<｜begin▁of▁sentence｜>### Instruction:
As a fashion stylist, you often need to create cohesive looks by pairing clothing and accessories that complement each other. Imagine you have a wardrobe with garments categorized into different styles represented by the numbers 0 and 1. Additionally, you have a list of pairing probabilities based on the compatibility of these styles. Using this information, you aim to generate a visual representation of how garments from different styles could be connected based on their compatibility probabilities.

The wardrobe consists of items categorized as either style 0 or style 1. The probabilities of pairing garments from these styles are defined in a dictionary format, with probabilities listed for connections between style 0 and style 1, as well as connections within each style.

Your task is to leverage a tool called "FashionGraphX" to create a preference graph reflecting these garment connections, where vertices represent individual garments, and edges represent the likelihood of pairing garments based on their styles and defined probabilities.
### Response:
from igraph import Graph

# Generating a preference graph with type-based connection probabilities
vertex_types = [0, 1, 0, 1, 0] # Define the type of each vertex
type_probabilities = {(0, 0): 0.1, (0, 1): 0.8, (1, 0): 0.8, (1, 1): 0.1} # Define connection probabilities based on types

# Create the preference graph
g = Graph.Preference(len(vertex_types), type_dist=vertex_types, pref_matrix=type_probabilities)

print(g)
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