_cost = [] def load_input_data(self, data_file): with open(data_file) as f: input_x = [] input_y = [] for line in f: [x0, x1, y] = line.split() input_x.append([float(x0), float(x1)]) input_y.append(float(y)) self._input_x = np.array(input_x) self._input_...
from sklearn.linear_model import LinearRegression, Ridge, Lasso from sklearn.preprocessing import PolynomialFeatures from sklearn.base import BaseEstimator, TransformerMixin from sklearn.pipeline import make_pipeline sns.set() plt.rc('font', family='SimHei') plt.rc('axes', unicode_minus=False) 1....
defload_exdata(filename):data=[]withopen(filename,'r')asf:forlineinf.readlines():line=line.split(',')current=[int(item)foriteminline]#5.5277,9.1302data.append(current)returndata data=load_exdata('ex1data2.txt');data=np.array(data,np.int64)x=data[:,(0,1)].reshape((-1,2))y=dat...
(x_train)将X_train转换为Tensor#model()根据输入和模型,得到输出#detach().numpy()预测结结果转换为numpy数组predicted=model(torch.from_numpy(x_train)).detach().numpy()plt.plot(x_train,y_train,‘ro’,label=‘Original data’)plt.plot(x_train,predicted,label=‘Fitted line’)plt.legend()plt....
linereg01.fit(X_train,y_train) y_predict_in_train= linereg01.predict(X_train) y_predict_in_test = linereg01.predict(X_test) w = linereg01.coef_ #得到权重列表 b = linereg01.intercept_ #得到bias值 print(len(w)) # 输出参数数目 ...
'line’ : line plot (default)#折线图 ‘bar’ : vertical bar plot#条形图。 ‘hist’ : histogram#直方图 ‘box’ : boxplot#箱型图 ‘kde’ : Kernel Density Estimation plot#密度图 ‘hexbin’ : hexbin plot#蜂巢图。需指定X轴Y轴
prob = sigmoid(sum(inX*weights)) ifprob > 0.5: return1.0 else: return0.0 def colicTest(filetrain,filetest): frTrain = open(filetrain) frTest = open(filetest) trainingSet = [] trainingLabeles = [] forlineinfrTrain.readlines(): ...
fileIn = open('E:/Python/Machine Learning in Action/testSet.txt') for linein fileIn.readlines(): lineArr = line.strip().split() train_x.append([1.0, float(lineArr[0]), float(lineArr[1])]) train_y.append(float(lineArr[2])) ...
In Machine Learning, predicting the future is very important. How Does it Work? Python has methods for finding a relationship between data-points and to draw a line of linear regression. We will show you how to use these methods instead of going through the mathematic formula. ...
line ˜ i = l i n e i - line ^ i + line ¯ This is not necessary for debiasing, but it puts line ˜ in the same range as the original l i n e , which is better for visualization purposes: In [10]: debiasing_model = smf.ols( 'credit_limit ~ wage + credit_score1 ...