=LN, facecolor=fill)
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ax.add_patch(rect)
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marker = ' ★' if max_regrets[i] == min(max_regrets) else ''
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ax.text(x + w/2, y + row_h/2, f'{max_regrets[i]}{marker}', ha='center', va='center',
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fontsize=FS, fontweight='bold')
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# Bottom conclusion
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ax.text(5.0, 2.8, 'Krok 3: Wybierz min z max żalu → A₂ (max żal = 120)',
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fontsize=10, ha='center', va='center', fontweight='bold',
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bbox=dict(boxstyle='round,pad=0.3', facecolor=GRAY1, edgecolor=LN, lw=1.5))
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# Interpretatation examples
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ax.text(5.0, 2.0, 'Interpretacja żalu: r₁₃ = 140 oznacza:\n'
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'„Gdyby nastąpił S₃ (zła koniunktura), a wybrałbym A₁,\n'
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'żałowałbym, bo najlepszą opcją byłoby A₂ z wynikiem 40 — traciłbym 140"',
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fontsize=7.5, ha='center', va='center', style='italic',
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bbox=dict(boxstyle='round,pad=0.3', facecolor=GRAY4, edgecolor=GRAY3, lw=0.8))
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# Mnemonic
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ax.text(5.0, 0.8, 'Mnemonik: Savage = „Żal jak nóż"\nMaksymalny żal to nóż '
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'— wybierz opcję z NAJMNIEJSZYM nożem',
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fontsize=8, ha='center', va='center', fontweight='bold',
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bbox=dict(boxstyle='round,pad=0.3', facecolor='white', edgecolor=LN, lw=1))
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plt.tight_layout()
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outpath = os.path.join(OUTPUT_DIR, 'q31_regret_matrix.png')
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fig.savefig(outpath, dpi=DPI, bbox_inches='tight', facecolor=BG)
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plt.close(fig)
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print(f" Saved: {outpath}")
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# ============================================================
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# 3. HURWICZ α INTERPOLATION
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# ============================================================
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def draw_hurwicz_interpolation():
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fig, ax = plt.subplots(1, 1, figsize=(8.27, 4))
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ax.set_title('Kryterium Hurwicza — wpływ α na wybór alternatywy',
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fontsize=FS_TITLE + 1, fontweight='bold', pad=10)
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alphas = np.linspace(0, 1, 200)
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# V(Ai) = α * max_i + (1-α) * min_i
