bobbywlindsey

weights = initialize_weights(train_data.shape[1])
for epoch in range(3):
    sum_of_squared_errors = 0.0
    for sample in range(train_data.shape[0]):
        vector = train_data.loc[sample].values
        label = train_label.loc[sample]
        if label == 0: label = -1
        prediction = predict(vector, weights, 'sign')
        error = label - prediction
        sum_of_squared_errors += error**2

bobbywlindsey

weights = initialize_weights(train_data.shape[1])
for epoch in range(3):
    sum_of_squared_errors = 0.0
    for sample in range(train_data.shape[0]):
        vector = train_data.loc[sample].values
        label = train_label.loc[sample]
        if label == 0: label = -1
        prediction = predict(vector, weights, 'sign')
        error = label - prediction
        sum_of_squared_errors += error**2

bobbywlindsey

weights = initialize_weights(train_data.shape[1])
for epoch in range(3):
    sum_of_squared_errors = 0.0
    for sample in range(train_data.shape[0]):
        vector = train_data.loc[sample].values
        label = train_label.loc[sample]
        prediction = predict(vector, weights, 'step')
        error = label - prediction
        sum_of_squared_errors += error**2
        weights = weights + error * vector

bobbywlindsey

weights = initialize_weights(train_data.shape[1])
sum_of_squared_errors = 0.0
for sample in range(train_data.shape[0]):
    vector = train_data.loc[sample].values
    label = train_label.loc[sample]
    prediction = predict(vector, weights, 'step')
    error = label - prediction
    sum_of_squared_errors += error**2
    weights = weights + error * vector
print(f'SSE: {sum_of_squared_errors}')

bobbywlindsey

weights = initialize_weights(train_data.shape[1])
vector = train_data.loc[0].values
label = train_label.loc[0]
prediction = predict(vector, weights, 'step')
error = label - prediction
print(f'Prediction: {prediction}, Label: {label}, Error: {error}')

bobbywlindsey

def activation(x, function_name):
    if function_name == 'step':
        return step(x)
    elif function_name == 'sign':
        return sign(x)
    else:
        raise NotImplementedError
    
    
def initialize_weights(num_columns):

bobbywlindsey

train_data = pd.DataFrame(data = data, index=list(range(len(data))), columns=['X_1', 'X_2', 'label'])
# Don't forget to add a vector of 1s for the bias weight
train_data['bias'] = np.repeat(1.0, train_data.shape[0])
train_label = train_data.label
train_data = train_data[[each for each in train_data.columns if each != 'label']]
train_data.head()

bobbywlindsey

import pandas as pd
import numpy as np

data = [[2.7810836,2.550537003,0],
        [1.465489372,2.362125076,0],
        [3.396561688,4.400293529,0],
        [1.38807019,1.850220317,0],
        [3.06407232,3.005305973,0],
        [7.627531214,2.759262235,1],
        [5.332441248,2.088626775,1],

bobbywlindsey

def sign(x):
    if x > 0:
        return 1.0
    elif x < 0:
        return -1.0
    else:
        return 0.0
    
    
def step(x):

bobbywlindsey

Keybase proof

I hereby claim:

• I am bobbywlindsey on github.
• I am bobbylindsey (https://keybase.io/bobbylindsey) on keybase.
• I have a public key ASDhPbagCzR47qYnqzdc76WQ-KqheCkZRS_v8qgjmwLB1Ao

To claim this, I am signing this object: