mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-21 23:21:59 +00:00
adam is default now
This commit is contained in:
Alexander Kozhikhov
parent
7c54bb0956
commit
52007c96d9
@ -45,11 +45,11 @@ namespace
|
||||
|
||||
/// Such default parameters were picked because they did good on some tests,
|
||||
/// though it still requires to fit parameters to achieve better result
|
||||
auto learning_rate = Float64(0.01);
|
||||
auto l2_reg_coef = Float64(0.1);
|
||||
auto learning_rate = Float64(1.0);
|
||||
auto l2_reg_coef = Float64(0.5);
|
||||
UInt64 batch_size = 15;
|
||||
|
||||
std::string weights_updater_name = "SGD";
|
||||
std::string weights_updater_name = "Adam";
|
||||
std::unique_ptr<IGradientComputer> gradient_computer;
|
||||
|
||||
if (!parameters.empty())
|
||||
@ -126,7 +126,7 @@ void LinearModelData::update_state()
|
||||
if (batch_size == 0)
|
||||
return;
|
||||
|
||||
weights_updater->update(batch_size, weights, bias, gradient_batch);
|
||||
weights_updater->update(batch_size, weights, bias, learning_rate, gradient_batch);
|
||||
batch_size = 0;
|
||||
++iter_num;
|
||||
gradient_batch.assign(gradient_batch.size(), Float64{0.0});
|
||||
@ -211,7 +211,7 @@ void LinearModelData::add(const IColumn ** columns, size_t row_num)
|
||||
|
||||
/// Here we have columns + 1 as first column corresponds to target value, and others - to features
|
||||
weights_updater->add_to_batch(
|
||||
gradient_batch, *gradient_computer, weights, bias, learning_rate, l2_reg_coef, target, columns + 1, row_num);
|
||||
gradient_batch, *gradient_computer, weights, bias, l2_reg_coef, target, columns + 1, row_num);
|
||||
|
||||
++batch_size;
|
||||
if (batch_size == batch_capacity)
|
||||
@ -256,7 +256,7 @@ void Adam::merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac)
|
||||
beta2_powered_ *= adam_rhs.beta2_powered_;
|
||||
}
|
||||
|
||||
void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
|
||||
void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
|
||||
{
|
||||
if (average_gradient.empty())
|
||||
{
|
||||
@ -267,7 +267,6 @@ void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & b
|
||||
average_squared_gradient.resize(batch_gradient.size(), Float64{0.0});
|
||||
}
|
||||
|
||||
/// batch_gradient already includes learning_rate - bad for squared gradient
|
||||
for (size_t i = 0; i != average_gradient.size(); ++i)
|
||||
{
|
||||
Float64 normed_gradient = batch_gradient[i] / batch_size;
|
||||
@ -278,10 +277,10 @@ void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & b
|
||||
|
||||
for (size_t i = 0; i < weights.size(); ++i)
|
||||
{
|
||||
weights[i] += average_gradient[i] /
|
||||
weights[i] += (learning_rate * average_gradient[i]) /
|
||||
((1 - beta1_powered_) * (sqrt(average_squared_gradient[i] / (1 - beta2_powered_)) + eps_));
|
||||
}
|
||||
bias += average_gradient[weights.size()] /
|
||||
bias += (learning_rate * average_gradient[weights.size()]) /
|
||||
((1 - beta1_powered_) * (sqrt(average_squared_gradient[weights.size()] / (1 - beta2_powered_)) + eps_));
|
||||
|
||||
beta1_powered_ *= beta1_;
|
||||
@ -293,7 +292,6 @@ void Adam::add_to_batch(
|
||||
IGradientComputer & gradient_computer,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
@ -304,7 +302,7 @@ void Adam::add_to_batch(
|
||||
average_gradient.resize(batch_gradient.size(), Float64{0.0});
|
||||
average_squared_gradient.resize(batch_gradient.size(), Float64{0.0});
|
||||
}
|
||||
gradient_computer.compute(batch_gradient, weights, bias, learning_rate, l2_reg_coef, target, columns, row_num);
|
||||
gradient_computer.compute(batch_gradient, weights, bias, l2_reg_coef, target, columns, row_num);
|
||||
}
|
||||
|
||||
void Nesterov::read(ReadBuffer & buf)
|
||||
@ -329,7 +327,7 @@ void Nesterov::merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac
|
||||
}
|
||||
}
|
||||
|
||||
void Nesterov::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
