mirror of
https://github.com/TheAlgorithms/Python.git
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272 lines
8.4 KiB
Python
272 lines
8.4 KiB
Python
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import make_moons
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import warnings
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def euclidean_distance(q, p):
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"""
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Calculates the Euclidean distance
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between points q and p
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Distance can only be calculated between numeric values
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>>> euclidean_distance([1,'a'],[1,2])
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Traceback (most recent call last):
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...
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ValueError: Non-numeric input detected
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The dimentions of both the points must be the same
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>>> euclidean_distance([1,1,1],[1,2])
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Traceback (most recent call last):
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...
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ValueError: expected dimensions to be 2-d, instead got p:3 and q:2
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Supports only two dimentional points
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>>> euclidean_distance([1,1,1],[1,2])
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Traceback (most recent call last):
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...
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ValueError: expected dimensions to be 2-d, instead got p:3 and q:2
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Input should be in the format [x,y] or (x,y)
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>>> euclidean_distance(1,2)
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Traceback (most recent call last):
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...
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TypeError: inputs must be iterable, either list [x,y] or tuple (x,y)
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"""
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if not hasattr(q, "__iter__") or not hasattr(p, "__iter__"):
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raise TypeError("inputs must be iterable, either list [x,y] or tuple (x,y)")
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if isinstance(q, str) or isinstance(p, str):
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raise TypeError("inputs cannot be str")
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if len(q) != 2 or len(p) != 2:
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raise ValueError(
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"expected dimensions to be 2-d, instead got p:{} and q:{}".format(
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len(q), len(p)
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)
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)
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for num in q + p:
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try:
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num = int(num)
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except:
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raise ValueError("Non-numeric input detected")
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a = pow((q[0] - p[0]), 2)
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b = pow((q[1] - p[1]), 2)
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return pow((a + b), 0.5)
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def find_neighbors(db, q, eps):
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"""
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Finds all points in the db that
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are within a distance of eps from Q
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eps value should be a number
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>>> find_neighbors({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}}, (2,5),'a')
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Traceback (most recent call last):
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...
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ValueError: eps should be either int or float
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Q must be a 2-d point as list or tuple
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>>> find_neighbors({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}}, 2, 0.5)
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Traceback (most recent call last):
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...
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TypeError: Q must a 2-dimentional point in the format (x,y) or [x,y]
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Points must be in correct format
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>>> find_neighbors([], (2,2) ,0.4)
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Traceback (most recent call last):
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...
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TypeError: db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}}
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"""
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if not isinstance(eps, (int, float)):
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raise ValueError("eps should be either int or float")
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if not hasattr(q, "__iter__"):
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raise TypeError("Q must a 2-dimentional point in the format (x,y) or [x,y]")
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if not isinstance(db, dict):
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raise TypeError(
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"db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}}"
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)
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return [p for p in db if euclidean_distance(q, p) <= eps]
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def plot_cluster(db, clusters, ax):
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"""
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Extracts all the points in the db and puts them together
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as seperate clusters and finally plots them
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db cannot be empty
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>>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5))
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>>> plot_cluster({},[1,2], axes[1] )
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Traceback (most recent call last):
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...
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Exception: db is empty. No points to cluster
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clusters cannot be empty
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>>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5))
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>>> plot_cluster({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},[],axes[1] )
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Traceback (most recent call last):
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...
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Exception: nothing to cluster. Empty clusters
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clusters cannot be empty
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>>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5))
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>>> plot_cluster({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},[],axes[1] )
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Traceback (most recent call last):
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...
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Exception: nothing to cluster. Empty clusters
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ax must be a plotable
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>>> plot_cluster({ (1,2):{'label':'1'}, (2,3):{'label':'2'}},[1,2], [] )
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Traceback (most recent call last):
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...
