FisherKK

def train(X, y, model_parameters, learning_rate=0.0005, iterations=20000):
    # Make prediction for every data sample
    predictions = [predict(x, model_parameters) for x in X]

    # Calculate initial cost for model - MSE
    initial_error = mse(predictions, y)
    
    print("Initial state:")
    print(" - error: {}".format(initial_error))
    print(" - parameters: {}".format(model_parameters))

FisherKK

def train(X, y, model_parameters, learning_rate=0.1, iterations=100):
    # Make prediction for every data sample
    predictions = [predict(x, model_parameters) for x in X]

    # Calculate initial cost for model - MSE
    lowest_error = mse(predictions, y)
    
    for i in range(iterations):
        # Sum up partial gradients for every data sample, for every parameter in model
        accumulated_grad_w0 = 0

FisherKK

matplotlib.rcParams.update({'font.size': 14})

plt.figure(figsize=(8, 5))
plt.scatter(df_data["size"], df_data["price"],
            edgecolor='black', linewidth='1', s=70, alpha=0.7, c="#3176f7")
plt.plot(np.arange(0, 120, 0.1), [predict([x], model_parameters) for x in np.arange(0, 120, 0.1)], c="red")
plt.text(90, 700, "y = 7.1x + 10", color="r", fontsize=14, rotation=25)
plt.xlabel("Size [m^2]")
plt.ylabel("Price [k zł]")
plt.ylim(-100, 1000)

FisherKK

# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from copy import deepcopy

# Load data from .csv
df_data = pd.read_csv("cracow_apartments.csv", sep=",")

# Used features and target value

FisherKK

# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from copy import deepcopy

# Load data from .csv
df_data = pd.read_csv("cracow_apartments.csv", sep=",")

# Used features and target value

FisherKK

# Import libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from copy import deepcopy

# Load data from .csv
df_data = pd.read_csv("cracow_apartments.csv", sep=",")

# Used features and target value

FisherKK

from copy import deepcopy

def train(X, y, model_parameters, step=0.1, iterations=100):    
    # Make prediction for every data sample
    predictions = [predict(x, model_parameters) for x in X]

    # Calculate cost for model - MSE
    lowest_error = mse(predictions, y)
    
    print("\nInitial state:")

FisherKK

from copy import deepcopy

def train(X, y, model_parameters, step=0.1, iterations=100): 
    # Make prediction for every data sample
    predictions = [predict(x, model_parameters) for x in X]

    # Calculate initial cost for model - MSE
    lowest_error = mse(predictions, y)

    for i in range(iterations):

FisherKK

Source: https://kafka-python.readthedocs.io/en/master/apidoc/KafkaConsumer.html

max_in_flight_requests_per_connection (int)

Requests are pipelined to kafka brokers up to this number of maximum requests per broker connection. Default: 5.

fetch_max_wait_ms (int)

The maximum amount of time in milliseconds the server will block before answering the fetch request if there isn’t sufficient data to immediately satisfy the requirement given by fetch_min_bytes. Default: 500.

request_timeout_ms (int)

Client request timeout in milliseconds. Default: 305000.

FisherKK

X = np.array([[0.0], [1.0], [2.0], [3.0]])
y = np.array([0.0, 2.0, 4.0, 6.0])

model_parameters = {'b': 0.0, 'w': np.array([2.0])}
y_predicted = [predict(x, model_parameters) for x in X]

plt.figure(figsize=(8, 5))
plt.yticks(np.arange(0, 7, 0.5))
plt.xticks(np.arange(0, 4, 1))
plt.ylabel("y")