def plot_knn_classification(n_neighbors=1):
X, y = make_forge()
x_test = np.arange(8, 12, 0.5)
y_test = (x_test - 8) * 1.4
X_test = np.c_[x_test, y_test]
clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)
test_points = discrete_scatter(X_test[:, 0],
X_test[:, 1],
clf.predict(X_test),
markers="*")
training_points = discrete_scatter(X[:, 0], X[:, 1], y)
plt.legend(
training_points + test_points,
["training class 0", "training class 1", "test pred 0", "test pred 1"])
dist = euclidean_distances(X, X_test)
closest = np.argsort(dist, axis=0)
for x, neighbors in zip(X_test, closest.T):
for neighbor in neighbors[:n_neighbors]:
plt.arrow(x[0],
x[1],
X[neighbor, 0] - x[0],
X[neighbor, 1] - x[1],
head_width=0,
fc='k',
ec='k')
def create_forge():
X, y = datasets.make_forge()
mglearn.discrete_scatter(X[:, 0], X[:, 1], y)
plt.legend(['Class 0', 'Class 1'], loc=4)
plt.xlabel('First feature')
plt.ylabel('Second feature')
print('X.shape: {}'.format(X.shape))
print('y.shape: {}'.format(y.shape))
plt.plot(X[:, 0], X[:, 1], 'x')
plt.suptitle("图2-2：forge数据集的散点图")
from sklearn.model_selection import train_test_split
from mglearn.datasets import make_forge
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
X, y = make_forge()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = KNeighborsClassifier(5).fit(X_train, y_train)
preds = clf.predict(X_test)
# Scores 0.86
print("Score {:.2f}".format(clf.score(X_test, y_test)))
def main(): #TEST
from mglearn.datasets import make_forge
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
X, y = make_forge()
X_train,X_test, y_train, y_test = train_test_split(X,y, random_state=0)
n = 5
md = KnnClassifier(n_neighbors=n)
md.fit(X_train,y_train)
y_pred_my = md.predict(X_test)
md = KNeighborsClassifier(n_neighbors=n)
md.fit(X_train,y_train)
y_pred_sk = md.predict(X_test)
ab = y_pred_my == y_pred_sk
print(ab.all())
########################
import numpy as np
X = np.random.randint(0,100, size=(50,3))
y = np.random.randint(0,3, size=len(X))
X_train,X_test, y_train, y_test = train_test_split(X,y, random_state=0)
n = 7
md = KnnClassifier(n_neighbors=n)
md.fit(X_train,y_train)
y_pred_my = md.predict(X_test)
md = KNeighborsClassifier(n_neighbors=n)
md.fit(X_train,y_train)
y_pred_sk = md.predict(X_test)
ab = y_pred_my == y_pred_sk
print(ab.all())
############################################
from pylab import scatter, plot, show
from sklearn.neighbors import KNeighborsRegressor
from mglearn.datasets import make_wave
X,y = make_wave()
import numpy as np
X_test = np.arange(-3,3,0.001).reshape(-1,1)
n=4
md = KNeighborsRegressor(n_neighbors=n).fit(X,y)
scatter(X.ravel(), y, marker='.', color='blue')
y_pred = md.predict(X_test)
plot(X_test.ravel(), y_pred, linewidth=0.4)
md = KnnRegressor(n_neighbors=n).fit(X,y)
y_pred = md.predict(X_test)
plot(X_test.ravel(), y_pred, linewidth=0.6, linestyle='--', alpha=0.5, color='red')
show()
#------------------------------------
X,y = np.split(np.random.randint(-10,10, size=(10,5)), axis=1, indices_or_sections=[4])
y = y.ravel()
n = 5
md = KnnRegressor(n_neighbors=n).fit(X,y)
X_test = np.random.randint(-10,10,size=(25,4))
y_pred_my = md.predict(X_test)
md = KNeighborsRegressor(n_neighbors=n).fit(X,y)
y_pred_sk = md.predict(X_test)
b = np.allclose(y_pred_my, y_pred_sk)
print(b)
from sklearn.model_selection import train_test_split
from mglearn import datasets
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import mglearn
X, y = datasets.make_forge()
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
clf = KNeighborsClassifier(n_neighbors=5)
clf.fit(X_train, y_train)
print(f"Test set predicitons: {clf.predict(X_test)}")
print(f"Test set accuracy: {clf.score(X_test, y_test)}")
fig, axes = plt.subplots(1, 3, figsize=(10, 3))
for n_neighbors, ax in zip([1, 3, 9], axes):
clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)
mglearn.plots.plot_2d_separator(clf,
X,
fill=True,
eps=0.5,
ax=ax,
alpha=.4)
mglearn.discrete_scatter(X[:, 0])
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2019-08-17 18:10 # @Author : liupan # @Site : # @File : test_make_forge.py # @Software: PyCharm from mglearn.datasets import make_forge make_forge()
@author: Sven
'''
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import mglearn
import mglearn.datasets as mgd
#from IPython.display import display
if __name__ == '__main__':
pass
# generate dataset
X, y = mgd.make_forge()
print(X)
print(y)
print("X.shape: {}".format(X.shape))
# plot dataset
mglearn.discrete_scatter(X[:, 0], X[:, 1], y)
plt.legend(["Class 0", "Class 1"], loc=4)
plt.xlabel("First feature")
plt.ylabel("Second feature")
# KNN
from sklearn.model_selection import train_test_split
