Looking at training the Perceptron model....got error in running it

[skw24]

HI...Im trying to run the perceptron model in Pycharm professional and I keep getting the error below:

Traceback (most recent call last):
File "C:\Users\skwon\PycharmProjects\pythonProject1\iris_ds_&plot_Perceptron_10052023.py", line 87, in
ppn.fit(X, y)
File "C:\Users\skwon\PycharmProjects\pythonProject1\iris_ds&_plot_Perceptron_10052023.py", line 70, in fit
Update = self.eta * (target - self.predict(xi))
~~~~~~~^~~~~~~~~~~~~~~~~~
numpy.core._exceptions._UFuncNoLoopError: ufunc 'subtract' did not contain a loop with signature matching types (dtype('<U11'), dtype('int32')) -> None

---

Below is the Perceptron model which I ran both originally and then modified slightly for simlicity. I keep getting the same error. Any insights or help would be appreciated!

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

df = pd.read_csv('https://archive.ics.uci.edu/ml/'
'machine-learning-databases/iris/iris.data', header=None)
print(df)

select sertosa and versicolor

y = df.iloc[0:100, 4].values
x = np.where(y == 'Iris-setosa', -1, 1)

extract sepal length and petol length

X =df.iloc[0:100, [0, 2]].values

plot data

plt.scatter(X[:50, 0], X[:50, 1],
color='red', marker='o',label='setosa')
plt.scatter(X[50:100, 0], X[50:100, 1],
color='blue', marker='x', label='versicolor')

plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')

plt.tight_layout()
#plt.savefig('./images/02_06.png', dpe=300)
plt.show()

Perceptron

class Perceptron(object):
"""Perceptron classifier.

Parameters
-----------
eta : float
    Learning Rate (between 0.0 and 1.0)
n_iter : int
    Passes over the training dataset.
shuffle : bool (default: True)
    Shuffles training data every epoch if True to prevent cycles.
random_state : int (default: None)
    set random state for shuffing and initialize the weights.


Attributes
-----------
w_ : 1d-array
    Weights after fitting.
errors_ : list
    Number of misclassifications (updates) in each epoch.

"""

def _init_(self, eta=0.01, n_iter=10, shuffle=True, random_state=None, random_seed=1):
    self.eta = eta
    self.n_iter = n_iter
    self.shuffle = shuffle
    self.random_seed = random_seed
    np.random_seed(random_state)

def fit(self, X, y):
    """Fit training data.

    Parameters
    ----------
    X : {array-like}, shape = [n_samples, n_features]
        Training vectors, where n_samples is the number of samples and n_features is the number of features.
    y : array-like, shape = [n_samples]
        Target values.

    Returns
    -------
    self : object

    """
    rgen = np.random.RandomState(self.random_seed)
    self.w_ = rgen.normal(loc=0.0, scale=0.01, size=1 + X.shape[1])
    self.errors_ = []

    for _ in range(self.n_iter):
        if self.shuttle:
            X, y = self_shuffle(X, y)
        errors = 0
        for xi.target in zip(X, y):
            for _ in range(self.n_iter):
                errors = 0
            for xi, target in zip(X, y):
                Update = self.eta * (target - self.predict(xi))
                self.w_[1:] += update * xi
                self.w_[0] += update
                errors += int(update != 0.0)
            self.errors_.append(errors)
        return self

def _shuffle(self, X, y):
    """Shuffle training data"""
    r = np.random.permutation(len(y))
    return X[r], y[r]

def net_input(self, X):
    """Calculate net input"""
    return np.dot(X, self.w_[1:]) + self.w_[0]

def predict(self, X):
    """Return class label after unit step"""
    return np.where(self.net_input(X) >= 0.0, 1, -1)

ppn = Perceptron(eta=0.1, n_iter=10)

ppn.fit(X, y)

plt.plot(range(1, len(ppn.errors_) + 1), ppn.errors_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Number of updates')

plt.tight_layout()

plt.savefig('./perceptron_1.png', dpi=300)

plt.show()