import numpy as np import matplotlib.pyplot as plt ########################### # # Perceptron Object # # ########################## class Perceptron(object): def __init__(self, eta=0.1, iter=50): self.eta = eta self.iter = iter def fit(self, X, y): self.w_ = np.zeros(X.shape[1]) self.errors_ = [] for _ in range(self.iter): error = 0 for xi, target in zip(X, y): update = self.eta * (target - self.predict(xi)) self.w_[:] += update * xi error += int(update != 0.0) self.errors_.append(error) return self def predict(self, x): return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1) ########################### # # Linear Regression Object # # ########################## class LinearRegression(object): def __init__(self, eta=0.1, iter=50): self.eta = eta self.iter = iter def fit(self, X, y): self.w_ = np.zeros(X.shape[1]) self.cost_ = [] for i in range(self.iter): output = self.net_input(X) error = y - output self.w_[:] += self.eta * np.dot(X.T, error) cost = np.dot(error.T, error) / 2.0 self.cost_.append(cost) return self def net_input(self, X): return np.dot(X, self.w_[:]) def predict(self, x): return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1) ########################### # # Logistic Regression Object # # ########################## class LogisticRegression(object): def __init__(self, eta=0.1, iter=50): self.eta = eta self.iter = iter def fit(self, X, y): self.w_ = np.zeros(X.shape[1]) self.cost_ = [] for i in range(self.iter): s = np.dot(X, self.w_[:]) grad = np.dot(X.T, y) / (1 + np.exp(np.dot(y, s.T))) error = -grad.sum() / len(y) self.w_[:] += self.eta * error cost = np.dot(error.T, error) / 2.0 self.cost_.append(cost) return self def predict(self, x): return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1) ########################### # # main() # # ########################## def main(): dataFile = ("C:\\Users\\mspat\\Desktop\\iris.txt") X, y = formatIris(dataFile) # X includes 1 for the bias: X = [[1,x11, x12], [1,x21,x22], .... [1,xN1, xN2]]T # X = np.array(([1, 5.7, 4.2], [1, 5.7, 4.1], [1, 7.1, 6.7], [1, 6.7, 5])) # y = np.array([1, 1, -1, 1]) eta = 0.1 # learning rate X_std = np.copy(X) X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std() # sepal len X_std[:, 2] = (X[:, 2] - X[:, 2].mean()) / X[:, 2].std() # petal len # Perceptron ppn = Perceptron(eta) # , iter=len(y)) ppn.fit(X, y) # Linear Regression linR = LinearRegression(eta) linR.fit(X, y) # Logistic Regression logR = LogisticRegression(eta) logR.fit(X, y) plot_regions(X, y, classifier=ppn) plot_regions(X, y, classifier=linR) plot_regions(X, y, classifier=logR) ########################### # # iris data file # get sepal and petal lengths of iris-vericolor and iris-virginica # ########################## def formatIris(dataset): X = [] y = [] with open(dataset, 'r') as f: for line in f: sline = line.replace('\n', '') t = sline.split(',') if t[4] == 'Iris-versicolor': val = 1 elif t[4] == 'Iris-virginica': val = -1 else: val = 0 X.append([1, float(t[0]), float(t[2])]) # [1, sepal len, petal len] y.append(val) return np.array(X), np.array(y) ########################### # # Plot regions # # ########################## from matplotlib.colors import ListedColormap def plot_regions(X, y, classifier, resolution=0.02): # setup marker generato and clor map markers = ('o', 'x', 's', '^', 'v') colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan') cmap = ListedColormap(colors[:len(np.unique(y))]) # plot the decision surface x1_min, x1_max = X[:, 1].min() - 1, X[:, 1].max() + 1 x2_min, x2_max = X[:, 2].min() - 1, X[:, 2].max() + 1 xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution)) coordinates = np.array([np.ones(len(xx1.ravel())), xx1.ravel(), xx2.ravel()]) region = np.dot(coordinates.T, classifier.w_[:]) Z = np.where(region >= 0.0, 1, -1) 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()) for idx, cl in enumerate(np.unique(y)): plt.scatter(x=X[y == cl, 1], y=X[y == cl, 2], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl) plt.xlabel('sepal length') plt.ylabel('petal length') plt.legend(loc='upper right') plt.show() main()

