Can someone explain how does svr_rbf.predict(dates) work? - Printable Version +- Python Forum (https://python-forum.io) +-- Forum: Python Coding (https://python-forum.io/forum-7.html) +--- Forum: Data Science (https://python-forum.io/forum-44.html) +--- Thread: Can someone explain how does svr_rbf.predict(dates) work? (/thread-8490.html) Can someone explain how does svr_rbf.predict(dates) work? - j2ee - Feb-22-2018 I am new to Python and cannot fully understand how this Python svr_rbf.predict function work. There is this predicted_price = predict_price(dates, prices, 29) which would return the predicted price in command prompt, does that mean if there is 28 rows of data in the csv, then using this 29 would predict the next day price? How does the training for RBF work in this code? Thanks. ```import csv import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt #plt.switch_backend('QT5Agg') dates = [] prices = [] def get_data(filename): with open(filename, 'r') as csvfile: csvFileReader = csv.reader(csvfile) next(csvFileReader) # skipping column names for row in csvFileReader: #dates.append(int(row[0].split('-')[0])) dates.append(float(row[0])) prices.append(float(row[1])) return def predict_price(dates, prices, x): dates = np.reshape(dates,(len(dates), 1)) # converting to matrix of n X 1 #svr_lin = SVR(kernel= 'linear', C= 1e3) #svr_poly = SVR(kernel= 'poly', C= 1e3, degree= 2) svr_rbf = SVR(kernel= 'rbf', C= 1e3, gamma= 0.1) # defining the support vector regression models svr_rbf.fit(dates, prices) # fitting the data points in the models #svr_lin.fit(dates, prices) #svr_poly.fit(dates, prices) plt.scatter(dates, prices, color= 'black', label= 'Data') # plotting the initial datapoints plt.plot(dates, svr_rbf.predict(dates), color= 'red', label= 'RBF model') # plotting the line made by the RBF kernel #plt.plot(dates,svr_lin.predict(dates), color= 'green', label= 'Linear model') # plotting the line made by linear kernel #plt.plot(dates,svr_poly.predict(dates), color= 'blue', label= 'Polynomial model') # plotting the line made by polynomial kernel plt.xlabel('Date') plt.ylabel('Price') plt.title('Support Vector Regression') plt.legend() plt.show() #return svr_rbf.predict(x)[0], svr_lin.predict(x)[0], svr_poly.predict(x)[0] return svr_rbf.predict(x)[0] get_data('aapl.csv') # calling get_data method by passing the csv file to it print ("Dates- ", dates) print ("Prices- ", prices) predicted_price = predict_price(dates, prices, 29) print(predicted_price)```