def k_validation(data, rs, k, params):
errors = {}
for key in rs.iterkeys():
l = {}
es, ts = kfv.k_fold_cvalidation(data, k, rs[key], kfv.test_regression, params)
l["errors"] = [float(a) for a in es]
l["terrors"] = [float(a) for a in ts]
errors[key] = l
return errors
def vary_alpha(data, k, alphas, iterations):
errors = []
params = {"iterations": iterations}
for a in alphas:
print "On alpha " + str(a)
params["alpha"] = a
es, ts = kfv.k_fold_cvalidation(data, k, kfv.train_grad_descent, kfv.test_regression, params)
errors.append([a, kfv.mean_error(es), kfv.mean_error(ts)])
return errors
def vary_iterations(data, k, alpha, iterations):
errors = []
params = {"alpha": alpha}
for i in iterations:
print "On iteration count " + str(i)
params["iterations"] = i
es, ts = kfv.k_fold_cvalidation(data, k, kfv.train_grad_descent, kfv.test_regression, params)
errors.append([i, kfv.mean_error(es), kfv.mean_error(ts)])
return errors
def k_validation(data, rs, k, params):
errors = {}
for key in rs.iterkeys():
l = {}
es, ts = kfv.k_fold_cvalidation(data, k, rs[key], kfv.test_regression,
params)
l["errors"] = [float(a) for a in es]
l["terrors"] = [float(a) for a in ts]
errors[key] = l
return errors
def vary_iterations(data, k, alpha, iterations):
errors = []
params = {"alpha": alpha}
for i in iterations:
print "On iteration count " + str(i)
params["iterations"] = i
es, ts = kfv.k_fold_cvalidation(data, k, kfv.train_grad_descent,
kfv.test_regression, params)
errors.append([i, kfv.mean_error(es), kfv.mean_error(ts)])
return errors
def vary_alpha(data, k, alphas, iterations):
errors = []
params = {"iterations": iterations}
for a in alphas:
print "On alpha " + str(a)
params["alpha"] = a
es, ts = kfv.k_fold_cvalidation(data, k, kfv.train_grad_descent,
kfv.test_regression, params)
errors.append([a, kfv.mean_error(es), kfv.mean_error(ts)])
return errors
def vary_k(data, rs, min_k, max_k, inc, params):
errors = {}
for key in rs.iterkeys():
print "On regression: " + str(key)
l = []
for k in range(min_k, max_k + 1, inc):
print "On k value of " + str(k)
es, ts = kfv.k_fold_cvalidation(data, k, rs[key], kfv.test_regression, params)
l.append([k,kfv.mean_error(es), kfv.mean_error(ts)])
errors[key] = l
return errors
def vary_k(data, rs, min_k, max_k, inc, params):
errors = {}
for key in rs.iterkeys():
print "On regression: " + str(key)
l = []
for k in range(min_k, max_k + 1, inc):
print "On k value of " + str(k)
es, ts = kfv.k_fold_cvalidation(data, k, rs[key],
kfv.test_regression, params)
l.append([k, kfv.mean_error(es), kfv.mean_error(ts)])
errors[key] = l
return errors