import numpy as np import matplotlib.pyplot as plt ########################### # # Perceptron Object # # ########################## class Perceptron(object): def init(self, eta=0.1, iter=50): self.eta = eta self.iter = iter def fit(self, X, y): self.w_ = np.zeros(X.shape[1]) self.errors_ = [] for _ in range(self.iter): error = 0 for xi, target in zip(X, y): update = self.eta * (target - self.predict(xi)) self.w_[:] += update * xi error += int(update != 0.0) self.errors_.append(error) return self def predict(self, x): return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1) ########################### # # Linear Regression Object # # ########################## class LinearRegression(object): def init(self, eta=0.1, iter=50): self.eta = eta self.iter = iter def fit(self, X, y): self.w_ = np.zeros(X.shape[1]) self.cost_ = [] for i in range(self.iter): output = self.net_input(X) error = y - output self.w_[:] += self.eta * np.dot(X.T, error) cost = np.dot(error.T, error) / 2.0 self.cost_.append(cost) return self def net_input(self, X): return np.dot(X, self.w_[:]) def predict(self, x): return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1) ########################### # # Logistic Regression Object # # ########################## class LogisticRegression(object): def init(self, eta=0.1, iter=50): self.eta = eta self.iter = iter def fit(self, X, y): self.w_ = np.zeros(X.shape[1]) self.cost_ = [] for i in range(self.iter): s = np.dot(X, self.w_[:]) grad = np.dot(X.T, y) / (1 + np.exp(np.dot(y, s.T))) error = -grad.sum() / len(y) self.w_[:] += self.eta * error cost = np.dot(error.T, error) / 2.0 self.cost_.append(cost) return self def predict(self, x): return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1) ########################### # # main() # # ########################## def main(): dataFile = ("C:\\Users\\mspat\\Desktop\\iris.txt") X, y = formatIris(dataFile) # X includes 1 for the bias: X = [[1,x11, x12], [1,x21,x22], .... [1,xN1, xN2]]T # X = np.array(([1, 5.7, 4.2], [1, 5.7, 4.1], [1, 7.1, 6.7], [1, 6.7, 5])) # y = np.array([1, 1, -1, 1]) eta = 0.1 # learning rate X_std = np.copy(X) X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std() # sepal len X_std[:, 2] = (X[:, 2] - X[:, 2].mean()) / X[:, 2].std() # petal len # Perceptron ppn = Perceptron(eta) # , iter=len(y)) ppn.fit(X, y) # Linear Regression linR = LinearRegression(eta) linR.fit(X, y) # Logistic Regression logR = LogisticRegression(eta) logR.fit(X, y) plot_regions(X, y, classifier=ppn) plot_regions(X, y, classifier=linR) plot_regions(X, y, classifier=logR) ########################### # # iris data file # get sepal and petal lengths of iris-vericolor and iris-virginica # ########################## def formatIris(dataset): X = [] y = [] with open(dataset, 'r') as f: for line in f: sline = line.replace('\n', '') t = sline.split(',') if t[4] == 'Iris-versicolor': val = 1 elif t[4] == 'Iris-virginica': val = -1 else: val = 0 X.append([1, float(t[0]), float(t[2])]) # [1, sepal len, petal len] y.append(val) return np.array(X), np.array(y) ########################### # # Plot regions # # ########################## from matplotlib.colors import ListedColormap def plot_regions(X, y, classifier, resolution=0.02): # setup marker generato and clor map markers = ('o', 'x', 's', '^', 'v') colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan') cmap = ListedColormap(colors[:len(np.unique(y))]) # plot the decision surface x1_min, x1_max = X[:, 1].min() - 1, X[:, 1].max() + 1 x2_min, x2_max = X[:, 2].min() - 1, X[:, 2].max() + 1 xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution)) coordinates = np.array([np.ones(len(xx1.ravel())), xx1.ravel(), xx2.ravel()]) region = np.dot(coordinates.T, classifier.w_[:]) Z = np.where(region >= 0.0, 1, -1) 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()) for idx, cl in enumerate(np.unique(y)): plt.scatter(x=X[y == cl, 1], y=X[y == cl, 2], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl) plt.xlabel('sepal length') plt.ylabel('petal length') plt.legend(loc='upper right') plt.show() main()

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Related questions

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Question

( can you solve my error here is my python code and errors can you fixed my code plz as soon as)

import numpy as np
import matplotlib.pyplot as plt

###########################
#
# Perceptron Object
#
#
##########################
class Perceptron(object):
def __init__(self, eta=0.1, iter=50):
self.eta = eta
self.iter = iter

def fit(self, X, y):
self.w_ = np.zeros(X.shape[1])
self.errors_ = []

for _ in range(self.iter):
error = 0
for xi, target in zip(X, y):
update = self.eta * (target - self.predict(xi))
self.w_[:] += update * xi
error += int(update != 0.0)
self.errors_.append(error)
return self

def predict(self, x):
return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1)

