gradient descent linear regression

training data set. The error measure used here is the squared error: This section tests: Logistic Regression Error Calculate Ein (W) e_{in} (W) using a matrix: Ein (W) e_{in} (W) is a continuous, micro-convex function, so now to find a Wlin W_{lin} makes ∇ein (Wlin) =0 \nabla e_{in} (W_{lin}) = 0. Because the blogger had already learned Andrew Ng's machine learning in front of him, the derivation process was omitted. Summing up, the

I believe that we have learned the linear regression of mathematical statistics (linear regression), this article will explain the univariate linear regression and write out the use of least squares method (least squares) In order

__': dataarr, labelmat = loaddataset () # print dataarr print gradascent (dataarr, labelmat) Important Notes: Sigmoid function, which is a function used to simulate the 0-1 step process. When the given number of independent variables X is more and the number of dense sets, the closer the function is to vertical jump at x = 0, the function value is rounded to 0 or 1, which is the process of determining the category. The gradient rise method in the

, gradient method, and BFGS; The constraints of regular expressions can be viewed as the penalty function method or the Laplace Multiplier Method in the "constraint optimization problem. ========================================================== ================7.4 robust linear regression This section can be seen as: the least squares cost function in the use o

Linear regression ExercisesFollow Andrew Ng and do the exercises: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=DeepLearningdoc= Exercises/ex2/ex2.htmlThis section does a little exercise in linear regression, with data from the Web site above, where X is the height of the little boy,Y is the age

I. Linear Logistic Regression The Code is as follows: Import numpy as npimport pandas as pdimport matplotlib. pyplot as pltimport scipy. optimize as optimport Seaborn as SNS # Read the dataset Path = 'ex2data1.txt 'Data = PD. read_csv (path, header = none, names = ['expired', 'expired', 'admitted']) # Separate Positive and Negative datasets positive = data [DATA ['admitted']. ISIN ([1])] Negative = data [DA

shape (x) print shape (y) Plt.sca (AX) plt.plot (x, y) #ramdomgradAscent #plt. Plot (x,y[0]) #grAdascent plt.xlabel (' density ') plt.ylabel (' Ratio_sugar ') #plt. Title (' Gradascent Logistic regression ') Plt.title (' ramdom gradascent logistic regression ') plt.show () #weights =gradascent (Datamat,labelmat) Weights=rando Mgradascent (Datamat,labelmat) plotbestfit (weights)The results obtained by the

(size_data) price_data_n = Normalize (P Rice_data) Size_data_test_n = normalize (size_data_test) Price_data_test_n = Normalize (price_data_test) # Display a plot Plt.plot (Size_data, Price_data, ' ro ', label= ' Samples data ') Plt.legend () Plt.draw () Samples_number = price_data_n.size # TF graph Input X = Tf.placeholder ("float") Y = Tf.placeholder ("float") # Create a model # Set model Weights W = tf. Variable (Numpy.random.randn (), name= "weight") b = tf. Variable (Numpy.random.randn (),

ImportTensorFlow as TFImportNumPy as NPImportMatplotlib.pyplot as Plt fromSklearnImportdatasetssess=TF. Session ()#loading the iris setiris=Datasets.load_iris ()#width LengthX_vals=np.array ([x[3] forXinchIris.data]) Y_vals=np.array ([x[0] forXinchIris.data]) Learning_rate=0.05batch_size=25X_data=tf.placeholder (shape=[none,1],dtype=tf.float32) Y_data=tf.placeholder (shape=[none,1],dtype=Tf.float32) A=TF. Variable (Tf.random_normal (shape=[1,1])) b=TF. Variable (Tf.random_normal (shape=[1,1]))#A