|
||||
void Nesterov::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
|
||||
{
|
||||
if (accumulated_gradient.empty())
|
||||
{
|
||||
@ -338,7 +336,7 @@ void Nesterov::update(UInt64 batch_size, std::vector<Float64> & weights, Float64
|
||||
|
||||
for (size_t i = 0; i < batch_gradient.size(); ++i)
|
||||
{
|
||||
accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + batch_gradient[i] / batch_size;
|
||||
accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + (learning_rate * batch_gradient[i]) / batch_size;
|
||||
}
|
||||
for (size_t i = 0; i < weights.size(); ++i)
|
||||
{
|
||||
@ -352,7 +350,6 @@ void Nesterov::add_to_batch(
|
||||
IGradientComputer & gradient_computer,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
@ -370,7 +367,7 @@ void Nesterov::add_to_batch(
|
||||
}
|
||||
auto shifted_bias = bias + accumulated_gradient[weights.size()] * alpha_;
|
||||
|
||||
gradient_computer.compute(batch_gradient, shifted_weights, shifted_bias, learning_rate, l2_reg_coef, target, columns, row_num);
|
||||
gradient_computer.compute(batch_gradient, shifted_weights, shifted_bias, l2_reg_coef, target, columns, row_num);
|
||||
}
|
||||
|
||||
void Momentum::read(ReadBuffer & buf)
|
||||
@ -392,7 +389,7 @@ void Momentum::merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac
|
||||
}
|
||||
}
|
||||
|
||||
void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
|
||||
void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
|
||||
{
|
||||
/// batch_size is already checked to be greater than 0
|
||||
if (accumulated_gradient.empty())
|
||||
@ -402,7 +399,7 @@ void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64
|
||||
|
||||
for (size_t i = 0; i < batch_gradient.size(); ++i)
|
||||
{
|
||||
accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + batch_gradient[i] / batch_size;
|
||||
accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + (learning_rate * batch_gradient[i]) / batch_size;
|
||||
}
|
||||
for (size_t i = 0; i < weights.size(); ++i)
|
||||
{
|
||||
@ -412,14 +409,14 @@ void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64
|
||||
}
|
||||
|
||||
void StochasticGradientDescent::update(
|
||||
UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
|
||||
UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
|
||||
{
|
||||
/// batch_size is already checked to be greater than 0
|
||||
for (size_t i = 0; i < weights.size(); ++i)
|
||||
{
|
||||
weights[i] += batch_gradient[i] / batch_size;
|
||||
weights[i] += (learning_rate * batch_gradient[i]) / batch_size;
|
||||
}
|
||||
bias += batch_gradient[weights.size()] / batch_size;
|
||||
bias += (learning_rate * batch_gradient[weights.size()]) / batch_size;
|
||||
}
|
||||
|
||||
void IWeightsUpdater::add_to_batch(
|
||||
@ -427,13 +424,12 @@ void IWeightsUpdater::add_to_batch(
|
||||
IGradientComputer & gradient_computer,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
size_t row_num)
|
||||
{
|
||||
gradient_computer.compute(batch_gradient, weights, bias, learning_rate, l2_reg_coef, target, columns, row_num);
|
||||
gradient_computer.compute(batch_gradient, weights, bias, l2_reg_coef, target, columns, row_num);
|
||||
}
|
||||
|
||||
/// Gradient computers
|
||||
@ -479,7 +475,6 @@ void LogisticRegression::compute(
|
||||
std::vector<Float64> & batch_gradient,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
@ -494,11 +489,11 @@ void LogisticRegression::compute(
|
||||
derivative *= target;
|
||||
derivative = exp(derivative);
|
||||
|
||||
batch_gradient[weights.size()] += learning_rate * target / (derivative + 1);
|
||||
batch_gradient[weights.size()] += target / (derivative + 1);
|
||||
for (size_t i = 0; i < weights.size(); ++i)
|
||||
{
|
||||
auto value = (*columns[i]).getFloat64(row_num);
|
||||
batch_gradient[i] += learning_rate * target * value / (derivative + 1) - 2 * learning_rate * l2_reg_coef * weights[i];
