def main():
data = DataManager.load_data("data/data_banknote_authentication.txt")
# remove break lines
data = np.array([item.strip("\n") for item in data])
data = np.array([item.split(',') for item in data])
data = data.astype(np.float)
# 0 for authentic and 1 for inauthentic
df = pd.DataFrame(data)
df[4] = df[4].astype(int)
authentic = df[df[4] == 0]
inauthentic = df[df[4] == 1]
X = df.iloc[np.r_[0:200, 1100:1300], [0, 3]].values
y = [0 if x < 200 else 1 for x in range(400)]
plt.scatter(X[:200, 0],
X[:200, 1],
color='red',
marker='o',
label='authentic')
plt.scatter(X[200:400, 0],
X[200:400, 1],
color='blue',
marker='x',
label='inauthentic')
plt.xlabel('variance of Wavelet Transformed image')
plt.ylabel('entropy of image')
plt.legend(loc='upper left')
plt.show()
ppn = Perceptron(eta=0.1, n_iter=10)
ppn.fit(X, y)
plot_decision_regions(X, y, clasiifier=ppn)
plt.xlabel('variance of Wavelet Transformed image')
plt.ylabel('entropy of image')
plt.legend(loc='upper left')
plt.show()
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
ada = AdalineGD(n_iter=10, eta=0.01)
ada.fit(X_std, y)
plot_decision_regions(X_std, y, clasiifier=ada)
plt.title('Adaline - gradient descent')
plt.xlabel('variance of Wavelet Transformed image')
plt.ylabel('entropy of image')
plt.legend(loc='upper left')
plt.show()
def test_tashizan(self):
"""test method for adaline
"""
# 初期化
expected = np.array([1.59872116e-16, -1.26256159e-01, 1.10479201e+00])
df = pd.read_csv('../tests/data/iris.data', header=None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
# テスト実行
ad = AdalineGD(n_iter=15, eta=0.01)
ad.fit(X_std, y)
actual = ad.w_
# Assert
np.testing.assert_array_almost_equal(expected, actual, 2)
def main():
# prepare training data and target variable
features = ['sepal length (cm)', 'petal length (cm)']
labels = ['setosa', 'versicolor']
D = IrisData(features, labels)
X = D.X
y = np.where(D.y == 0, -1, 1)
# standardize training data
X_std = np.copy(X)
for i in range(len(labels)):
X_std[:, i] = (X[:, i] - X[:, i].mean()) / X[:, i].std()
# fit classifiers
classifiers = [
AdalineGD(eta=0.01, n_iter=10).fit(X, y),
AdalineGD(eta=0.0001, n_iter=10).fit(X, y),
AdalineGD(eta=0.01, n_iter=15).fit(X_std, y),
AdalineSGD(eta=0.01, n_iter=15).fit(X_std, y)
]
# show history of costs
for classifier in classifiers:
plot_update_history(classifier)
# show decision regions
plot_decision_regions(X_std,
y,
classifier=classifiers[2],
xlabel='sepal length [standardized]',
ylabel='petal lnegth [standardized]')
plot_decision_regions(X_std,
y,
classifier=classifiers[3],
xlabel='sepal length [standardized]',
ylabel='petal lnegth [standardized]')
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
np.arange(x2_min, x2_max, resolution))
Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
Z = Z.reshape(xx1.shape)
plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
plt.xlim(xx1.min(), xx1.max())
plt.ylim(xx2.min(), xx2.max())
# plot class samples
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
c=cmap(idx),
marker=markers[idx],
label=cl)
ada = AdalineGD(n_iter=15, eta=0.01)
ada.fit(X_std, y)
plot_decision_regions(X_std, y, classifier=ada)
plt.title('Adaline - Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.show()
plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Sum-squared-error')
plt.show()
# Data Preparation
df = pd.read_csv('raschka/data/iris.data', header=None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
# 標準化(Zvalue)
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(14, 4))
ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X_std, y)
ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(Sum-squared-error)')
ax[0].set_title("Adaline - Learning rate 0.01")
ax[0].set_xlim([0, 16])
ada2 = AdalineGD(n_iter=10, eta=0.001).fit(X_std, y)
ax[1].plot(range(1, len(ada2.cost_) + 1), np.log10(ada2.cost_), marker='o')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('Sum-squared-error')
ax[1].set_title('Adaline - Learning rate 0.001')
ax[1].set_xlim([0, 16])
ada3 = AdalineGD(n_iter=10, eta=0.0001).fit(X_std, y)
ax[2].plot(range(1, len(ada3.cost_) + 1), np.log10(ada3.cost_), marker='o')
def Test():
# get data
ml_data = Data()
ml_data.init_data()
ml_data.view_initial_data()
# test perceptron
ppn = Perceptron(eta=0.1, n_iter=10)
ppn.fit(ml_data.X, ml_data.y)
plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Number of updates')
plt.show()
plot = ml_data.plot_decision_regions(ml_data.X, ml_data.y, classifier=ppn)
plot.xlabel('sepal length')
plot.ylabel('petal length')
plot.legend(loc='upper left')
plot.show()
# Adaline implementation
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
ada1 = AdalineGD(n_iter=10, eta=0.1).fit(ml_data.X, ml_data.y)
ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')
ax[0].set_xlabel('Epoch')
ax[0].set_ylabel('log(Sum Squared error)')
ax[0].set_title('Adaline - Learning rate 0.01')
ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(ml_data.X, ml_data.y)
ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')
ax[1].set_xlabel('Epoch')
ax[1].set_ylabel('Sum Squared error')
ax[1].set_title('Adaline - Learning rate 0.0001')
plt.show()
# Adaline with feature scaling
ml_data.standard_scaled()
ada = AdalineGD(n_iter=10, eta=0.01)
ada.fit(ml_data.X_std, ml_data.y)
plot = ml_data.plot_decision_regions(ml_data.X_std,
ml_data.y,
classifier=ada)
plot.title('Adaline - Gradinet Descent')
plot.xlabel('sepal length')
plot.ylabel('petal length')
plot.legend(loc='upper left')
plot.tight_layout()
plot.show()
plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Sum Squared error')
plt.show()
# Adaline with stochastic gradient
ada3 = AdalineGD(n_iter=15, eta=0.01, random_state=1)
ada3.fit(ml_data.X_std, ml_data.y)
plot = ml_data.plot_decision_regions(ml_data.X_std,
ml_data.y,
classifier=ada3)
plot.title('Adaline - Stochastic Gradient Descent')
plot.xlabel('sepal length')
plot.ylabel('petal length')
plot.legend(loc='upper left')
plot.show()
plt.plot(range(1, len(ada3.cost_) + 1), ada3.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Average cost')
plt.show()
# only select the setosa and versicolor species
data = data.loc[(data["Class label"] == "Iris-setosa") |
(data["Class label"] == "Iris-versicolor")]
# convert species labels to integers, -1 for setosa and 1 for versicolor
data["Class label"] = np.where(data["Class label"]=="Iris-setosa", -1, 1)
# split data into feature matrix (only "Sepal length" and "Petal length" are
# used) and target vector
X = data[["Sepal length", "Petal length"]]
y = data["Class label"]
# create a plot of the cost function against the iteration number for learning
# rates of 0.01 and 0.0001
iris_ada1 = AdalineGD(n_iterations=10, eta=0.01)