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# A1: max=200, min=-100
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# A2: max=80, min=40
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# A3: max=30, min=30
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v1 = alphas * 200 + (1 - alphas) * (-100)
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v2 = alphas * 80 + (1 - alphas) * 40
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v3 = alphas * 30 + (1 - alphas) * 30
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ax.plot(alphas, v1, 'k-', lw=2, label='A₁ (fabryka): V = 300α − 100')
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ax.plot(alphas, v2, 'k--', lw=2, label='A₂ (sklep): V = 40α + 40')
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ax.plot(alphas, v3, 'k:', lw=2, label='A₃ (obligacje): V = 30')
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# Find crossover points
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# A2 = A1: 40α + 40 = 300α - 100 → 140 = 260α → α = 140/260 ≈ 0.538
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alpha_cross_12 = 140 / 260
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v_cross_12 = 40 * alpha_cross_12 + 40
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# A2 = A3: 40α + 40 = 30 → 40α = -10 → α = -0.25 (never — A2 always > A3)
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# A1 = A3: 300α - 100 = 30 → 300α = 130 → α = 130/300 ≈ 0.433
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alpha_cross_13 = 130 / 300
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v_cross_13 = 30
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ax.plot(alpha_cross_12, v_cross_12, 'ko', markersize=8, zorder=5)
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ax.annotate(f'α ≈ {alpha_cross_12:.2f}\nA₁ = A₂', xy=(alpha_cross_12, v_cross_12),
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xytext=(alpha_cross_12 + 0.12, v_cross_12 - 30),
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fontsize=8, fontweight='bold',
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arrowprops=dict(arrowstyle='->', color=LN, lw=1))
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# Shade winning regions
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ax.axvspan(0, alpha_cross_12, alpha=0.08, color='black', label='_')
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ax.axvspan(alpha_cross_12, 1, alpha=0.15, color='black', label='_')
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ax.text(alpha_cross_12 / 2, -60, 'A₂ wygrywa\n(pesymistycznie)',
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fontsize=8, ha='center', va='center',
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bbox=dict(boxstyle='round', facecolor='white', edgecolor=LN))
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ax.text((alpha_cross_12 + 1) / 2, 160, 'A₁ wygrywa\n(optymistycznie)',
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fontsize=8, ha='center', va='center',
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bbox=dict(boxstyle='round', facecolor='white', edgecolor=LN))
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# Special α values
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ax.axvline(x=0, color=LN, lw=0.5, ls=':')
