165 lines
4.5 KiB
Python
165 lines
4.5 KiB
Python
training_data = [
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#zyznosc, nawodnienie, cien, kwasowosc
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['z', 'n', 's', 'z', 1],
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['z', 'n', 's', 'n', 1],
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['j', 'n', 's', 'z', 1],
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['z', 's', 's', 'n', 1],
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['j', 'n', 'c', 'n', 1],
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['z', 'n', 's', 'k', 1],
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['z', 'n', 'c', 'k', 2],
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['z', 's', 's', 'k', 2],
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['z', 's', 'c', 'k', 2],
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['j', 'n', 's', 'k', 2],
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['z', 's', 'c', 'z', 3],
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['j', 'n', 's', 'n', 3]
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]
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header = ["zyznosc", "nawodnienie", "cien", "kwasowosc", "wybor"]
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def class_counts(rows):
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counts = {}
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for row in rows:
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label = row[-1]
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if label not in counts:
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counts[label] = 0
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counts[label] += 1
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return counts
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def is_numeric(value):
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return isinstance(value, int) or isinstance(value, float)
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class Question:
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def __init__(self, column, value):
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self.column = column
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self.value = value
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def match(self, example):
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val = example[self.column]
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if is_numeric(val):
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return val >= self.value
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else:
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return val == self.value
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def __repr__(self):
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condition = "=="
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if is_numeric(self.value):
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condition = ">="
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return "Czy %s %s %s?" % (
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header[self.column], condition, str(self.value))
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def partition(rows, question):
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true_rows, false_rows = [], []
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for row in rows:
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if question.match(row):
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true_rows.append(row)
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else:
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false_rows.append(row)
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return true_rows, false_rows
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def gini(rows):
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counts = class_counts(rows)
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impurity = 1
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for lbl in counts:
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prob_of_lbl = counts[lbl] / float(len(rows))
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impurity -= prob_of_lbl**2
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return impurity
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def info_gain(left, right, current_uncertainty):
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p = float(len(left)) / (len(left) + len(right))
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return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
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def find_best_split(rows):
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best_gain = 0
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best_question = None
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current_uncertainty = gini(rows)
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n_features = len(rows[0]) - 1
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for col in range(n_features):
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values = set([row[col] for row in rows])
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for val in values:
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question = Question(col, val)
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true_rows, false_rows = partition(rows, question)
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if len(true_rows) == 0 or len(false_rows) == 0:
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continue
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gain = info_gain(true_rows, false_rows, current_uncertainty)
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if gain >= best_gain:
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best_gain, best_question = gain, question
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return best_gain, best_question
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class Leaf:
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def __init__(self, rows):
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self.predictions = class_counts(rows)
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class Decision_Node:
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def __init__(self,
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question,
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true_branch,
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false_branch):
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self.question = question
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self.true_branch = true_branch
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self.false_branch = false_branch
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def build_tree(rows):
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gain, question = find_best_split(rows)
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if gain == 0:
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return Leaf(rows)
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true_rows, false_rows = partition(rows, question)
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true_branch = build_tree(true_rows)
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false_branch = build_tree(false_rows)
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return Decision_Node(question, true_branch, false_branch)
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def print_tree(node, spacing=""):
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if isinstance(node, Leaf):
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print (spacing + "Predict", node.predictions)
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return
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print (spacing + str(node.question))
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print (spacing + '--> True:')
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print_tree(node.true_branch, spacing + " ")
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print (spacing + '--> False:')
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print_tree(node.false_branch, spacing + " ")
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my_tree = build_tree(training_data)
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print_tree(my_tree)
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def classify(row, node):
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if isinstance(node, Leaf):
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return node.predictions
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if node.question.match(row):
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return classify(row, node.true_branch)
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else:
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return classify(row, node.false_branch)
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def print_leaf(counts):
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total = sum(counts.values()) * 1.0
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probs = {}
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for lbl in counts.keys():
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probs[lbl] = str(int(counts[lbl] / total * 100)) + "%"
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return probs
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with open( 'dane.txt', "r" ) as f:
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testing_data = [ line.split() for line in f ]
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file = open("decyzje.txt", "w")
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file.write("")
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file.close()
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for row in testing_data:
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pom = print_leaf(classify(row, my_tree))
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f = open("decyzje.txt", "a")
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if pom == {1: '100%'}:
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f.write("B\n")
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if pom == {2: '100%'}:
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f.write("Z\n")
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if pom == {3: '100%'}:
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f.write(".\n")
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f.close() |