iris_ada1.fit(X.values, y.values)
iris_ada2 = AdalineGD(n_iterations=10, eta=0.0001)
iris_ada2.fit(X.values, y.values)
fig1, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))
ax[0].plot(range(1, iris_ada1.n_iterations + 1), np.log10(iris_ada1.cost_),
marker="o")
ax[0].set(xlabel="Iterations", ylabel="log(SSE)",
title="Adaline - Learning rate 0.01")
ax[1].plot(range(1, iris_ada2.n_iterations + 1), iris_ada2.cost_,
marker="o")
ax[1].set(xlabel="Iterations", ylabel="SSE",
import numpy as np
from adaline import AdalineGD
agd = AdalineGD(eta=0.001)
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [0, 1, 1], [7, 8, 8], [7, 7,
7]])
Y = np.array([-1, -1, 1, -1, 1, 1])
agd.fit(X, Y)
print(agd.predict(X))
import numpy as np
from mlxtend.plotting import plot_decision_regions
from adaline import AdalineGD
dataset = pd.read_csv('iris.csv', header=None)
# print (dataset.tail())
output = dataset.iloc[0:100, 4].values
Y = np.where(output == 'Iris-setosa', -1, 1)
X = dataset.iloc[0:100, [0, 2]].values
X_std = np.copy(X)
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
obj = AdalineGD(epochs=15, learning_rate=0.01)
obj.fit(X_std, Y)
plot_decision_regions(X_std, Y, clf=obj)
plt.title('Adaline - Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.show()
plt.plot(range(1, len(obj.cost_list) + 1), obj.cost_list, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Sum-squared-error')
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from adaline import AdalineGD
dataset = pd.read_csv('iris.csv', header=None)
# print (dataset.tail())
output = dataset.iloc[0:100, 4].values
Y = np.where(output == 'Iris-setosa', -1, 1)
X = dataset.iloc[0:100, [0, 2]].values
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
obj1 = AdalineGD(epochs=10, learning_rate=0.01).fit(X, Y)
ax[0].plot(range(1,
len(obj1.cost_list) + 1),
np.log10(obj1.cost_list),
marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(Sum-squared-errors)')
ax[0].set_title('Adaline - Learning rate 0.01')
obj2 = AdalineGD(epochs=10, learning_rate=0.0001).fit(X, Y)
ax[1].plot(range(1, len(obj2.cost_list) + 1), obj2.cost_list, marker='o')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('Sum-squared-errors')
ax[1].set_title('Adaline - Learning rate 0.0001')
plt.show()
machine-learning-databases/iris/iris.data'
df = pd.read_csv(s, header=None, encoding='utf-8')
# 1〜100行目の目的変数の抽出と変換
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
# 1〜100行目の1、3列目(今回使うデータ)を抽出
X = df.iloc[0:100, [0, 2]].values
# 描画領域を1行2列に分割
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
# 勾配降下法によるADALINEの学習(学習率 = 0.01)
ada1 = AdalineGD(n_iter=10, eta=0.01).fit(X, y)
ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(Sum-squared-error)')
ax[0].set_title('Adaline - Learning rate 0.01')
# 勾配降下法によるADALINEの学習(学習率 = 0.0001)
ada2 = AdalineGD(n_iter=10, eta=0.0001).fit(X, y)
ax[1].plot(range(1, len(ada2.cost_) + 1), ada2.cost_, marker='o')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('Sum-squared-error')
ax[1].set_title('Adaline - Learning rate 0.0001')
plt.show()
# データのスケーリング(標準化)を行う
plt.clf()
plot_decision_regions(X2, y2, clf=ppn)
plt.show()
plt.clf()
plt.plot(range(1, len(ppn.errors_)+1), ppn.errors_, marker='o')
plt.xlabel('Iterations')
plt.ylabel('Missclassifications')
