This commit is contained in:
ltcptgeneral 2020-01-05 19:08:06 -06:00
commit 478c793917
3 changed files with 32 additions and 20 deletions

2
.gitignore vendored
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@ -12,3 +12,5 @@ data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.cp37-win_amd64.
data analysis/analysis/build/temp.win-amd64-3.7/Release/analysis.obj
data analysis/test.ipynb
data analysis/.ipynb_checkpoints/test-checkpoint.ipynb
.vscode/settings.json
.vscode

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@ -7,10 +7,17 @@
# current benchmark of optimization: 1.33 times faster
# setup:
__version__ = "1.1.11.004"
__version__ = "1.1.11.007"
# changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
1.1.11.007:
- bug fixes
1.1.11.006:
- tested min and max
- bug fixes
1.1.11.005:
- added min and max in basic_stats
1.1.11.004:
- bug fixes
1.1.11.003:
@ -269,8 +276,8 @@ def basic_stats(data):
_median = median(data_t)
_stdev = stdev(data_t)
_variance = variance(data_t)
_min = min(data_t)
_max = max(data_t)
_min = npmin(data_t)
_max = npmax(data_t)
return _mean, _median, _stdev, _variance, _min, _max
@ -383,9 +390,9 @@ def trueskill(teams_data, observations):#teams_data is array of array of tuples
class RegressionMetrics():
def __new__(self, predictions, targets):
def __new__(cls, predictions, targets):
return self.r_squared(self, predictions, targets), self.mse(self, predictions, targets), self.rms(self, predictions, targets)
return cls.r_squared(cls, predictions, targets), cls.mse(cls, predictions, targets), cls.rms(cls, predictions, targets)
def r_squared(self, predictions, targets): # assumes equal size inputs
@ -401,9 +408,9 @@ class RegressionMetrics():
class ClassificationMetrics():
def __new__(self, predictions, targets):
def __new__(cls, predictions, targets):
return self.cm(self, predictions, targets), self.cr(self, predictions, targets)
return cls.cm(cls, predictions, targets), cls.cr(cls, predictions, targets)
def cm(self, predictions, targets):
@ -434,14 +441,14 @@ def variance(data):
return np.var(data)
@jit(nopython=True)
def min(data):
def npmin(data):
return data.min
return np.amin(data)
@jit(nopython=True)
def max(data):
def npmax(data):
return data.max
return np.amax(data)
@jit(forceobj=True)
def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):
@ -532,13 +539,13 @@ class SVM:
class CustomKernel:
def __new__(self, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
def __new__(cls, C, kernel, degre, gamma, coef0, shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape, random_state):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
class StandardKernel:
def __new__(self, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
def __new__(cls, kernel, C=1.0, degree=3, gamma='auto_deprecated', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None):
return sklearn.svm.SVC(C = C, kernel = kernel, gamma = gamma, coef0 = coef0, shrinking = shrinking, probability = probability, tol = tol, cache_size = cache_size, class_weight = class_weight, verbose = verbose, max_iter = max_iter, decision_function_shape = decision_function_shape, random_state = random_state)
@ -546,25 +553,25 @@ class SVM:
class Linear:
def __new__(self):
def __new__(cls):
return sklearn.svm.SVC(kernel = 'linear')
class Polynomial:
def __new__(self, power, r_bias):
def __new__(cls, power, r_bias):
return sklearn.svm.SVC(kernel = 'polynomial', degree = power, coef0 = r_bias)
class RBF:
def __new__(self, gamma):
def __new__(cls, gamma):
return sklearn.svm.SVC(kernel = 'rbf', gamma = gamma)
class Sigmoid:
def __new__(self, r_bias):
def __new__(cls, r_bias):
return sklearn.svm.SVC(kernel = 'sigmoid', coef0 = r_bias)
@ -611,10 +618,12 @@ class Regression:
# this module is cuda-optimized and vectorized (except for one small part)
# setup:
__version__ = "1.0.0.002"
__version__ = "1.0.0.003"
# changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """
1.0.0.003:
- bug fixes
1.0.0.002:
-Added more parameters to log, exponential, polynomial
-Added SigmoidalRegKernelArthur, because Arthur apparently needs
@ -645,12 +654,13 @@ class Regression:
'CustomTrain'
]
global device
device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
#todo: document completely
def set_device(self, new_device):
global device
device=new_device
class LinearRegKernel():
@ -769,7 +779,7 @@ class Regression:
long_bias=self.bias.repeat([1,mtx.size()[1]])
return torch.matmul(self.weights,new_mtx)+long_bias
def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):
optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
data_cuda=data.to(device)
ground_cuda=ground.to(device)