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ax.axvline(x=1, color=LN, lw=0.5, ls=':')
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ax.text(0, -115, 'α=0\nmaximin', fontsize=7, ha='center', va='top', fontweight='bold')
|
||
ax.text(1, -115, 'α=1\nmaximax', fontsize=7, ha='center', va='top', fontweight='bold')
|
||
|
||
ax.set_xlabel('Współczynnik optymizmu α', fontsize=9)
|
||
ax.set_ylabel('V(Aᵢ) = α·max + (1−α)·min', fontsize=9)
|
||
ax.legend(fontsize=8, loc='upper left')
|
||
ax.spines['top'].set_visible(False)
|
||
ax.spines['right'].set_visible(False)
|
||
ax.set_xlim(-0.05, 1.05)
|
||
ax.axhline(y=0, color=LN, lw=0.3, ls='-')
|
||
ax.tick_params(labelsize=8)
|
||
|
||
plt.tight_layout()
|
||
outpath = os.path.join(OUTPUT_DIR, 'q31_hurwicz_alpha.png')
|
||
fig.savefig(outpath, dpi=DPI, bbox_inches='tight', facecolor=BG)
|
||
plt.close(fig)
|
||
print(f" Saved: {outpath}")
|
||
|
||
|
||
# ============================================================
|
||
# 4. DECISION CRITERIA MNEMONIC MAP
|
||
# ============================================================
|
||
def draw_criteria_mnemonic():
|
||
fig, ax = plt.subplots(1, 1, figsize=(8.27, 6))
|
||
ax.set_xlim(0, 10)
|
||
ax.set_ylim(0, 8)
|
||
ax.set_aspect('equal')
|
||
ax.axis('off')
|
||
ax.set_title('Mapa mnemoniczna — 6 kryteriów decyzyjnych',
|
||
fontsize=FS_TITLE + 2, fontweight='bold', pad=10)
|
||
|
||
# Central node
|
||
draw_box(ax, 3.5, 3.5, 3, 1, 'MACIERZ\nWYPŁAT', fill=GRAY2, lw=2,
|
||
fontsize=11, fontweight='bold')
|
||
|
||
# Criteria boxes around the center
|
||
criteria = [
|
||
# (x, y, w, h, title, mnemonic, formula)
|
||
(0, 6.5, 3, 1.2, 'WARTOŚĆ OCZEKIWANA', '„Mam prawdopodobieństwa"', 'E[Aᵢ] = Σ pⱼ·aᵢⱼ'),
|
||
(3.5, 6.5, 3, 1.2, 'LAPLACE', '„Wszystko po równo"', 'V = Σaᵢⱼ / n'),
|
||
(7, 6.5, 3, 1.2, 'MAXIMAX', '„Optymista: max z max"', 'max maxⱼ aᵢⱼ'),
|
||
(0, 0.5, 3, 1.2, 'MAXIMIN (Wald)', '„Pesymista: max z min"', 'max minⱼ aᵢⱼ'),
|
||
(3.5, 0.5, 3, 1.2, 'HURWICZ', '„α pomiędzy"', 'α·max + (1−α)·min'),
|
||
(7, 0.5, 3, 1.2, 'SAVAGE', '„Min max żalu"', 'min maxⱼ rᵢⱼ'),
|
||
]
|
||
|
||
fills = [GRAY3, GRAY1, 'white', 'white', GRAY1, GRAY3]
|
||
|
||
for i, (x, y, w, h, title, mnem, formula) in enumerate(criteria):
|
||
rect = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.08",
|
||
lw=1.5, edgecolor=LN, facecolor=fills[i])
|
||
ax.add_patch(rect)
|
||
ax.text(x + w/2, y + h*0.78, title, ha='center', va='center',
|
||
fontsize=8, fontweight='bold')
|
||
ax.text(x + w/2, y + h*0.45, mnem, ha='center', va='center',
|
||
fontsize=7, style='italic')
|
||
ax.text(x + w/2, y + h*0.15, formula, ha='center', va='center',
|
||
fontsize=7, fontweight='bold', family='monospace')
|
||
|
||
# Arrows from center to each box
|
||
cx, cy = 5, 4 # center of macierz
|
||
bx, by = x + w/2, y + h/2
|
||
if by > cy:
|
||
ax.annotate("", xy=(bx, y), xytext=(cx, 4.5),
|
||
arrowprops=dict(arrowstyle='->', color=LN, lw=1,
|
||
connectionstyle='arc3,rad=0'))
|
||
else:
|
||
ax.annotate("", xy=(bx, y + h), xytext=(cx, 3.5),
|
||
arrowprops=dict(arrowstyle='->', color=LN, lw=1,
|
||
connectionstyle='arc3,rad=0'))
|
||
|
||
# Labels on arrows
|
||
ax.text(1.2, 5.6, 'znane p', fontsize=7, ha='center', va='center',
|
||
bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor=GRAY3, lw=0.5))
|
||
ax.text(5, 5.6, 'p = 1/n', fontsize=7, ha='center', va='center',
|
||
bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor=GRAY3, lw=0.5))
|
||
ax.text(8.7, 5.6, 'max ↑', fontsize=7, ha='center', va='center',
|
||
bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor=GRAY3, lw=0.5))
|
||
ax.text(1.2, 2.5, 'min ↑', fontsize=7, ha='center', va='center',
|
||
bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor=GRAY3, lw=0.5))
|
||
ax.text(5, 2.5, 'podaj α', fontsize=7, ha='center', va='center',
|
||
bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor=GRAY3, lw=0.5))
|
||
ax.text(8.7, 2.5, 'macierz\nżalu', fontsize=7, ha='center', va='center',
|
||
bbox=dict(boxstyle='round,pad=0.15', facecolor='white', edgecolor=GRAY3, lw=0.5))
|
||
|
||
plt.tight_layout()
|
||
outpath = os.path.join(OUTPUT_DIR, 'q31_criteria_mnemonic.png')
|
||
fig.savefig(outpath, dpi=DPI, bbox_inches='tight', facecolor=BG)
|
||
plt.close(fig)
|
||
print(f" Saved: {outpath}")
|
||
|
||
|
||
# ============================================================
|
||
# 5. EXPECTED VALUE CRITERION WITH PROBABILITY BARS
|
||
# ============================================================
|
||
def draw_expected_value():
|
||
fig, axes = plt.subplots(1, 3, figsize=(8.27, 3.5), sharey=True)
|
||
fig.suptitle('Kryterium wartości oczekiwanej E[X] — rozkład wyników per alternatywa',
|
||
fontsize=FS_TITLE, fontweight='bold', y=1.02)
|
||
|
||
# Probabilities: p1=0.5, p2=0.3, p3=0.2
|
||
probs = [0.5, 0.3, 0.2]
|
||
states = ['S₁\n(dobra)\np=0.5', 'S₂\n(średnia)\np=0.3', 'S₃\n(zła)\np=0.2']
|
||
alts = [
|
||
('A₁ (fabryka)', [200, 50, -100], 95),
|
||
('A₂ (sklep)', [80, 70, 40], 69),
|
||
('A₃ (obligacje)', [30, 30, 30], 30),
|
||
]
|
||
|
||
hatches = ['///', '...', 'xxx']
|
||
|
||
for idx, (ax, (name, vals, ev)) in enumerate(zip(axes, alts)):
|
||
# Bar: height = payoff, width proportional to probability
|
||
x_positions = [0, 0.6, 1.0]
|
||
widths = [p * 0.9 for p in probs]
|
||
|
||
for i, (v, p, h) in enumerate(zip(vals, probs, hatches)):
|
||
color = 'white' if v >= 0 else GRAY2
|
||
bar = ax.bar(x_positions[i], v, width=widths[i], color=color,
|
||
edgecolor=LN, hatch=h, lw=0.8, align='edge')
|
||
# Value label
|
||
offset = 8 if v >= 0 else -12
|
||
ax.text(x_positions[i] + widths[i]/2, v + offset, f'{v}',
|
||
ha='center', va='center', fontsize=8, fontweight='bold')
|
||
# Probability contribution
|
||
contrib = v * p
|
||
ax.text(x_positions[i] + widths[i]/2, v/2,
|
||
f'{v}×{p}\n={contrib:.0f}', ha='center', va='center',
|
||
fontsize=6, style='italic')
|
||
|
||
# Expected value line
|
||
ax.axhline(y=ev, color=LN, lw=2, ls='--')
|
||
ax.text(1.35, ev, f'E[X]={ev}', fontsize=8, fontweight='bold',
|
||
va='center', ha='left',
|
||
bbox=dict(boxstyle='round,pad=0.15', facecolor=GRAY1, edgecolor=LN))
|
||
|
||
ax.set_title(name, fontsize=9, fontweight='bold')
|
||
ax.set_xticks([0.225, 0.735, 1.09])
|
||
ax.set_xticklabels(['S₁', 'S₂', 'S₃'], fontsize=7)
|
||
ax.axhline(y=0, color=LN, lw=0.5)
|
||
ax.spines['top'].set_visible(False)
|
||
ax.spines['right'].set_visible(False)
|
||
ax.tick_params(labelsize=7)
|
||
|
||
# Star on winner
|
||
if ev == 95:
|
||
ax.text(0.7, ev + 20, '★ MAX', fontsize=9, fontweight='bold',
|
||
ha='center', va='bottom')
|
||
|
||
axes[0].set_ylabel('Wypłata (tys. zł)', fontsize=8)
|
||
|
||
plt.tight_layout()
|
||
outpath = os.path.join(OUTPUT_DIR, 'q31_expected_value.png')
|
||