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TypeError: ax must be an slot in a matplotlib figure
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"""
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if len(db) == 0:
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raise Exception("db is empty. No points to cluster")
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if len(clusters) == 0:
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raise Exception("nothing to cluster. Empty clusters")
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if not hasattr(ax, "plot"):
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raise TypeError("ax must be an slot in a matplotlib figure")
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temp = []
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noise = []
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for i in clusters:
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stack = []
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for k, v in db.items():
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if v["label"] == i:
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stack.append(k)
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elif v["label"] == "noise":
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noise.append(k)
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temp.append(stack)
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color = iter(plt.cm.rainbow(np.linspace(0, 1, len(clusters))))
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for i in range(0, len(temp)):
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c = next(color)
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x = [l[0] for l in temp[i]]
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y = [l[1] for l in temp[i]]
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ax.plot(x, y, "ro", c=c)
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x = [l[0] for l in noise]
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y = [l[1] for l in noise]
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ax.plot(x, y, "ro", c="0")
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def dbscan(db, eps, min_pts):
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"""
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Implementation of the DBSCAN algorithm
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Points must be in correct format
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>>> dbscan([], (2,2) ,0.4)
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Traceback (most recent call last):
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...
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TypeError: db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}}
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eps value should be a number
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>>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},'a',20 )
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Traceback (most recent call last):
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...
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ValueError: eps should be either int or float
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min_pts value should be an integer
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>>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},0.4,20.0 )
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Traceback (most recent call last):
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...
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ValueError: min_pts should be int
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db cannot be empty
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>>> dbscan({},0.4,20.0 )
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Traceback (most recent call last):
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...
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Exception: db is empty, nothing to cluster
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min_pts cannot be negative
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>>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}}, 0.4, -20)
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Traceback (most recent call last):
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...
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ValueError: min_pts or eps cannot be negative
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eps cannot be negative
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>>> dbscan({ (1,2):{'label':'undefined'}, (2,3):{'label':'undefined'}},-0.4, 20)
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Traceback (most recent call last):
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...
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ValueError: min_pts or eps cannot be negative
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"""
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if not isinstance(db, dict):
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raise TypeError(
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"db must be a dict of points in the format {(x,y):{'label':'boolean/undefined'}}"
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)
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if len(db) == 0:
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raise Exception("db is empty, nothing to cluster")
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if not isinstance(eps, (int, float)):
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raise ValueError("eps should be either int or float")
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if not isinstance(min_pts, int):
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raise ValueError("min_pts should be int")
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if min_pts < 0 or eps < 0:
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raise ValueError("min_pts or eps cannot be negative")
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if min_pts == 0:
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warnings.warn("min_pts is 0. Are you sure you want this ?")
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if eps == 0:
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warnings.warn("eps is 0. Are you sure you want this ?")
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clusters = []
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c = 0
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for p in db:
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if db[p]["label"] != "undefined":
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continue
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neighbors = find_neighbors(db, p, eps)
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if len(neighbors) < min_pts:
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db[p]["label"] = "noise"
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continue
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c += 1
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clusters.append(c)
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db[p]["label"] = c
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neighbors.remove(p)
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seed_set = neighbors.copy()
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while seed_set != []:
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q = seed_set.pop(0)
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if db[q]["label"] == "noise":
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db[q]["label"] = c
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if db[q]["label"] != "undefined":
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continue
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db[q]["label"] = c
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neighbors_n = find_neighbors(db, q, eps)
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if len(neighbors_n) >= min_pts:
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seed_set = seed_set + neighbors_n
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return db, clusters
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if __name__ == "__main__":
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fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(7, 5))
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x, label = make_moons(n_samples=200, noise=0.1, random_state=19)
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axes[0].plot(x[:, 0], x[:, 1], "ro")
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points = {(point[0], point[1]): {"label": "undefined"} for point in x}
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eps = 0.25
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min_pts = 12
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db, clusters = dbscan(points, eps, min_pts)
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plot_cluster(db, clusters, axes[1])
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plt.show()
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