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analysis.py v 1.1.5.001

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art 2019-10-25 09:50:02 -05:00
parent f70e5f573e
commit 886735d9c8
2 changed files with 17 additions and 6 deletions
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data analysis/analysis/analysis.py
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@ -11,6 +11,9 @@ __version__ = "1.1.5.001"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.5.002:
- reduced import list
- added kmeans clustering engine
1.1.5.001: 1.1.5.001:
- simplified regression by using .to(device) - simplified regression by using .to(device)
1.1.5.000: 1.1.5.000:
@ -194,8 +197,8 @@ try:
from analysis import trueskill as Trueskill from analysis import trueskill as Trueskill
except: except:
import trueskill as Trueskill import trueskill as Trueskill
from sklearn import metrics import sklearn
from sklearn import preprocessing from sklearn import *
import torch import torch
class error(ValueError): class error(ValueError):
@ -238,7 +241,7 @@ def z_normalize(array, *args):
array = np.array(array) array = np.array(array)
for arg in args: for arg in args:
array = preprocessing.normalize(array, axis = arg) array = sklearnpreprocessing.normalize(array, axis = arg)
return array return array
@ -335,17 +338,17 @@ def trueskill(teams_data, observations):#teams_data is array of array of tuples
@jit(forceobj=True) @jit(forceobj=True)
def r_squared(predictions, targets): # assumes equal size inputs def r_squared(predictions, targets): # assumes equal size inputs
return metrics.r2_score(np.array(targets), np.array(predictions)) return sklearn.metrics.r2_score(np.array(targets), np.array(predictions))
@jit(forceobj=True) @jit(forceobj=True)
def mse(predictions, targets): def mse(predictions, targets):
return metrics.mean_squared_error(np.array(targets), np.array(predictions)) return sklearn.metrics.mean_squared_error(np.array(targets), np.array(predictions))
@jit(forceobj=True) @jit(forceobj=True)
def rms(predictions, targets): def rms(predictions, targets):
return math.sqrt(metrics.mean_squared_error(np.array(targets), np.array(predictions))) return math.sqrt(sklearn.metrics.mean_squared_error(np.array(targets), np.array(predictions)))
@jit(nopython=True) @jit(nopython=True)
def mean(data): def mean(data):
@ -367,6 +370,14 @@ def variance(data):
return np.var(data) return np.var(data)
def kmeans(data, kernel=sklearn.cluster.KMeans()):
kernel.fit(data)
predictions = kernel.predict(data)
centers = kernel.cluster_centers_
return centers, predictions
class Regression: class Regression:
# Titan Robotics Team 2022: CUDA-based Regressions Module # Titan Robotics Team 2022: CUDA-based Regressions Module