###########################
#
# Linear Regression Object
#
#
##########################
class LinearRegression(object):
def __init__(self, eta=0.1, iter=50):
self.eta = eta
self.iter = iter

def fit(self, X, y):
self.w_ = np.zeros(X.shape[1])
self.cost_ = []

for i in range(self.iter):
output = self.net_input(X)
error = y - output
self.w_[:] += self.eta * np.dot(X.T, error)
cost = np.dot(error.T, error) / 2.0
self.cost_.append(cost)
return self

def net_input(self, X):
return np.dot(X, self.w_[:])

def predict(self, x):
return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1)

###########################
#
# Logistic Regression Object
#
#
##########################
class LogisticRegression(object):
def __init__(self, eta=0.1, iter=50):
self.eta = eta
self.iter = iter

def fit(self, X, y):
self.w_ = np.zeros(X.shape[1])
self.cost_ = []

for i in range(self.iter):
s = np.dot(X, self.w_[:])
grad = np.dot(X.T, y) / (1 + np.exp(np.dot(y, s.T)))
error = -grad.sum() / len(y)
self.w_[:] += self.eta * error
cost = np.dot(error.T, error) / 2.0
self.cost_.append(cost)
return self

def predict(self, x):
return np.where(np.dot(x, self.w_[:]) >= 0.0, 1, -1)

###########################
#
# main()
#
#
##########################
def main():
dataFile = ("C:\\Users\\mspat\\Desktop\\iris.txt")

X, y = formatIris(dataFile) # X includes 1 for the bias: X = [[1,x11, x12], [1,x21,x22], .... [1,xN1, xN2]]T

# X = np.array(([1, 5.7, 4.2], [1, 5.7, 4.1], [1, 7.1, 6.7], [1, 6.7, 5]))
# y = np.array([1, 1, -1, 1])

eta = 0.1 # learning rate

X_std = np.copy(X)
X_std[:, 1] = (X[:, 1] - X[:, 1].mean()) / X[:, 1].std() # sepal len
X_std[:, 2] = (X[:, 2] - X[:, 2].mean()) / X[:, 2].std() # petal len

# Perceptron
ppn = Perceptron(eta) # , iter=len(y))
ppn.fit(X, y)

# Linear Regression
linR = LinearRegression(eta)
linR.fit(X, y)

# Logistic Regression
logR = LogisticRegression(eta)
logR.fit(X, y)

plot_regions(X, y, classifier=ppn)
plot_regions(X, y, classifier=linR)
plot_regions(X, y, classifier=logR)

###########################
#
# iris data file
# get sepal and petal lengths of iris-vericolor and iris-virginica
#
##########################
def formatIris(dataset):
X = []
y = []
with open(dataset, 'r') as f:
for line in f:
sline = line.replace('\n', '')
t = sline.split(',')
if t[4] == 'Iris-versicolor':
val = 1
elif t[4] == 'Iris-virginica':
val = -1
else:
val = 0

X.append([1, float(t[0]), float(t[2])]) # [1, sepal len, petal len]
y.append(val)
return np.array(X), np.array(y)

###########################
#
# Plot regions
#
#
##########################
from matplotlib.colors import ListedColormap

def plot_regions(X, y, classifier, resolution=0.02):
# setup marker generato and clor map
markers = ('o', 'x', 's', '^', 'v')
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])

# plot the decision surface
x1_min, x1_max = X[:, 1].min() - 1, X[:, 1].max() + 1
x2_min, x2_max = X[:, 2].min() - 1, X[:, 2].max() + 1
xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution))

coordinates = np.array([np.ones(len(xx1.ravel())), xx1.ravel(), xx2.ravel()])
region = np.dot(coordinates.T, classifier.w_[:])
Z = np.where(region >= 0.0, 1, -1)
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())
for idx, cl in enumerate(np.unique(y)):
plt.scatter(x=X[y == cl, 1], y=X[y == cl, 2], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl)

plt.xlabel('sepal length')
plt.ylabel('petal length')
plt.legend(loc='upper right')
plt.show()

main()

Traceback (most recent call last):
File "C:\Users\mspat\PycharmProjects\bioinformatics hw5\hw5.py", line 192, in <module>
main()
File "C:\Users\mspat\PycharmProjects\bioinformatics hw5\hw5.py", line 171, in main
х, у %3
formatIris(datafile)
#x inciudes 1 for the bias: x =
[1,x11,x12], [1,x21,x22],...[1,xN1, xN2]]T
File "C:\Users\mspat\PycharmProjects\bioinformatics hw5\hw5.py", line 126, in formatIris
if t[4] =='Iris-versicolor':
IndexError: list index out of range
Process finished with exit code 1

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