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Concept explainers
( 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](https://content.bartleby.com/qna-images/question/1834bd91-86c9-40fc-9c20-321ad2d6490a/ba3822dd-3a34-46a8-a0e4-5b96689dd4ab/0nf5v3f_processed.png)
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