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// Generated by CoffeeScript 1.8.0
var Stats, bxd_only;
Stats = (function() {
function Stats(the_values) {
this.the_values = the_values;
}
Stats.prototype.add_value = function(value) {
return this.the_values.push(value);
};
Stats.prototype.n_of_samples = function() {
return this.the_values.length;
};
Stats.prototype.sum = function() {
var total, value, _i, _len, _ref;
total = 0;
_ref = this.the_values;
for (_i = 0, _len = _ref.length; _i < _len; _i++) {
value = _ref[_i];
total += value;
}
return total;
};
Stats.prototype.mean = function() {
return this.sum() / this.n_of_samples();
};
Stats.prototype.median = function() {
var is_odd, median_position, the_values_sorted;
is_odd = this.the_values.length % 2;
median_position = Math.floor(this.the_values.length / 2);
the_values_sorted = this.the_values.sort(function(a, b) {
return a - b;
});
if (is_odd) {
return the_values_sorted[median_position];
} else {
return (the_values_sorted[median_position] + the_values_sorted[median_position - 1]) / 2;
}
};
Stats.prototype.std_dev = function() {
var step_a, step_b, sum, value, _i, _len, _ref;
sum = 0;
_ref = this.the_values;
for (_i = 0, _len = _ref.length; _i < _len; _i++) {
value = _ref[_i];
step_a = Math.pow(value - this.mean(), 2);
sum += step_a;
}
step_b = sum / this.the_values.length;
return Math.sqrt(step_b);
};
Stats.prototype.std_error = function() {
return this.std_dev() / Math.sqrt(this.n_of_samples());
};
Stats.prototype.min = function() {
return Math.min.apply(Math, this.the_values);
};
Stats.prototype.max = function() {
return Math.max.apply(Math, this.the_values);
};
Stats.prototype.range = function() {
if (js_data.dataset_type == "ProbeSet"){
if (js_data.data_scale == "linear_positive"){
return Math.log2(this.max()) - Math.log2(this.min());
} else {
return this.max() - this.min()
}
} else {
return this.max() - this.min()
}
};
Stats.prototype.range_fold = function() {
if (js_data.dataset_type == "ProbeSet"){
return Math.pow(2, this.range());
} else {
return this.range()
}
};
Stats.prototype.interquartile = function() {
var iq, length, q1, q3;
length = this.the_values.length;
if (js_data.dataset_type == "ProbeSet" && js_data.data_scale == "linear_positive") {
q1 = Math.log2(this.the_values[Math.floor(length * .25)]);
q3 = Math.log2(this.the_values[Math.floor(length * .75)]);
} else {
q1 = this.the_values[Math.floor(length * .25)];
q3 = this.the_values[Math.floor(length * .75)];
}
iq = q3 - q1;
if (js_data.dataset_type == "ProbeSet") {
return Math.pow(2, iq);
} else {
return iq;
}
};
Stats.prototype.skewness = function() {
var len = this.the_values.length,
delta = 0,
delta_n = 0,
term1 = 0,
N = 0,
mean = 0,
M2 = 0,
M3 = 0,
g;
for ( var i = 0; i < len; i++ ) {
N += 1;
delta = this.the_values[ i ] - mean;
delta_n = delta / N;
term1 = delta * delta_n * (N-1);
M3 += term1*delta_n*(N-2) - 3*delta_n*M2;
M2 += term1;
mean += delta_n;
}
// Calculate the population skewness:
g = Math.sqrt( N )*M3 / Math.pow( M2, 3/2 );
// Return the corrected sample skewness:
return Math.sqrt( N*(N-1))*g / (N-2);
};
Stats.prototype.kurtosis = function() {
var len = this.the_values.length,
delta = 0,
delta_n = 0,
delta_n2 = 0,
term1 = 0,
N = 0,
mean = 0,
M2 = 0,
M3 = 0,
M4 = 0,
g;
for ( var i = 0; i < len; i++ ) {
N += 1;
delta = this.the_values[ i ] - mean;
delta_n = delta / N;
delta_n2 = delta_n * delta_n;
term1 = delta * delta_n * (N-1);
M4 += term1*delta_n2*(N*N - 3*N + 3) + 6*delta_n2*M2 - 4*delta_n*M3;
M3 += term1*delta_n*(N-2) - 3*delta_n*M2;
M2 += term1;
mean += delta_n;
}
// Calculate the population excess kurtosis:
g = N*M4 / (M2*M2) - 3;
//Return the corrected sample excess kurtosis:
return (N-1) / ( (N-2)*(N-3) ) * ( (N+1)*g + 6 );
};
return Stats;
})();
window.Stats = Stats;
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