|
||||
batch_gradient[i] += target * value / (derivative + 1) - 2 * l2_reg_coef * weights[i];
|
||||
}
|
||||
}
|
||||
|
||||
@ -551,7 +546,6 @@ void LinearRegression::compute(
|
||||
std::vector<Float64> & batch_gradient,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
@ -563,13 +557,13 @@ void LinearRegression::compute(
|
||||
auto value = (*columns[i]).getFloat64(row_num);
|
||||
derivative -= weights[i] * value;
|
||||
}
|
||||
derivative *= (2 * learning_rate);
|
||||
derivative *= 2;
|
||||
|
||||
batch_gradient[weights.size()] += derivative;
|
||||
for (size_t i = 0; i < weights.size(); ++i)
|
||||
{
|
||||
auto value = (*columns[i]).getFloat64(row_num);
|
||||
batch_gradient[i] += derivative * value - 2 * learning_rate * l2_reg_coef * weights[i];
|
||||
batch_gradient[i] += derivative * value - 2 * l2_reg_coef * weights[i];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@ -33,7 +33,6 @@ public:
|
||||
std::vector<Float64> & batch_gradient,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
@ -60,7 +59,6 @@ public:
|
||||
std::vector<Float64> & batch_gradient,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
@ -87,7 +85,6 @@ public:
|
||||
std::vector<Float64> & batch_gradient,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
@ -120,14 +117,18 @@ public:
|
||||
IGradientComputer & gradient_computer,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
size_t row_num);
|
||||
|
||||
/// Updates current weights according to the gradient from the last mini-batch
|
||||
virtual void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & gradient) = 0;
|
||||
virtual void update(
|
||||
UInt64 batch_size,
|
||||
std::vector<Float64> & weights,
|
||||
Float64 & bias,
|
||||
Float64 learning_rate,
|
||||
const std::vector<Float64> & gradient) = 0;
|
||||
|
||||
/// Used during the merge of two states
|
||||
virtual void merge(const IWeightsUpdater &, Float64, Float64) {}
|
||||
@ -143,7 +144,7 @@ public:
|
||||
class StochasticGradientDescent : public IWeightsUpdater
|
||||
{
|
||||
public:
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
|
||||
};
|
||||
|
||||
|
||||
@ -154,7 +155,7 @@ public:
|
||||
|
||||
Momentum(Float64 alpha) : alpha_(alpha) {}
|
||||
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
|
||||
|
||||
virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override;
|
||||
|
||||
@ -180,13 +181,12 @@ public:
|
||||
IGradientComputer & gradient_computer,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
size_t row_num) override;
|
||||
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
|
||||
|
||||
virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override;
|
||||
|
||||
@ -195,7 +195,7 @@ public:
|
||||
void read(ReadBuffer & buf) override;
|
||||
|
||||
private:
|
||||
Float64 alpha_{0.1};
|
||||
const Float64 alpha_ = 0.9;
|
||||
std::vector<Float64> accumulated_gradient;
|
||||
};
|
||||
|
||||
@ -214,13 +214,12 @@ public:
|
||||
IGradientComputer & gradient_computer,
|
||||
const std::vector<Float64> & weights,
|
||||
Float64 bias,
|
||||
Float64 learning_rate,
|
||||
Float64 l2_reg_coef,
|
||||
Float64 target,
|
||||
const IColumn ** columns,
|
||||
size_t row_num) override;
|
||||
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
|
||||
void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
|
||||
|
||||
virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override;
|
||||
|
||||
|
||||
@ -16,7 +16,7 @@ select ans < -61.374 and ans > -61.375 from
|
||||
(with (select state from remote('127.0.0.1', currentDatabase(), model)) as model select evalMLMethod(model, predict1, predict2) as ans from remote('127.0.0.1', currentDatabase(), defaults));
|
||||
|
||||
SELECT 0 < ans[1] and ans[1] < 0.15 and 0.95 < ans[2] and ans[2] < 1.0 and 0 < ans[3] and ans[3] < 0.05 FROM
|
||||
