from pipe import Pipe
from wrapper import knn
from pipetools import predict, dump, load_y, copy, evaluate, echo
from utils import load_x, load_y
from multipipetools import average
from splitter import cross
file = './datasets/iris/iris.data'
a = Pipe() \
.x(load_x(file)) \
.y(load_y(file))\
.split(5, cross()) \
.pipe(knn(1)) \
.pipe(copy('x_test', 'x')) \
.pipe(copy('y_test', 'y')) \
.pipe(predict()) \
.pipe(evaluate()) \
.merge('evaluation', average('evaluation'))\
.pipe(dump('evaluation'))
from pipe import Pipe
from wrapper import agglomerative
from pipetools import dump, evaluate
from utils import load_x
file = './datasets/iris/iris.data'
Pipe()\
.x(load_x(file, delimiter=','))\
.pipe(agglomerative(n_clusters=3))\
.pipe(dump('prediction'))
from pipe import Pipe
from wrapper import kernel_density_estimation
from pipetools import dump, evaluate, start_timer, stop_timer
from utils import load_x
# file = './datasets/pendigits/pendigits.tra'
file = './datasets/iris/iris.data'
Pipe() \
.x(load_x(file, delimiter=',')) \
.connect(start_timer()) \
.pipe(kernel_density_estimation(bandwidth=0.5)) \
.connect(stop_timer())\
.pipe(dump('pdf'))
