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 Why I get the ValueError vokoyo Silly Frenchman Posts: 27 Threads: 16 Joined: Apr 2018 Reputation: 0 Likes received: 0 #1 Mar-16-2019, 06:38 PM (This post was last modified: Mar-16-2019, 06:38 PM by vokoyo. Edited 3 times in total.) Dear May I know why I get the error message - line 49, in _update_weights self.w_[1:]= self.eta*xi.dot(error) ValueError: shapes (1,2) and (1,) not aligned: 2 (dim 1) != 1 (dim 0) (Please refer to the attached file - Adaline Stochastic) Prayerfully Tron Orino Yeong tcynotebook@yahoo.com 0916643858 ``` import numpy as np class AdalineSGD (object): def __init__(self, eta=0.01, n_iter=10, shuffle=True, random_state=None, batch=10): self.batch=100/batch self.eta=eta self.n_iter=n_iter self.w_initialized=False self.shuffle=shuffle self.random_state=random_state def fit (self,X, y): self._initialize_weights(X.shape[1]) self.cost_=[] for i in range(self.n_iter): if self.shuffle: X, y=self._shuffle(X,y) cost=[] mini_X=np.array_split(X,self.batch) mini_y=np.array_split(y,self.batch) for xi, target in zip (mini_X, mini_y): cost.append(self._update_weights(xi,target)) avg_cost=sum(cost)/len(y) self.cost_.append(avg_cost) return self def partial_fit(self, X, y): if not self.w_initialized: self._inintialize_weights(X.shape[1]) if y.ravel().shape[0]>1: for xi, target in zip (X, y): self._update_weights(X,y) else: self._update_weights(X,y) return self def _shuffle(self, X,y): r=self.rgen.permutation(len(y)) return X[r],y[r] def _initialize_weights(self, m): self.rgen=np.random.RandomState(self.random_state) self.w_=self.rgen.normal(loc=0.0,scale=0.01,size=1+m) self.w_initialized=True def _update_weights(self,xi,target): output = self.activation(self.net_input(xi)) error = (target - output) self.w_[1:]= self.eta*xi.dot(error) self.w_[0] = self.eta*error cost = 0.5 * error**2 return cost def net_input(self,X): return np.dot(X,self.w_[1:])+self.w_[0] def activation (self,X): return X def predict(self,X): return np.where(self.activation(self.net_input(X))>=0.0,1,-1) import pandas as pd df=pd.read_csv('https://archive.ics.uci.edu/ml/''machine-learning-databases/iris/iris.data',header=None) df.tail() import matplotlib.pyplot as plt y=df.iloc[0:100,4].values y = np.where(y=='Iris-setosa',-1,1) X=df.iloc[0:100,[0,2]].values X_std=np.copy(X) X_std[:,0]=(X[:,0]-X[:,0].mean())/X[:,0].std() X_std[:,1]=(X[:,1]-X[:,1].mean())/X[:,1].std() ada1=AdalineSGD(n_iter=15,eta=0.01, random_state=1, batch=1) ada2=AdalineSGD(n_iter=15,eta=0.01, random_state=1, batch=2) ada3=AdalineSGD(n_iter=15,eta=0.01, random_state=1, batch=10) ada1.fit(X_std,y) ada2.fit(X_std,y) ada3.fit(X_std,y) plt.plot(range(1,len(ada1.cost_)+1), ada1.cost_,marker='0',color='blue',label='batch=1') plt.plot(range(1,len(ada2.cost_)+1), ada2.cost_,marker='0',color='orange',label='batch=2') plt.plot(range(1,len(ada3.cost_)+1), ada3.cost_,marker='0',color='green',label='batch=10') plt.title('Mini-batch learnign') plt.legend(loc='upper right') plt.xlabel('Epochs') plt.ylabel('Avaerage Cost') plt.show() ```Please refer to the link - https://www.freecodecamp.org/forum/t/how...ent/265072 « Next Oldest | Next Newest »

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