(SELECT stochasticLinearRegression(0.000001, 0.01, 100)(number, rand() % 100, number) AS ans FROM numbers(1000));
|
||||
(SELECT stochasticLinearRegression(0.000001, 0.01, 100, 'SGD')(number, rand() % 100, number) AS ans FROM numbers(1000));
|
||||
|
||||
DROP TABLE model;
|
||||
DROP TABLE defaults;
|
||||
|
||||
@ -11,43 +11,43 @@
|
||||
0.6542885368159769
|
||||
0.6542885368159769
|
||||
0.6542885368159769
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.8444267125384497
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.9683751248474649
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.7836319925339996
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.8444267125384498
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.968375124847465
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.7836319925339997
|
||||
0.6375535053362572
|
||||
0.6375535053362572
|
||||
0.6375535053362572
|
||||
@ -60,18 +60,18 @@
|
||||
0.6375535053362572
|
||||
0.6375535053362572
|
||||
0.6375535053362572
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307677
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5871709781307678
|
||||
0.5202091999924413
|
||||
0.5202091999924413
|
||||
0.5202091999924413
|
||||
@ -97,18 +97,18 @@
|
||||
0.5130525169352177
|
||||
0.5130525169352177
|
||||
0.5130525169352177
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249047
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.5581075117249046
|
||||
0.6798311701936688
|
||||
0.6798311701936688
|
||||
0.6798311701936688
|
||||
@ -146,43 +146,43 @@
|
||||
0.7773509726916165
|
||||
0.7773509726916165
|
||||
0.7773509726916165
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.8606987912604607
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.6352934050115681
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.9771089703353684
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.8606987912604608
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.635293405011568
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9771089703353683
|
||||
0.9955717835823895
|
||||
0.9955717835823895
|
||||
0.9955717835823895
|
||||
@ -196,18 +196,18 @@
|
||||
0.9955717835823895
|
||||
0.9955717835823895
|
||||
0.9955717835823895
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938347
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6124539775938348
|
||||
0.6564358792397615
|
||||
0.6564358792397615
|
||||
0.6564358792397615
|
||||
@ -220,19 +220,19 @@
|
||||
0.6564358792397615
|
||||
0.6564358792397615
|
||||
0.6564358792397615
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.552111558999158
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.5521115589991579
|
||||
0.7792659923782862
|
||||
0.7792659923782862
|
||||
0.7792659923782862
|
||||
@ -257,31 +257,31 @@
|
||||
0.6656871036437929
|
||||
0.6656871036437929
|
||||
0.6656871036437929
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.7435137743371989
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.8688023472919777
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.743513774337199
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.8688023472919778
|
||||
0.7225690042828818
|
||||
0.7225690042828818
|
||||
0.7225690042828818
|
||||
@ -307,18 +307,18 @@
|
||||
0.8866100282141612
|
||||
0.8866100282141612
|
||||
0.8866100282141612
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184257
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8374461350184258
|
||||
0.8365104788783658
|
||||
0.8365104788783658
|
||||
0.8365104788783658
|
||||
@ -344,18 +344,18 @@
|
||||
0.928892180915439
|
||||
0.928892180915439
|
||||
0.928892180915439
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899534
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.7275019293899533
|
||||
0.9516437185963472
|
||||
0.9516437185963472
|
||||
0.9516437185963472
|
||||
|
||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user