plt.show()
########### Adaline ###########
from adaline import AdalineGD
ada = AdalineGD(epochs=10, eta=0.01).train(X, y)
plt.clf()
plt.plot(range(1, len(ada.cost_)+1), np.log10(ada.cost_), marker='o')
plt.xlabel('Iterations')
plt.ylabel('log(Sum-squared-error)')
plt.title('Adaline - Learning rate 0.01')
plt.show()
ada = AdalineGD(epochs=10, eta=0.001).train(X, y)
plt.clf()
plt.plot(range(1, len(ada.cost_)+1), ada.cost_, marker='o')
plt.xlabel('Iterations')
plt.ylabel('Sum-squared-error')
plt.title('Adaline - Learning rate 0.0001')
plt.show()
plt.contourf(xx, yy, Z, alpha=0.3, cmap=cmap)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 0],
y=X[y == cl, 1],
alpha=0.8,
c=colors[idx],
marker=markers[idx],
label=cl,
edgecolor="black")
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))
ada1 = AdalineGD(eta=0.01, n_itr=10).fit(X, y)
ax[0].plot(range(1, len(ada1.cost_) + 1), np.log10(ada1.cost_), marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(sum_squarred error)')
ax[0].set_title('Adaline learning rate 0.01')
ada2 = AdalineGD(eta=0.00001, n_itr=10).fit(X, y)
ax[1].plot(range(1, len(ada2.cost_) + 1), np.log10(ada2.cost_), marker='o')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('log(sum squarred errors)')
ax[1].set_title('Adaline learning rate 0.00001')
plt.show()
X_std = np.copy(X)
X_std[:, 0] = (X_std[:, 0] - X_std[:, 0].mean()) / X_std[:, 0].std()
X_std[:, 1] = (X_std[:, 1] - X_std[:, 1].mean()) / X_std[:, 1].std()
from matplotlib import style
from adaline import AdalineGD
if __name__ == '__main__':
# iris.data 품종 데이터를 읽어오는 부분, perceptron_iris와 동일함
df = pd.read_csv('/Users/narun/Desktop/mylib/hwang/dataSet/iris.data',
header=None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
# learning rate = 0.01로 두고 아달라인을 수행
# 결과값을 보면 값이 점점 커져서 J(w)값이 발산해버린다. 원하는 값을 찾을 수 없다.
adal = AdalineGD(eta=0.01, n_iter=10).fit(X, y)
ax[0].plot(range(1, len(adal.cost_) + 1), np.log10(adal.cost_), marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(SQE)')
ax[0].set_title('Adaline - Learning rate 0.01')
# learning rate = 0.0001로 두고 아달라인을 수행
# 결과값을 보면 값이 점점 작아져서 J(w)값이 0에 수렴한다. 원하는 값을 찾을 수 있다.
adal2 = AdalineGD(eta=0.0001, n_iter=10).fit(X, y)
ax[1].plot(range(1,
len(adal2.cost_) + 1),
np.log10(adal2.cost_),
marker='o')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('log(SQE)')
ax[1].set_title('Adaline - Learning rate 0.0001')
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import style
from adaline import AdalineGD
if __name__ == '__main__':
# iris.data 품종 데이터를 읽어오는 부분, perceptron_iris와 동일함
df = pd.read_csv('/Users/narun/Desktop/mylib/hwang/dataSet/iris.data', header = None)
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', -1, 1)
X = df.iloc[0:100, [0, 2]].values
X_std = np.copy(X) # X의 값을 복사해서 X_std에 저장
# Iris.data에서 꽃받침 길이와 꽃잎 길이의 표준화한 값을 X_std에 할당하는 부분
# numpy의 mean()은 numpy 배열에 있는 값들의 평균을 구한다.
# numpy의 std()는 numpy 배열에 있는 값들의 표준편차를 구한다.
X_std[:, 0] = (X[:, 0] - X[:, 0].mean()) / X[:, 0].std()
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std()
# learning rate를 0.01로, 반복회수를 15로 두고, X_std를 아달라인으로 머신러닝을 수행한다.
adal = AdalineGD(eta = 0.01, n_iter = 15).fit(X_std, y)
plt.plot(range(1, len(adal.cost_) + 1), adal.cost_, marker = 'o')
plt.xlabel('Epochs')
plt.ylabel('SSE')
plt.title('Adaline Standardized - Learning rate 0.01')
plt.show()