fig.savefig(outpath, dpi=DPI, bbox_inches='tight', facecolor=BG)
|
||
plt.close(fig)
|
||
print(f" Saved: {outpath}")
|
||
|
||
|
||
# ============================================================
|
||
# 6. DECISION CONDITIONS SPECTRUM
|
||
# ============================================================
|
||
def draw_conditions_spectrum():
|
||
fig, ax = plt.subplots(1, 1, figsize=(8.27, 3.5))
|
||
ax.set_xlim(0, 10)
|
||
ax.set_ylim(0, 5)
|
||
ax.set_aspect('equal')
|
||
ax.axis('off')
|
||
ax.set_title('Warunki decyzyjne — spektrum wiedzy decydenta',
|
||
fontsize=FS_TITLE + 1, fontweight='bold', pad=10)
|
||
|
||
# Three zones
|
||
zones = [
|
||
(0.3, 1.5, 2.8, 2.5, 'PEWNOŚĆ', 'white', [
|
||
'Znamy dokładny wynik',
|
||
'Przykład: lokata 5%',
|
||
'Metoda: po prostu wybierz',
|
||
'najlepszy wynik'
|
||
]),
|
||
(3.5, 1.5, 2.8, 2.5, 'RYZYKO', GRAY1, [
|
||
'Znamy wyniki I prawdop.',
|
||
'Przykład: gra w kości',
|
||
'Metoda: wartość',
|
||
'oczekiwana E[X]'
|
||
]),
|
||
(6.7, 1.5, 2.8, 2.5, 'NIEPEWNOŚĆ', GRAY3, [
|
||
'Znamy wyniki, ale',
|
||
'NIE znamy prawdop.',
|
||
'Metody: Laplace, maximax,',
|
||
'maximin, Hurwicz, Savage'
|
||
]),
|
||
]
|
||
|
||
for x, y, w, h, title, fill, lines in zones:
|
||
rect = FancyBboxPatch((x, y), w, h, boxstyle="round,pad=0.1",
|
||
lw=2, edgecolor=LN, facecolor=fill)
|
||
ax.add_patch(rect)
|
||
ax.text(x + w/2, y + h - 0.3, title, ha='center', va='center',
|
||
fontsize=11, fontweight='bold')
|
||
for i, line in enumerate(lines):
|
||
ax.text(x + w/2, y + h - 0.7 - i*0.4, line, ha='center', va='center',
|
||
fontsize=7)
|
||
|
||
# Arrows between zones
|
||
ax.annotate("", xy=(3.4, 2.75), xytext=(3.15, 2.75),
|
||
arrowprops=dict(arrowstyle='->', color=LN, lw=2))
|
||
ax.annotate("", xy=(6.6, 2.75), xytext=(6.35, 2.75),
|
||
arrowprops=dict(arrowstyle='->', color=LN, lw=2))
|
||
|
||
# Bottom: knowledge gradient bar
|
||
gradient_y = 0.5
|
||
gradient_h = 0.5
|
||
n_steps = 50
|
||
for i in range(n_steps):
|
||
x = 0.3 + i * (9.2 / n_steps)
|
||
w = 9.2 / n_steps + 0.01
|
||
gray_val = 1 - (i / n_steps) * 0.7
|
||
rect = mpatches.Rectangle((x, gradient_y), w, gradient_h, lw=0,
|
||
facecolor=str(gray_val))
|
||
ax.add_patch(rect)
|
||
|
||
rect = mpatches.Rectangle((0.3, gradient_y), 9.2, gradient_h, lw=1.5,
|
||
edgecolor=LN, facecolor='none')
|
||
ax.add_patch(rect)
|
||
|
||
ax.text(0.3, gradient_y - 0.15, 'Dużo wiedzy', fontsize=7, ha='left', va='top')
|
||
ax.text(9.5, gradient_y - 0.15, 'Mało wiedzy', fontsize=7, ha='right', va='top')
|
||
ax.text(4.95, gradient_y + gradient_h / 2, 'POZIOM WIEDZY DECYDENTA',
|
||
fontsize=8, fontweight='bold', ha='center', va='center', color='white')
|
||
|
||
plt.tight_layout()
|
||
outpath = os.path.join(OUTPUT_DIR, 'q31_conditions_spectrum.png')
|
||
fig.savefig(outpath, dpi=DPI, bbox_inches='tight', facecolor=BG)
|
||
plt.close(fig)
|
||
print(f" Saved: {outpath}")
|
||
|
||
|
||
# ============================================================
|
||
# MAIN
|
||
# ============================================================
|
||
if __name__ == '__main__':
|
||
print("Generating PYTANIE 31 diagrams...")
|
||
draw_criteria_comparison()
|
||
draw_regret_matrix()
|
||
draw_hurwicz_interpolation()
|
||
draw_criteria_mnemonic()
|
||
draw_expected_value()
|
||
draw_conditions_spectrum()
|
||
print("Done! All Q31 diagrams saved to:", OUTPUT_DIR)
|