We can apply the MinMaxScaler to the Sonar dataset directly to normalize the input variables. We will use the default configuration and scale values to the range 0 and 1. First, a MinMaxScaler instance is defined with default hyperparameters. Once defined, we can call the fit_transform() func...
This means you can use the normalized data to train your model. This is done by calling the transform() function. Apply the scale to data going forward. This means you can prepare new data in the future on which you want to make predictions. The default scale for the Min...
apply(lambda x: x.count(" ")).max() vocab_size_tit = ad_title_table.wordId.nunique() from sklearn.preprocessing import LabelEncoder, MinMaxScaler from sklearn.model_selection import train_test_split from deepctr.models import DeepFM, FiBiNET from deepctr.inputs import SparseFeat, DenseFeat,...
random_normal(loc=0, scale=1, shape=(self.batch_size, self.n_latent), ctx=model_ctx) z = mu + F.exp(0.5*lv)*eps y = self.decoder(z) self.output = y KL = 0.5*F.sum(1+lv-mu*mu-F.exp(lv),axis=1) logloss = F.sum(x*F.log(y+self.soft_zero)+ (1-x)*F.log(1-y...
assertlen(row)==len(x[0])-len(unwanted_features) returnans if__name__=='__main__': # pdb.set_trace() neg_file="cc_data/black/black_all.txt" pos_file="cc_data/white/white_all.txt" X=[] y=[] ifos.path.isfile(pos_file): ...
scaler = preprocessing.StandardScaler().fit(X) #scaler = preprocessing.MinMaxScaler().fit(X) X = scaler.transform(X) print("standard X sample:", X[:3]) black_verify = scaler.transform(black_verify) print(black_verify) white_verify = scaler.transform(white_verify) ...
preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(...
random_normal(loc=0, scale=1, shape=(self.batch_size, self.n_latent), ctx=model_ctx) z = mu + F.exp(0.5*lv)*eps y = self.decoder(z) self.output = y KL = 0.5*F.sum(1+lv-mu*mu-F.exp(lv),axis=1) logloss = F.sum(x*F.log(y+self.soft_zero)+ (1-x)*F.log(1-y...
preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(...
preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(...